WP Common Indicators

Transcription

1 WP3.2.3 Common Indicators Jason N. Meggs Joerg Schweizer UNIBO-DICAM BICY Project, Central Europe, ERDF January 30, 2012

2 Page 2/152 Common Indicators Report BICY PROJECT TASK FINAL IDENTIFICATION OF COMMON INDICATORS FINAL REVISION: 30 JANUARY, 2012; BY JASON N. MEGGS & JOERG SCHWEIZER Abstract This report summarises the content and analysis of, and cross-validation between, three sources of primary data for WP3.2.3: official indicator data, collected from all partners, and used in developing Common Indicators of cycling; detailed mobility survey results; and OpenStreetMap geospatial analysis. Ultimately the goal of this report is to establish & test common performance indicators (including cost-benefit analyses) to quantify the environmental & socioeconomic impact of improved bike circulation. Cover photo: Provided by Graz partner FGM-AMOR.

3 Page 3/152 Table of Contents Abstract...2 Table of Contents...3 OVERVIEW...7 Key Findings...8 Models for Predicting Increases in Cycling...8 How cyclists experience of their environment affects cycling levels...9 Cost-Benefit Estimates...9 Methodology Study Areas INDICATOR DATA Indicators Strategy MOBILITY SURVEY Survey Strategy MODAL SPLIT Bicycle Modal Split Comparing Modal Split Data Exclusions COMMON INDICATORS Development of Common Indicators Common Indicators Hypothesis Standard indicators BICY Indicator Calculation of Common Indicators Standard Indicators Assessment of Standard Indicators Correlations between Indices and Bicycling Indicators PROJECTIONS Survey Scenarios Scenario Scenario Scenario

4 Page 4/152 Public Transport Scenarios Projected Carbon Reductions Scenario 1 Carbon Reductions Scenario 2 Carbon Reductions Scenario3 Carbon Reductions Average Carbon Reductions Indicator Calculations Cycling Index Predictive Power Target Cycling Levels Cost of Target Cycling Levels COST-BENEFIT ANALYSES Health Economic Assessment Tool (HEAT) HEAT Calculation assumptions: HEAT Cost-Benefit Calculation: 15% Cycling by Value of Carbon Reductions CONCLUSIONS References ANNEXES A. BICY Areas and Places Diversity of Types of Place B. Indicator Data Inventory The City and its People Infrastructure Measurements: Injuries and Fatalities Cost of New Bicycle Facilities (Lanes, Paths, and Parking) Supplemental data Additional data requested: Completeness of data from project partners: Additional Data Inquiries for Consideration: Additional Sources of Modal Share C. OpenStreetMap OpenStreetMap Framework OSM Methodology... 67

5 Page 5/152 Development Environment Pieces of the Puzzle Data Challenges Overview of Steps Example Subset of Highways Data OSM Analysis Comparing OSM Results D. Survey Data Inventory Participant Demographics Modal Splits Cyclists Routine Experience Conditions for Adopting Cycling New Bicycle Use Scenarios New Cyclists Carbon Reduction Estimates Conditions for Adopting Public Transport Use New Public Transport Use Scenarios Additional Survey Results E. Survey Methodology Standardized Approach Locations Processing Method Methodology Manual F. Survey Error/ Bias Correction Shares by Group Survey Adjustment Weights Relative Weights by Group: Under- and Over-Representation Exclusions and Adjustments Additional Survey Issues of Note G. Modal Split Considerations Comparison of Modal Splits (Time, Duration) and Regular Cyclists Modal Split of All modes Multi-Modal Modal Splits Bike Mode Share data barriers:

6 Page 6/152 SURVEY ANALYSIS ANNEXES H. Cyclists Experiences Types of Cyclists Gender and Cycling East Meets West Young and old Factors Affecting Cycling Effects of Topography Effects of Weather Driver Behavior Safety and Fear Culture of Driving Continuity of Bicycle Routes Bicycle Parking (Quality of Destinations) I. Standard Indicators Analysis MEMO: A review of Standard Indicators using new data sources: A brief summary table of the R2 values found for the various analyses: J. Common Indicators Additional Analysis and Steps K. Exploration of Additional Indicators Bicycle use indicators Experimental Additional Indicators BICY Indices BICY Index BICY Index BICY Index Comparing Standard Indicators Statistical Modeling of Common Indicators L. Notes on Terminology

7 Page 7/152 OVERVIEW The goal of the Common Indicators effort is to obtain data relevant to cycling, and then to use that data to develop combined metrics both for assessing current cycling, and for predicting future cycling. The two primary components are indicator data, and survey data. These two components are complementary; some data can only be acquired by one of the two types of source. For example, population data cannot be found by a street survey; likewise, public opinion as to the quality of the transport system, and what people would require to change their own behavior, cannot be found in official references. However, population data assists in correcting survey data, and the survey data assists in filling gaps in official data, most notably, to determine the amount of bicycling in the partner places. In this way the two sources are essential for crossvalidation. Similarly, geographic (GIS) data provided by OpenStreetMap (OSM) affords a source of new data as well as a means of cross-validating indicator data; and a means of conducting spatial analysis. Thus there are a number of components to this inquiry and analysis: Indicator Data official descriptive information Survey Data information directly from the people OpenStreetMap (OSM) - geographic data and spatial analysis Common Indicators - calculated from foregoing data and analyses Projections - based on Common Indicators This is the most arduous portion of the WP3 effort, and absolutely essential to the Transnational Strategy. From the three sources of data (Indicator, Survey, and OSM), patterns are sought, and models developed, principally in the form of Common Indicators, with the goal of determining what the effect of investments in new cycling will be in the Projections. The types of expected results considered here include: Increased cycling rates Reduced carbon emissions Economic benefits These projections are developed first here, but are principally the work of the forthcoming Transnational Strategy Report (TSR), which builds on this report along with all reports from this Work Package (WP): SWOT Report; Stakeholder Interviews; State of the Art (best practices); and other sources, including individual Bicycle Policies. The TSR is intended to be a synthesis of these many efforts; final conclusions must be deferred to its release.

8 Page 8/152 Key Findings At this advanced stage in the Common Indicators effort, a diversity of meaningful results is arriving. While this will continue to build in depth and complexity, through iterations of contributions from partners in the development of the Transnational Strategy, key findings highlighted at this time include: Models showing the power of investments to increase cycling Insights into how the cycling environment, and individual cyclists experiences, affect cycling levels Cost-Benefit calculations based both on models and survey predictions Models for Predicting Increases in Cycling The key predictive indicator developed at this time has been the Cycling Index, which measures bikeways per capita (in km per person). When correlated to the percent bicycling in the survey-generated modal share, a strong linear relationship is observed: Figure 1: Cycling Index shown as it relates to Bicycle Mode Share (% cycling). In addition, the public s stated cycling infrastructure needs allow the assessment of scenarios. Based on survey preferences, these scenarios promise to provide results such as: Increases of 500% more cycling in eastern Partner Places Carbon reductions of up to 13.7%

9 Page 9/152 How cyclists experience of their environment affects cycling levels Other findings, detailed in this report and particularly in its Annexes, include a number of other promising indicators, as well as observations and insights into the relationship between cycling levels, cycling conditions, and personal experience; factors which affect the amount of cycling, such as: Availability of Secure Bicycle Parking and fear of bicycle theft Availability of bikeways and traffic limited streets on one s route Fear of an Accident having been in one and whether drivers respect cyclists Hills and other tiring grades as well as cold and rain These observations are found not only for regular workday trips, the standard measure, but also for those who cycle less frequently; and, perhaps most importantly, insights are gained into what it would take to get those who do not cycle, to do so regularly (see Annex H). Cost-Benefit Estimates Long elusive in the world of cycling, a variety of cost-benefit estimates are now possible. The combination of the BICY Project s achievements with latest scientific research has allowed Economic Benefit calculations based on the investment cost of implementing various cycling improvement scenarios. Conservative results include: Cost-Benefit Ratios of up to 12:1 for eastern partner places, saving lives and millions of euros per year, by investing in bikeways. Substantial carbon reductions Many more quantifications of the benefits of investments in cycling will be demonstrated in the final Transnational Strategy Report. Indeed, these are key to creating robust and effective policy actions.

10 Page 10/152 Methodology The indicator analysis will be based on three main sources of information: The data-collection: these are basic quantitative data of the infrastructure, population and transport collected from each of the partners. The mobility survey: each of the partners will conduct a unified mobility survey. The mobility survey will provide information on the travel behavior of the citizens, including their perception of the present transport offer and their propensity to change to more sustainable modes of transport such as the bike. The OpenStreetMap analysis: this is an analysis method developed by DICAM specifically for the BICY project to overcome problems with inconsistent or incomplete physical data. Basically, this analysis reads the GEO-referenced highways network of all partner cities from the OpenStreetMap server, including different kinds of cycling facilities. The unified highways attributes of OpenStreetMap allow a comparative analysis with the data collection of the currently implemented cycling infrastructure, received from partners. A geographical, network-oriented analysis in development allows a much deeper insight to the functionality of cycling infrastructure. Measures like connectivity, and the temporal competitiveness of the cycling network with respect to the road network, can be determined. Figure 2: The basic methodological flow-chart relationship of the indicator activities. A sub-task of is the calculation of costs and benefits of an increase in cycling, taking into account the so called external costs (e.g., costs for pollution, health, and fatalities). An inclusion of all external costs would be beyond the possibilities of this project due to lack of comparable data. In particular, the reduction of medical costs caused by a healthier life style due to frequent cycling is extremely difficult to estimate, even though cost models have been reported in literature. (Woodcock, 2009) However, there exist tools which can predict some health and economic benefits, most notably the Health Economic Analysis Tool (HEAT) developed by the European World Health Organization. (WHO 2012)

11 Page 11/152 Therefore, the scope of the cost-benefit analysis for BICY will focus on costs that are known or that can be estimated from reliable and established methods such as certain health benefits, accidents 1 and fatalities. Transport related energy, CO2 emissions could be estimated indirectly from average national vehicle fleet composition and km car-travel from the survey. Costs caused by accidents and emissions represent a significant share of external costs of road transport. A further motivation to focus on accidents and emissions is that their dependency on current mobility can be modeled numerically. This means the reduction of accidents, energy-consumption and emissions due to an increase in cycling could be estimated. For this reason the data collection does gather all data that allows us to parameterize external cost models. This fits well with the survey data, which provides the exact use of different modes on a typical work day. With basic assumptions on energy use per person-km by mode, it should also be possible to reconstruct energy, emissions and their related costs. However, the quality of such estimations depends greatly on the amount and quality of data that is returned by the participants. It was anticipated that not all partners will have all data required for such estimates, and thus alternatives or indirect estimates have needed to be considered where possible. Because of the many pieces of this report and the iterative nature over time of reviewing the work with stakeholders and partners, the groundwork for future analysis laid here will be more fully and reliably developed in the final Transnational Strategy Report (WP3.4). Study Areas The BICY Project study area includes sixteen (16) Partner Areas, spanning seven of the eight countries within the Central Europe Programme (all but Poland). These Partner Areas include one region and two provinces, larger areas which contain Partner Places. These Partner Places are comprised of thirteen (13) cities and towns, and are the primary places of study, for which indicator, survey and geospatial data have been obtained. Although larger areas include provinces and regions, because urban cycling is the focus, most of BICY Project s numerical indicator-based analyses utilise only the data from the level of cities and towns. Qualitiative assesments and some numerical analyses may also include the levels of region and province. A comparison of the array of sizes, by population, of both Partner Areas and Partner Places, is below. Included are an inset graph showing the diversity of sizes (Region/Province, City (population > 100,000), and Towns. More detail about the Partner Areas and Places can be found in the Annexes. 1 Please see discussion of the term accident in the Annex, Notes on Terminology.

12 Page 12/152 Figure 3: places studied in BICY project, sorted by population size. Larger areas, and their sub-areas, are both included. Inset, a graph showing the quantity of each type of place or area. INDICATOR DATA Project Partners were asked to locate and provide key official indicator data using a standardized form developed by DICAM. The template form is available on the BICY website for review, and in the Annex to this report; its contents and results are described in this section, and its analysis further elaborated throughout the report and in the subsequent cross-analysis, the Transnational Strategy Report (WP3.4). The year 2008 was the target year for data, with alternative sources used when necessary, and adjustments made if needed and possible. Where partners were unable to supply data, or where problems and inconsistencies existed, the DICAM Team attempted to correct data through additional research or calculations. Details regarding the indicator data can be found in the Annex, Indicator Data Inventory. Indicators Strategy The field of bicycle research in the present day is very new, and rapidly evolving. Study has primarily focused on cities in the developed world where the highest rates of bicycling have occurred. Rarely are other places investigated, and the imperative social, political and economic question of how to increase bicycling remains largely untouched. Unique to the BICY project is an exploration across a diversity of places, from large to small, including countries where no bicycle data has ever been officially collected. Even the most studied countries have faced serious challenges developing useful data on bicycling. Thus this project is ground breaking, and

13 Page 13/152 incurs concomitant, unique challenges. For example, in some countries emerging from the former USSR, even the most basic details of population data were said to be not available today. To best fulfill the project's goal of understanding factors influencing the quality and quantity of bicycling across Central Europe, quantitative indicator data has been sought under WP In this way we attempt to explain the great difference in cycling mobility rates achieved by various European cities. The quantitative data gathered here allows formal scientific and mathematical analysis, complementary to the qualitative data from other efforts such as WP3.1 (SWOT Analysis) and WP3.2. Data of all kinds, representative of urban areas, human behavior, and transportation, can inform our overall analysis and may be sought to help explain differences found between areas. It is further the bases for the transnational strategy building and cycling policy formulation. A related project of indicator collection which can be useful in the BICY process, although the data is older, having been primarily benchmarked from the year 2000 and released in 2006, is the source of data, is the Citizen s Network Benchmarking Initiative [EC, 2006], which sought to collect urban transport indicators, including those for cycling. MOBILITY SURVEY A detailed mobility survey was designed by UNIBO-DICAM, translated into six languages, and conducted by each partner place according to a standardized, unified methodology. Survey Strategy The survey was designed to complement, cross-validate, and build upon the Indicator Data (described above), for use in transnational analyses. The survey affords an important snapshot in time of both the travel behaviour and individuals experiences in each Partner Place, and also provides prospective data as to how people s choices would change, given new options. Respondents were asked how their travel choices would change given a variety of different conditions such as improved bikeways, and improved public transport options. From this data, along with the indicator data, the potential success of interventions is evaluated, including carbon reductions and health-related cost-benefit analyses. Details regarding the survey, the data obtained, and its methodology, can be found in the Annexes. MODAL SPLIT The modal split is an essential piece of transport analysis: the proportion of travel done by each mode (e.g., percentage driving, using public transport, cycling, and walking). There are many different ways to find and represent modal split. Official modal splits for each Partner Place were first sought in the Indicator Data. The BICY Project also contributed a new, standardized modal split, calculated from the survey responses.

14 Page 14/152 For purposes of the BICY Project, the bicycle mode share (% bicycling) 2 is of utmost importance, as increasing bicycling is the Project s core goal, and is therefore is our first and foremost indicator. Because of its special nature, being generated both from the survey and official indicator efforts, as well as third party sources, its development is detailed here in its own section. The modal split then becomes a principal component of many Common Indicators, and thus a foundation of the Projections and subsequent TSR. Details of the other modal splits, their implications and analyses, including further information on the bicycle mode share, are in the Annexes. The total modal split found for all partners via survey is depicted in the graph, below: Figure 4: Modal split found by survey for all partners, based on mode used for the maximum distance traveled. Distances were calculated from times reported, based on assumed average travel speeds (see Annex). Bicycle Modal Split Even more than other official indicator data, it was extremely difficult to obtain official indicator data from all partners, describing the amount of cycling in each Partner Place. If the results of partner investigations are conclusive, some Partner Places evidently have never had such data generated, in all history, an important contribution of the BICY Project. Moreover, the methods used for the data that was obtained were varied, and the time periods studied also varied, reducing the validity of their combined use. Quite a few were explicitly guesstimates. Clearly this is an indicator of utmost importance, in an effort focused on increasing cycling, the mandate of the BICY Project, and therefore generating a credible and consistent modal split for bicycling has been a core goal of the survey. 2 The term bike share is tempting to use, but due to the rise of bicycle hire systems typically referred to as bike share or bikesharingg systems, the term is only used for that new purpose here.

15 Page 15/152 The difficulty in obtaining official bicycle modal split for all partners was substantial. Many partners reported the data simply did not exist, and the data that did exist was often questionable. The following incomplete table of the data received to-date illustrates the difficulty obtaining this essential metric, not just for bicycling but for all modes: Bicycle Pedestrian Bus Train Car Car+Motorbike Motorbike Pub Transport Ferrara Comacchio Ravenna Cervia Graz Erfurt Erfurt Erfurt Košice-R Košice-C Michalovce SNV Erfurt 2: Erfurt 3: 2003 data Prague Koper Velenje Budaörs Table 1:. After more than a year of follow-up, the bicycle mode share data provided by partners was still very incomplete. Mode share for Erfurt was found by UNIBO-DICAM in two studies; Erfurt 2 was determined most appropriate to use. Some partners resorted to guesstimates. Thus the survey aimed to generate current modal splits across all partners in a unified, standardized manner. The geography of cycling becomes apparent in the result found. Figure 5: Modal split for bicycling found by survey. Top: modal split based on distance, calculated from travel times reported for each mode, in a typical workday (average speed assumed to be 12 km/hr). Organized to emphasize geographic differences. Western partners places are on the left, eastern partners places are on the right In the BICY project, a conscious effort was made to pair Western places (cities and regions) with relatively high bicycling rates ( advanced cycling cities ) with Eastern places which hope to improve similarly ( starter cities

16 Page 16/152 and regions). Of course, most Western places also need to improve, and there is considerable variation there as well. However, our modal split findings discovered a clear difference between East and West cities, and so all graphs related to the survey are presented with places ordered geographically from west to east, rather than by alphabetical order, population size, or other method. First by country from west to east, and again within countries, from west to east. Comparing Modal Split Data Figure 6: Comparison of modal split data by source (official/partner data and survey findings data, only for those partners for whom both types of data are available; standard max distance modal splits used). Exclusions Due to evident problems with two survey groups, Erfurt and Budaörs, they have been removed from most presentations in this document. Erfurt modal split was much higher than the official (see figure), and the survey response is very low. Budaörs survey response was large, but unfortunately there were zero minors interviewed. As the share of minors bicycling ranges from 0% (Slovakian Partner Places) to 62.4% (Graz, Austria), a correction based on estimation is not possible. (However, Budaörs bicycle mode share was already close to zero.) Official data is substituted for Erfurt s bicycle mode share, where possible.

17 Page 17/152 COMMON INDICATORS The Common Indicators are numerical relationships found to be meaningful for assessing cycling. Typically they are mathematical relationships between one or more pieces of data (obtained through survey, and/or from official data sources). The BICY Project has sought to explore relationships and identify new and highly useful indicators which can be used widely not only in assessing current conditions, but in setting a course to improve them. The most essential indicator is surely the cycling mode share (% bicycling), first and foremost. Mode share tells us cycling levels. It is also notoriously difficult to obtain, and even more so to compare the various measurements of different places, as it may come in many forms, for many times. Because of its special nature, many considerations, and sources from both the survey and official indicator efforts, as well as third party sources, its development is detailed in the preceding section. The modal split then becomes a principal component of many Common Indicators. This section describes briefly the primary indicators calculated using the collected data: Firstly, some widely used standard indicators for cycling infrastructure and usage need to be determined in any case. This is necessary to be able to compare the results with the outcome of other projects and best practice cities. However, some alternative, more significant indicators will be proposed or identified during this Work Package (WP). Both the Standard Indicators and exploration of additional indicators are described below. Development of Common Indicators A literature review was conducted by DICAM transportation specialist Silvia Bertoni, who informed the initial request for indicator data and the development of the preliminary Standard Indicators, goals of the data collection effort. As survey and indicator data became available, experimentation led to additional indicators being conceived of and tested. Common Indicators Hypothesis The Common Indicators followed the hypothesis that infrastructure is essential to increasing cycling levels. This hypothesis is based on the literature, on review of leading cycling cities in the analysis of good practices (State of the Art report, WP3.2.1), and by sharing and comparing personal experiences with the greater BICY Project community. In Portland, Oregon, a top cycling city in the USA, an extensive GPS-based study of actual cyclist travel behaviour, found that although the bikeway network was only 8% of the total network, cyclists made nearly half (49%) of their travel in those bikeways. (Dill, 2009) Simply put, without a safe, comfortable, convenient place for cycling between destinations; and without a safe, secure, convenient place to park a bicycle at those destinations; cycling sinks. In most places today, those places do not exist unless we deliberately and consciously provide them. Thus all three primary Common Indicators (the Standard Indicators) use the length of exclusive bikeways (total km of both lanes and paths, including cycle tracks, combined) as a primary component, which is then compared with population, roadway length, and urban area in three separate indicators. Bicycle parking is further included in the subsequent proposal of the BICY Indicator.

18 Page 18/152 Standard indicators The following standard indicators shall be calculated for each partner (if not already available): Cycling index (km of cycle track per person) Network coverage index ( road km per km of cycle track) Network density index (cycle track km / area in km2 ) BICY Indicator A new indicator has been proposed to give a simple one-number measurement of cycling quality. This indicator would combine the second of the Standard Indicators (bikeway ratio to road length) in a ratio with an Endpoint index, another new indicator measuring the relative spatial availability of parking provisions: End-point index = number of public bicycle parking places / number of public car parks Coverage index = km effective bike path / km road network. With these two indicators combined, we calculate a comparison combining not only the quality of provisions for getting to one's destination, but also we compare the quality of those destinations. BICY Index= Coverage Index * Endpoint Index This would then be a single number from 0 to 1, with 0 being the worst for cycling, and 1 being the best.for cycling. Of course this indicator ignores many factors such as topography (hills), weather, and the nature of traffic including driver behavior, which could also be accounted for, but it is an important indicator in that it seeks to capture the overall effect of infrastructure both the connections and their destinations and specifically those concrete facilities and amenities which can be provided by a city for its cyclists. In this way it is also a proxy for the potential of cycling, and an indicator of the political, economic and cultural support for cycling. Unfortunately, as discussed elsewhere, obtaining end-point data from partners has not been successful enough to-date to test the BICY Index as comprehensively as desired. However, some data exists and more can be obtained for future analysis. An exploration of alternatives to the BICY Indicator is begun in the Annex on Experimental Indicators. These will be further developed and applied where possible in the TSR. Calculation of Common Indicators The Common Indicators described above are applied to the data obtained in the BICY Project to-date. Standard Indicators The following standard indicators are calculated here, for each partner: 1. Cycling index (km of cycle track per person) 2. Network coverage index ( road km per km of cycle track)

19 Page 19/ Network density index (cycle track km / area in km2 ) Cycling Index Comacchio Ferrara Ravenna Graz Erfurt Cervia Population Bikeways km Cycling Index (Raw) Cycling Index (10K) Michalovce Košice-C E E SNV Koper Velenje Budaörs Prague E Figure 12: Table showing source data and resulting calculation of Cycling Index Network Coverage Index Ferrara Total roadways km 990 Bikeways km 88.7 Coverage Index (Raw) Coverage Index (1K) Comacchio Ravenna Cervia Graz Erfurt Michalovce SNV Koper Velenje Budaörs Košice-C Prague Figure 13: Table showing source data and resulting calculation of Network Coverage Index Network Density Index Michalovce SNV Comacchio Ferrara Ravenna Graz Erfurt Koper Velenje Budaörs Cervia Košice-C Prague Area 82.2 Bikeways km Coverage Index (Raw) Coverage Index (10X) Figure 14: Table showing source data and resulting calculation of Network Density Index Assessment of Standard Indicators Based on the calculation of the Common Indicators above, assessments of the indicators predictive ability, particularly with regard to cycling behavior, are made here. Correlations between Indices and Bicycling Indicators The calculated indicators have been tested against the indicators of bicycling. This is an exploratory process. Where a correlation is observed, further analysis is warranted, with the goal of first better understanding the factors which create current conditions, and second producing reliable prediction models for creating target cycling levels in the future. As a first step, scatter plots are created using a measure of bicycling and the index being correlated. As bicycling is considered the dependent or response variable, it is plotted on the vertical or Y-axis, while the

20 Page 20/152 independent or predictor variable is plotted on the horizontal or X-axis. The best measure of cycling available for BICY Partner places is that created by the BICY Project survey, the Bicycle Modal Split (max time version), although other measures have special application, such as the share of regular bicycle commuters. In general, the Cycling Index has demonstrated good predictive power. This may be partly a function of the higher reliability and consistency of population data, compared to other indicator data sources. A select few relationships are shown below; more are provided in the Annex, with the final analysis provided in the TSR. Cycling Index Compared with Bicycle Mode Share By far the closest match seen in basic testing has been the Cycling Index, crossed with the bicycle mode share. The statistical R2 value is 0.812, with slope 180.6, as shown in the below graph: Figure 15: Cycling Index and associated Bicycle Mode Share (% cycling) In addtion, separating the Partner Places by size (cities being > 100,000 in population, towns be smaller), and perfoming the analysis again, we find even stronger relationships:

21 Page 21/152 Figure 16: Cycling Index for cities (population > 100,000), and associated Bicycle Mode Share (% cycling) Figure 17: Cycling Index for towns (population > 100,000), and associated Bicycle Mode Share (% cycling)

22 Page 22/152 Very interesting that the relationship is even stronger when viewed in terms of size. The R2 values become stronger, however the slopes are different.

23 Page 23/152 PROJECTIONS Although the Transnational Strategy Report is the final venue for projections, some preliminary predictive powers are described here, within this Common Indicators report. These predictions allow a variety of costbenefit analyses for combinations of potential investments aimed at increasing cycling. Two primary sources of predicting changes in behavior, developed in this WP, are: Survey Scenarios: based on responses to the Detailed Mobility Survey Indicator Calculations: Models based on the development of the Common Indicators Survey Scenarios The detailed mobility survey queried respondents as to what they would require to become a regular cyclist, as well as what they would require to become a regular user of public transport. Respondents were asked to check any number of options under the two respective headings: What are the minimum requirements that would convince you to use the bike for your daily trips? What are the minimum requirements that would convince you to use public transport for your daily trips? Based on these responses, three scenarios for increasing cycling, and several more focusing on increasing public transport use, were developed, from which predictions of new travel behaviour (new modal splits) have been calculated as a result. As an important step in scenario generation, Potential Cyclists (people who could become regular cyclists) were identified by their survey responses. Potential Cyclists were people who satisfied ALL of the following: Currently are not regular cyclists (according to modal split via the maximum distance criteria) Did not declare that they would never use a bike Regular total travel was less than 30min per day (by individual and/or public transport, if they were regular users of those modes) Who are regular walkers Next, the survey answers of this group of Potential Cyclists were analysed in light of three scenarios, using the data as to what changes would induce them to begin cycling regularly. The scenarios have been defined as follows: Scenario 1 Existence of cycle ways and/or traffic limitations on ALL of their regular travel path; Existence of secure bicycle parking at all destinations

24 Page 24/152 Scenario 2 Required all o f the above, as well as: Availability of cycle hire facilities at all destinations Scenario3 Required all of the above, as well as: Existence of bike path with sun, wind and rain protection Many more scenarios can be generated, to explore the personal influence of various investments. It is noteworthy that these investments are conservative; for example, in Scenario 3, one had to choose every single option above (entire route; bike parking everywhere; cycle hire everywhere; and finally, the rare concept of a continuous bike path with rain, sun and wind protection. Might some people have answered only with that last one? Yet they would not be counted as becoming a new cyclist, if that dream came true. Of course, asking the public to reliably predict whether their conversion to regular cycling is not a hard science, and people might predict incorrectly. Given that relatively large investments such as these would surely convert people who never imagined themselves cycling, it is quite conceivable that the scenario predictions, particularly for large combinations of investments, are very much underestimates. Public Transport Scenarios Although not elaborated upon here, a number of Public Transport (PT) scenarios were generated, and cross analyzed with the bicycle scenarios. It is interesting to infer the degree of competition between cycling and PT use, and also their synergies. For example, improvements to PT can reduce cycling, and improvements for cycling will reduce PT use; there can also be cases where both are increased. Policy decisions can benefit from understanding the costs and effects of different combinations of improvements. Further analysis in the Transnational Strategy is planned, to explore how much it would cost to reduce automobile mode share by implementing new PT services/infrastructure and compare with how much it would cost to implement new bicycle infrastructure to obtain the same reduction, as well as a search for ideal combinations of investments to maximize both. The graphs shown below illustrate potential trade-offs across a range of eight scenarios:

25 Page 25/152 Figure 18: Comparing western Partner Place, Ferrara, above, with eastern Partner Area, Košice, below. Graphs show an internal comparison of projected cycling and PT mode shares under eight scenarios: three scenarios focusing only on increasing cycling (PT decreases), and five additional scenarios focused only on increasing PT (cycling decreases).

26 Page 26/152 Projected Carbon Reductions Utilizing the newly estimated shares of regular cyclists, and/or regular PT users, it becomes possible to calculate the change in travel behavior. These are discussed in the Beginning with the distance traveled by car, motorbike, and PT for each person who regularly uses those modes in the survey, we next calculate for each scenario the new distance traveled by car, motorbike, and PT for each person who regularly uses those modes, but who changed to bike in the respective scenario. These are the carbon producing km saved per workday for each scenario. Then the carbon production saved per regular travel day can be calculated with certain assumptions on the average CO2 carbon production per km for car, motorbike and PT. In particular we assumed 180g/km for car, 120g/km for motorbike and 90g/km for public transport (bus in most cases; although reduced passenger loads may not substantially affect CO2 output, service may be cut back if ridership decreases). These are of course very crude assumptions that may vary strongly from partner to parter, and technology to technology; but as estimates the can be an effective first step in identifying the relative effects of policy options. To obtain the final comparison, we divide the CO2 emissions (by weight) saved per regular travel day in the new scenario, by the total CO2 produced under current survey conditions, to obtain the estimated of percentage carbon emissions reduced by each scenario. Scenario 1 Carbon Reductions Carbon reductions grew for Scenario 1, corresponding to a projected increase in bicycle travel. Figure 19. Carbon reductions grew for Scenario 1, corresponding to a projected increase in bicycle travel.

27 Page 27/152 Scenario 2 Carbon Reductions Carbon reductions grew for Scenario 2, corresponding to a projected increase in bicycle travel. Figure 20: Carbon reductions grew for Scenario 2, corresponding to a projected increase in bicycle travel. Scenario3 Carbon Reductions Carbon reductions were highest for Scenario 3, corresponding to a projected increase in bicycle travel. Figure 21: Carbon reduction percentages for Scenario 3, corresponding to a projected increase in bicycle travel.

28 Page 28/152 Average Carbon Reductions Based on the foregoing, the following average carbon reductions were found: Figure 22: Carbon reductions for each scenario, corresponding to projected increases in bicycle travel. Indicator Calculations Predicting future cycling can be accomplished based on the relationships seen between partners Common Indicators. In particular, trend line models arising from the calculation of Common Indicators, and described earlier in the Common Indicators section, allow projections of future cycling based on changes in the overall characteristics of the cycling network. The strongest correlation and therefore the indicator with the greatest predictive power at this time is the Cycling Index, which finds and compares the ratios of bikeway length to population. Essentially, how much bikeway per person is available in the study area?

29 Page 29/152 Cycling Index Predictive Power The top model based on official indicator and survey data is again, found as follows: Figure 23: Cycling Index shown as it relates to Bicycle Mode Share (% cycling). The model was further refined into two models, one for cities and one for towns (see above section). Target Cycling Levels Many targets have been adopted in recent years. The Charter of Brussels, 2009, was signed by at least 36 cities, including BICY partners Graz and Ferrara, along with Budapest, Brussels, Ghent, Milan, Munich, Seville, Edinburgh, Toulouse, Bordeaux, Gdansk, Timisoara, and more. The Charter commits the signatories to achieve at least 15% of bicycling modal share by 2020, and calls upon European institutions to do likewise. Other commitments have included 10% by 2010 and 20% by It is safe to say there s no upper limit for cycling goals; targets represent minimum short-term goals to rapidly increase cycling to the levels that can and must be attained. In the context of the BICY Project, target cycling levels can also be generated based on projections of reasonable results expected from interventions. From these, cost-benefit analyses may be performed. Cost of Target Cycling Levels Utilising the linear model identified for the Cycling Index, with bicycle mode share predicted by bikeway km per population using the formula y = * x for larger cities, costs for an increase of cycling to 10%, and to 15%, are projected for eastern starter cities.

30 Page 30/152 Foundational data for cost-benefit use is provided in the table below (Figure 24), which includes data on the cycling population, and the calculation of the Cycling Index for each place. The cost data for Prague 5 and Budaors was clearly incorrect, but evidently due to errors in provision, so assumptions were made to put it in the proper formats. Cost data for Koper marked bikeways was borrowed from Velenje (highlighted orange). Foundational Data Population Bikeways km Cycling Index (Raw) Fit Line Slope (city & town) Marked Bikeway Physical Bikeway Share Adult Workers Population Adult Workers Share Biker and Worker Share Regular Bike-Work Pop. Reg. Bike Workers Košice Michalovce SNV Prague 5 Koper Velenje Budaörs E E E , , % % 2.30% , , % % 3.40% , , % % 2.70% , , % % 3.00% , , % % 2.10% , , % % 3.30% , , % % 1.90% 537 Figure 24: Foundational data for cost-benefit calculations, including population, length of bikeways, the resulting Cycling Index; the slope used for calculating the number of new km of bikeways needed to reach target cycling levels (one for cities, one for towns); and the cost of bikeways; and the population of adult bicycle commuters.. The above costs will be very useful now in generating cost-benefit analyses. COST-BENEFIT ANALYSES In recent years, great leaps have been made in our understanding of the overall benefits of cycling, along with increasingly reliable means of quantifying those benefits economically. The BICY Project aims to use its insights into how cycling investments may increase cycling, to also estimate the benefits of those investments such that informed policy decisions can be made. The benefits of cycling are legion, although debate continues as to how to quantify these costs. As early as 1977, the literature attempted such analysis. (Everett, 1977) The debate over whether cycling, on balance, is even good for us seems firmly settled. For example, a recent major review focusing on the Netherlands, an area where cycling is relatively very well understood, found on average, the estimated health benefits of cycling were substantially larger than the risks relative to car driving for individuals shifting their mode of transport. (Hartog, 2010) These benefits should only increase as cycling increases, for many reasons, including the Safety in Numbers effect, whereby the risk to each cyclist is reduced with each additional cyclist. (Jacobsen 2003) Health Economic Assessment Tool (HEAT) The Health Economic Assessment Tool (HEAT), developed by the World Health Organization (WHO) Europe through Project PHAN under the European Union in the framework of the Health Programme , is a

31 Page 31/152 cutting edge tool first made public in 2007, with a new release in (WHO 2011) The HEAT team has been very helpful in providing support to the BICY project for use of HEAT in this report. HEAT provides a framework for economic assessment of transport infrastructure and policies in relation to the health effects of walking and cycling. Recent studies form the basis. Only regular trips are considered, and only for adults3, so the effect is presumed to be an under-estimate. Investments in cycling facilities are strongly expected to increase non-regular cycling trips, as well as increasing walking. To use HEAT with the BICY project findings, we must provide data including: 1. Current cycling levels (total number of regular trips) 2. Future cycling levels (total number of regular trips, after the investment) 3. Cost of the investment/intervention producing the increase The BICY Project has produced answers to those required questions: 1. Because we have identified regular cyclists and their regular trips, as well as those who work, we can calculate the estimated total current regular work trips, satisfying the first requirement. (See figure 24, in the preceding section.) 2. Using any of the predictions developed here, from the relationships identified, we can estimate the effect of investments in new bikeways on cycling levels, providing for the second requirement. (See figure 25, below.) 3. Using the cost estimates for new bikeways as provided by partners, the final requirement, cost of the investment, may be calculated. (See subsequent sections on HEAT calculations.) This analysis will be further refined, and expanded to additional partners, for the final Transnational Strategy Report. The predictive resources which can be used in HEAT cost-benefit calculations include: Benefits predicted by implementing survey scenarios Benefits from models (e.g., Standard Indicators relationships to bicycle mode share) These can further be combined with other benefits analysis, such as carbon reduction benefits. 3 HEAT age group is for approximately years of age, which is close to our survey adult category of years.

32 Page 32/152 The costs for a solution using either all new separated bikeways (paths); or for all new painted bicycle lanes; or a mix with half of each, are given below (Figure 25). Target 15% Košice Target Cycling Index (Raw) Target Total Bikeway km New km needed Cost, Marked Bikeways 9,799,811 Cost, Physical Bikeways 23,332,883 Middle cost (half each) 16,566,347 Michalovce ,910,414 4,341,851 3,126,132 SNV ,774,703 6,625,558 4,200,131 Prague ,390,004 14,901,115 9,145,560 Koper ,067 5,765,137 3,155,102 Velenje ,362 2,947,956 1,633,659 Budaörs ,281,699 3,750,513 3,516,106 Figure 25: For target cycling levels of 15%, in eastern Partner Places, predicted costs are given for a solution using either all new separated bikeways (paths); or all new painted bicycle lanes; or a mix, half of each. It is important to note that the true cost of a large-scale investment, if done all at once, should be substantially lower than doing so in pieces, for many reasons (but certainly, economy of scale). Thus the above estimates are probably higher than they would be in a concerted and comprehensive effort to increase cycling quickly; moreover, doing all in one big step should allow maximum gains from the effects. The foregoing data is now ready to plug in to the HEAT online cost-benefit tool. HEAT Calculation assumptions: These figures were input to the HEAT online calculator, accepting the default assumptions for European mortality rate for each country, value of a statistical life4, and for discount rate (5.0%) used to calculate the net present value of the investments. For the required measure of amount of cycling in the study population, duration (average time cycled per person) was entered. This is ideal because (a) it s the direct data from the survey, and (b) it s the exact measure for the analysis. (Other options would be trips and distance.) Time spent cycling was assumed to be 220 days per year (60% of days), to account for and exclude non-work days, holidays, weather, and other aberrations in pattern. This should be a conservative under-estimate, allowing for approximately 41 days off from cycling to work, in addition to 104 weekend days, each year. The time spent cycling after an increase in cycling was kept constant, although it is possible it could increase or decrease slightly for any number of reasons. In the TSR this can be treated more fully. It was assumed that 100% of the increase in cycling was due to the intervention (although in practice there can be many reasons, which may be interdependent) euros was given as the average for European value of life.

33 Page 33/152 The time for implementation was assumed to be 3 years.5 This is short enough to be true for any place determined to create new cycle ways. One year is even possible. The cost of doubling regular cycle trips is estimated at 1,393K euros. The estimated costs used are the cost of half new bike paths, and half new painted bike lanes. This averaged (middle of the range) cost is most appropriate because, although paths are preferred, the true cost of a large installation is hoped and expected to be lower than costs taken from smaller installations. In any case, these costs are only estimates and better data is needed. For the purposes of the benefit cost calculation, total savings were calculated over the same time period as that used to calculate average annual savings (5 years). The process and results are shown in the next section. HEAT Cost-Benefit Calculation: 15% Cycling by 2020 Setting a course for increased cycling in eastern Partner Places, we utilize the results of WP3 to generate costbenefit analysis with HEAT for a scenario of 15% cycling by 2020 (based on the 2009 Charter of Brussels mentioned above). Higher targets are possible and encouraged and will be discussed in the TSR. The data now available, as detailed in preceding sections, was used to generate a prediction of increased cycling resulting from investing in new cycling infrastructure. This data is then provided to the HEAT online calculator to generate cost-benefit information. The following slide helps illustrate the process (Figure 26): 5 The legendary transformation of Bogota, Colombia, including many changes to bikeways, urban parks and parking, and most notably with the major intervention of the enormous and most successful Bus Rapid Transit system in the world, TransMilenio, was largely accomplished in an aggressive 3-year program around the year 2000.

34 Page 34/152 Figure 26: Illustration of the steps in calculating a HEAT cost-benefit analysis for 15% new cycling in eastern Partner Places by 2020, made using WP3 outputs (survey and indicator data). The first two tables show above are given in Figures 24 and 25. The last table results from the HEAT calculations, below (Figure 27):

35 Page 35/152 Figure 26: Table detailing final inputs and resulting outputs in calculating a HEAT cost-benefit analysis for 15% new cycling in eastern Partner Places The results show very high cost-benefit ratios, illustrated below in Figure 27: Figure 27: Graph illustrating cost-benefit ratios for investing in cycling according to a HEAT cost-benefit analysis for a target of 15% cycle commuting among adult workers in eastern Partner Places The high benefit-to-cost ratio is impressive. This is actually a very conservative estimate: only working adult bicycle commuters are considered, and the only benefit assessed is the economic benefit from preventing

36 Page 36/152 mortality through increased exercise by cycling. Of course new cycling infrastructure would increase cycling for all ages and for non-workers as well, and provide myriad other new benefits. Moreover, the benefits here are calculated only for the short-term (8 years, to 2020), although they would continue to accrue for many years longer. The time to implementation of the bikeways is also conservatively long, 5 years. Ideally new bikeways would be created in less one year, certainly within two years; the sooner and the larger the investment, and the sooner and the greater are the benefits obtained. Yet in just 8 years, even with those limitations, the bikeways more than paid for themselves as detailed in the following graph illustrating the HEAT output (Figure 28): Figure 28: Graph illustrating economic savings thanks to investing in cycling, according to a HEAT cost-benefit analysis for a target of 15% cycle commuting among adult workers in eastern Partner Places The reason the City of Košice has such a high value is that it is a larger city and so in the models found and described here, the expected increase in cycling is larger for a given investment than for smaller cities. Smaller cities will still benefit greatly from provision of bicycle facilities. Value of Carbon Reductions A detailed analysis of carbon reductions from increased cycling is beyond the scope of this report due to the many unknowns and disagreements regarding climate change, as well as the lack of complete data on carbon emissions in partner places (although partners were encouraged to provide these if available, and estimates can be made). However, given the percentage of reductions in carbon emissions predicted herein, these reductions will be both substantial and significant.

37 Page 37/152 The average of peer-reviewed carbon emissions values is taken to be (43/tC or 12/tCO2). As with all climate change predictions, it must be emphasized that the average falls far short of worst-case scenarios, necessarily making this exercise a conservative estimate at the outset. The life-cycle carbon emissions of a single cyclist (on either a standard bicycle or an electric bicycle) is given as approximately 1/13 that of a car passenger, and approximately 1/5 that of a bus passenger, per km traveled, on average (approximately 21 g/km). (European Cyclists' Federation, 2011) As a life-cycle assessment, in fact this figure includes the carbon emissions embedded in the manufacture of the bicycle and emitted in the provision of the food that the cyclist eats. A rough analysis of the annual savings from new commuter cyclists based on the 15% target generated for the HEAT calculations (above) is thus presented here (Figure 29): CO2 value (Target 15%) Current Bike Commuters Future Bike Commuters Average Cycling/Day (hours) Average Cycling (minutes) Estimated distance/day (km) Yearly distance (km) Tons CO2 saved v. car (person) Value per person/year Tons CO2 saved v. bus (person) Value per person/year New bike commuters Total savings over car Total savings over bus SNV Velenje Košice Michalovce Prague 5 Koper ,703 3,028 2,252 3,055 2,466 2, Budaörs Figure 29: Table calculating estimated annual economic savings thanks to carbon reductions from an increase to15% commuter cycling resulting from investments in cycling infrastructure. Total economic equivalent for car, or for bus. There are many assumptions hidden in the above figures which could be questioned, such as the amount of food and type of food a typical cyclist eats; however, for purposes of this cost-benefit evaluation there is strong support that the overall annual benefits would be positive for a wide range of assumptions. Although the savings predicted are not large, they combine with other benefits to promise even more strongly that bikeways investments pay for themselves many times over. As with the HEAT calculation, this is a conservative estimate and only a limited view: only commuter cyclists are considered. In reality, commuter travel makes up only a fraction of overall travel (less than 20%), so the total benefits can be many times greater perhaps five or more times greater, here when all travelers, and all trips, are considered. In Košice alone, this would thus add approximately euros in carbon benefits over ten years if all new cyclists switched from car travel.

38 Page 38/152 CONCLUSIONS In the quest to increase cycling, the provision of infrastructure cannot be neglected. The results of this study show a strong correlation between new infrastructure, and new cycling. After assessing all the foregoing components of BICY Project WP3, including stakeholder interviews, the SWOT analysis, and data from our three primary sources (official data, detailed mobility survey data, and OpenStreetMap spatialanalysis), a clear recommendation has emerged: building more bikeways is essential to increasing cycling. Bikeways in all forms including painted bicycle lanes, constructed bicycle paths/cycle tracks, and traffic calmed routes such as 30 zones are a strong prerequisite to cycling for a vast majority of today s cyclists, and are particularly necessary for attracting potential cyclists, who comprise the large and highly promising group waiting to adopt cycling for everyday travel that the BICY project is most focused on. While bikeways alone cannot be a complete strategy for achieving a healthier modal shift to cycling, to ignore them appears equivalent to closing the door on new cyclists. Support for a multiplicity of measures approach was also found. However, the strongest predictor of increased cycling, and the top request from stakeholders, has been to improve the amount, and the connectivity of, the bikeways networks. Certainly additional strategies such as education, promotions, and provision of secure bicycle parking as well as options for borrowing bicycles (bike sharing systems) and for using bicycles with public transport are critical as well, along with other measurs to slow car traffic, and to create friendlier traffic rules and friendlier traffic behaviour. Certainly the feelings of cyclists are paramount, and where cyclists feel unsafe, or disrespected, or that conditions are adverse, cycling levels suffer. Women in particular turn away from cycling as conditions degrade. The strong linear relationship of higher cycling rates where larger amounts of bikeways are provided was true for all types of cities. A larger positive response was found for larger cities, but the correlation was also very strong for smaller cities and towns. This finding was made possible in part because of the quality and consistency of the data and the diversity of places studied. It is rare to have such consistent and representative data on cycling, and the BICY methodology has potential to become a standard for low-cost assessment. The economics of investing in cycling are overwhelmingly supportive. The cost-benefit analyses shown here, in terms of reduced carbon emissions and the value of lives saved, is only a tiny view of a much larger bounty of rewards. These analyses already show that bikeways pay for themselves, but if we begin to account for the additional benefits of reduced illness; reduced noise and air pollution; reduced damage to roadways and historic buildings; myriad social benefits; and the local economic benefits from cyclists increased spending power coupled with increased local shopping; plus a more attractive and livable urban environment and the retention of local funds from imported oil, an impressive and robust economic argument emerges. It is not by chance that places with high cycling tend to fare better than neighboring places with lower cycling in every sense, and certainly economically. An investment in cycling has every promise of strong returns not only for the health and well-being of a place, and its people, but for the sustainability and health of its economic future as well.

39 Page 39/152 References Alliance for Walking & Cycling Bicycling and Walking in the United States: 2012 Benchmarking Report. Downloaded January 24, Andersen LB et al.: All-cause mortality associated with physical activity during leisure time, work, sports, and cycling to work. Arch Intern Med 2000: 160: Besson H et al.: Relationship between subdomains of total physical activity and mortality. Med Sci Sports Exerc 2008: 40: Cavill N et al. Economic assessment of transport infrastructure and policies. Methodological guidance on the economic appraisal of health effects related to walking and cycling. Copenhagen, WHO Regional Office for Europe, 2007 Charter of Brussels Dill, J Bicycling for Transportation and Health: The Role of Infrastructure. Journal of Public Health Policy, Vol. 30 Suppl 1, 2009, pp. S European Commission Citizen Benchmarking Initiative. Accessed repeatedly during January European Commission Online Benchmarking Tool, Year 2 Data, Citizen Benchmarking Initiative. Accessed repeatedly during January European Commission. Eurostat: your key to European statistics. Accessed repeatedly during January European Cycling Federation CyCle more often 2 Cool down the planet!: Quantifying Co2 savings of cycling. Available online at ECF.com. European Environmental Agency Accessed December Everett, Michael Benefit-Cost Analysis for Labor Intensive Transportation Systems. Transportation 6 (1977) Gatersleben, B. and Haddad, H. (2010). Who is the typical bicyclist? Transportation Research, Part F, 13, Health economic assessment tool (HEAT) for cycling and walking Copenhagen, WHO Regional Office for Europe, 2011 ( accessed repeatedly throughout January 2012).

40 Page 40/152 Heinen, Eva; Maat, Kees; van Wee, Bert The role of attitudes toward characteristics of bicycle commuting on the choice to cycle to work over various distances. Transportation Research Part D: Transport and Environment. Volume 16, Issue 2, March 2011, Pages Jacobsen, P. L. Safety in Numbers: More Walkers and Bicyclists, Safer Walking and Bicycling. Injury Prevention, Vol. 9, No. 3, 2003, pp Johan de Hartog, Jeroen; Hanna Boogaard, Hans Nijland, and Gerard Hoek. Do the Health Benefits of Cycling Outweigh the Risks? Environmental Health Perspectives v o l u m e 118, n u m b e r 8, August 2010 Kahlmeier, Sonja; Nick Cavill, Hywell Dinsdale, Harry Rutter, Thomas Götschi, Charlie Foster, Paul Kelly, Dushy Clarke, Pekka Oja, Richard Fordham, Dave Stone and Francesca Racioppi Health economic assessment tools (HEAT) for walking and for cycling. Methodology and user guide. Economic assessment of transport infrastructure and policies. WHO Europe. Project PHAN, European Union in the framework of the Health Programme (Grant agreement ) Kimmel, S. R., and R. W. Nagel Bicycle Safety Knowledge and Behavior in School-Age Children. The Journal of Family Practice, Vol. 30, No. 6, 1990, pp Košice region (source, page 12, Košice Indicators Report) Available on BICY.it internal web pages. LIZCAN (youtube user). (R)evolutions per Minute: Cargo Bikes in the U.S. Video. Uploaded Oct. 15, Matplotlib. A Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matthews CE et al.: Influence of exercise, walking, cycling, and overall nonexercise physical activity on mortality in Chinese women. Am J Epidemiol 2007: 165: Nankervis, Max The effect of weather and climate on bicycle commuting. Transportation Research Part A 33 (1999) NetworkX. A Python language software package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. Oja, L., and T. Jürimäe Physical Activity, Motor Ability, and School Readiness of 6-Yr.-Old Children. Perceptual and Motor Skills, Vol. 95, No. 2, 2002, pp Oja et al. (2011): Health benefits of cycling: a systematic review. Scand J Med Sci Sports 2011, Apr 18. doi: /j x. [E-pub ahead of print]. OpenStreetLib. A Python language software package developed by UNIBO-DICAM for the BICY Project, which will be made publicly available as an open source Python module when released. Python Programming Language. WHO Europe Accessed throughout January WHO/Europe Health Economic Assessment Tool (HEAT) for Cycling.

41 Page 41/152 Williams, A. F. Factors in the Initiation of Bicycle-Motor Vehicle Collisions. American Journal of Diseases of Children, Vol. 130, No. 4, 1976, pp Woodcock, James. Health and Climate Change 2: Public health benefits of strategies to reduce greenhouse -gas emissions. Urban Land Transport, Lancet 2009; 374:

42 Page 42/152 ANNEXES A. BICY Areas and Places Although region and province-level distinctions are important, it is important that we compare similar types of places (city to city, not city to region), because behavior and characteristics of a city will vary greatly from those of even its own larger region or province. Cycling in urban areas is the primary focus. We also can compare eastern places with western places (eastern places being defined as having been part of the former Soviet Union). Thus partners constituting larger Partner Areas (Košice, Ferrara and Ravenna) have responded by providing additional data for sub-areas (cities): Ferrara, Italy is a province which has provided data for the main city of the province, Ferrara, and has chosen one more city to supply data from, Comacchio, a smaller, coastal town. Ravenna, Italy is also a province, which has provided data for the main city of the province, Ravenna, and has chosen one more city to supply data from, Cervia, which like Ferrara Province s Comacchio, is also a smaller, coastal town. Košice Region (KSR) is an Autonomous Region of Slovakia, providing regional data, as well as data for three cities within the region: Košice, Michalovce, and Spišská Nová Ves. Diversity of Types of Place In the following graph, the number of larger Partner Areas, and the number of smaller Partner Places, is compared. Note that a Partner Area will contain a number of smaller areas. For purposes of the BICY Project, a City was determined to be any place with an official population greater than 100,000; anything smaller was considered a Town.

43 Page 43/152 Figure A.1: Quantity of each type of place: Region (1) or Province (2),; Cities (7); Towns <= 100k population (9). As discussed below, not represented in the above table are large cities with a smaller study area, e.g., Prague (containing Partner Place, Prague District 5), and Budapest (which closely neighbors the much smaller town of Budaörs). Nor are metropolitan regions taken into account. However, in the analysis process, it will be valuable to include those areas where possible for a variety of comparisons and other inquiries. Although larger areas include provinces and regions, because urban cycling is the focus, most of BICY Project s numerical indicator-based analyses utilise only the data from the level of cities and towns. Qualitiative assesments and some numerical analyses may also include the levels of region and province. A comparison of the array of sizes, by population, of both Partner Areas and Partner Places, is below.

44 Page 44/152 Figure A.2: places studied in BICY project, sorted by population size. Larger areas, and their sub-areas, are both included. In the case of Prague, the study area is District 5, which is the size of a town, yet it is within a much larger city. The region of Košice includes the large city of Košice, and two towns, Spišská Nová Ves (SNV) and Michalovce. Ravenna and Ferrara are both provinces in the Region of Emilia-Romagna, and both contain cities with the same name as the province. Both include a smaller, more tourism-oriented coastal town, alongside the Adriatic Sea. The small town of Budaörs is adjacent to the much larger city of Budapest, however, the two are totally isolated from connection by physical travel, except for one large road. A large opportunity in the BICY Project is the comparison and harmonization of approaches between East and West. When comparing the diversity of Partner Places, the range of sizes is more pronounced in western partners, but both have a range. Metropolitan areas (which for many purposes can be considered regions), as well as larger cities, which contain some of our Partner Places (e.g., Budaörs, Košice and Prague), were not studied, as the focus here is first and foremost on the cyclist s local urban experience. However, regional factors such as access to transit will certainly have an influence on the survey responses.

45 Page 45/152 Figure A.3: comparison of population size of western places studied in BICY project. Figure A.4: comparison of population size of eastern places studied in BICY project.

46 Page 46/152 A summary table of Partner Areas and Partner Places is below, including some very brief descriptively distinguishing notes in light of the data received: PARTNER COUNTRY AREA CITY DATA NOTES (within region if >1 places) Budaörs Hungary Town Budaörs Nearly adjacent to Budapest Erfurt Germany City Erfurt Ferrara Italy Province Ferrara City Ferrara Very similar behavior w/comacchio Town Comacchio 6X smaller, by the sea Graz Austria City Graz Košice Slovakia Region Košice City Košice Most "car lovers" Town Michalovce Most cycling Town Spišská Nová Ves Longest walking distances Prague Czech Rep. District 5 & City Ravenna Italy Province Ravenna City Ravenna 2X more walking dist, 1/2 cycling Italy Town Cervia Coastal town near Ravenna Slovenia Town Velenje Figure A.5: Some overview characteristics of places and brief observations based on survey results. B. Indicator Data Inventory Indicator data is discussed by group, with each subset of data illustrated in a table at the head of its section. First is The City and its People, focusing on urban area and population. The City and its People Reference area Area of the city or town, in square kilometers (km 2). This is the area to which all other data refer to. (Project Partners were also asked to upload a map with the city/province or town borders clearly marked to BICY website under folder WP3/ [PP name].)

47 Ferrara Comacchio Ravenna Cervia Graz Erfurth Page 47/152 Košice-R Reference Area POPULATION Population density (i) Fascia 333 costiera: /km2; sulla fascia costiera2926 risiede il 72% 743degli abitanti 0 External Density (ii) Prague 5 Koper Velenje Budaörs Michalovce SNV Košice-C i. Population density by city section or population by section and section area+ map with sections ii. Population density checked by calculation and/or external secondary sources Figure B.1: Summary statistics table for The City and its People Discussion: Not all cities provided a map, and generally maps were not explicitly correlated to area calculations. During the analysis, it was determined that reference areas may be misleading due to their inconsistent relationship to the urban travel environment. Open space may comprise a large portion of the official area. Separating city from province was also difficult. Fundamentally, a key issue is the analysis of effective area, independent of jurisdictional boundaries; a boundary may be adjacent to a dense city, or an impassible mountain. There is a difference expected for purposes of estimating bicycle use. Analysis with OSM, to determine urban area and the subset of the bikeway network within that area, will help answer this question in the pursuit of more accurate methods. It may be that radial bikeways outside of urban areas are indeed strong determinates, or indicators, of cycling levels, and open space may in fact have a relationship to urban areas that is also explanative. Population by category (male, female and by age) Population by subgroup category was sought: total males, females, and those above eight years of age. From this the total population can be calculated, and the population under eight years of age can be calculated. Partners were asked to provide official 2008 population data, specifically for within reference area. If no 2008 population data is available then they were asked to provide the latest available data, preferably with an official or estimated annual growth rate. Population (male) Population (female) TOTAL POPULATION Ferrara Comacchio Ravenna Cervia Graz Erfurth Košice-R Košice-C Michalovce SNV Prague Koper Velenje Budaörs Population >8 years Pop. 8 years & under Percent Child Pop % % % % % % % % % % % % % % Figure B.2: Official indicator data on population as provided by partners. Note, Košice was unable to find child population due to the nature of the Slovakian census. Discussion:

48 Page 48/152 There was one primary data availability problem in the initial set of population questions, and other smaller ones which were resolved more quickly. The second round of population data was much more problematic, discussed in survey correction sections below, illustrating a major gap in the accessibility of official data even to professionals from or working with government in their own countries. Slovakian statistics calculate with children < 14 and only adults > 15 years old; population older than 8 cannot be calculated. It was later clarified that 15 and over were adults, 14 and below were children. Even then, checking the child percentage in the population (roughly 35%) was unusually high. That an official census would not tally age by year was also difficult to accept. This led to a more extensive UNIBO-DICAM effort to locate official data. (In the end, only country-wide data was located for Košice, where the share of children under 8 was in fact 9%, not ~17%, much closer to values of other partners). Age is an important factor in understanding overall cycling rates, behavior patterns, and injury/fatality risk. Thus, additional age strata could be helpful. For survey correction, additional age strata data were pursued (see below). Again, many partners found they could not provide this; the UNIBO-DICAM team was motivated to make the survey as valid as possible and was successfully able to find official sources in all those cases. The indicator of percent children could be used for a number of purposes. In some places with low cycling, such as the United States, children are more likely to ride than adults, and much more likely to be involved in an injury- or fatal-collision, at least in the United States.6 Higher numbers of children can also indicate differences in adult population demographics and behaviour. In the case of the survey, as mentioned, in the end surveys were only collected for those age 12 and above (requiring they be weighted with a second population dataset, discussed below). Moreover, in many Central European cities, children are not allowed to cycle, surely because of their higher risk. For example, in Graz, a child waits for the age when she or he will be allowed to take a cycle driving test and obtain a permit allowing that freedom to travel. Population density by city section or population by section and section area+ map with sections (people/km 2) The sections should be the same as those used by the population census. The number of citizens living in each sector and the area of each sector should be available from the last population census. A map with the location of the census sections was also requested,. Upload table with section name, area and population as well as map with sections on the BICY web-site under folder WP3/ [PP name] (See above table for Reference Areas.) Infrastructure Measurements: 6 In the United States, studies have found Children constitute a special risk category for bicycle safety, owing to their lesser experience and knowledge; unpredictable play, lesser motor control [Oja 2002], and lower visibility. One study found that age is a leading factor in collision initiation; with bicyclists years of age possibly responsible for 87% of daylight collisions with motor vehicles, whereas over 25 years of age, probably only 34% were responsible. [Williams 1976]

49 Total road length Exclusive Bikeways (i) Traffic Calming (ii) Pedestrian areas (iii) Bike Parking (iv) Car Parks (v) Page 49/152 Ferrara Comacchio Ravenna Cervia Graz Erfurth Košice-R N/A N/A N/A N/A N/A N/A Small Car Park (1-4 places) 600 nearby some shops 2512and 48 Košice-C N/A 8.3 businesses 134 Michalovce N/A 0 58 SNV N/A 0 60 Prague Koper No data Velenje Budaörs i. Exclusive Bikeways included paths, cycle tracks, and painted lanes. ii. Traffic Calming included any area where the speed was limited below 30 km/h iii. Pedestrian Areas were Length of pedestrian areas where bikes are allowed iv. Bike Parking: focused on parking areas, not total spaces. However, Number of bike parking or racks was the request, which turned out to be ambiguous, problematic as it could be read as total number of spaces, or the number of places with spaces, the intended meaning (see discussion, below). Figure B.3: Official data describing the transport system Total road length (km) Total length of road network in the reference area in km (excluding unpaved roads). If different data is available, partners were asked to please specify what the difference is or which roads have been excluded/included. Exclusive Bikeways length (km) Exclusive bicycle path are roads where bikes have a separated lane, either segregated by a line or by a physical separation (borders, walls, flowerpots, etc). Bidirectional bike path on a single road count only once the length of the road. Discussion: This indicator is very important. The use of the term path was potentially problematic because the term is used in transportation engineering to refer only to physically separated facilities; in fact, all types of bikeways were sought (including bicycle lanes, marked on streets, with no physical barrier of separation). Partners were notified of the clarification and all verified. In a future effort, accurately counting the length of bikeways by type would be best, along with spatial analysis of connectivity, proximity, etc., which the BICY project intends to perform, using OSM for the TSR. Length of road with traffic calming (km) Road with mixed traffic but with traffic calming are considered roads with speed limits below 30km/h, which includes zones where only walking speed is allowed. Length of pedestrian areas where bikes are allowed Only pedestrian areas where a bike is allowed to pass.

50 Page 50/152 Number of bike parking or racks Number of bike parking or public bike racks in the reference area. Discussion: This indicator was difficult for partners for several reasons. First, the question can be seen as ambiguous; what was sought was the number of bicycle parking areas (not the total number of spots). Most of all, partners either did not have this data, or found it easier to provide total bicycle parking. The reason total areas, not total individual bicycle spots, was sought, was to compare with areas for car parking as a form of spatial metric. This way, a large central bicycle parking area would not skew results if there was little provision elsewhere. Total number of Light-Rail/Local-Rail/Metro stations Discussion: Partners were asked not to count bus or tram stops, however, some partners supplied bus station data separately for possible later use. Number of Light-Rail/Local-Rail/Metro stations with bike parking Discussion: Again, partners were asked not to count bus or tram stops, however, some partners supplied bus station data separately for possible later use. Injuries and Fatalities Partners had great difficulty finding local data on bicycle injuries and fatalities. The following table is populated by a variety of types of traffic injury/fatality data. Additional data has been needed for analysis. Severe road-injuries by cars/year Road-fatalities by cars/year Ferrara Comacchio (ii) Ravenna 970 Cervia Graz (i) 3 Erfurth Košice-R Košice-C Michalovce N/A N/A SNV 225 N/A Prague Koper Velenje 94 1 Budaörs 5 Figure B.4: Discussed below. Note that many figures are for areas larger than the study area, such as regional rather than city level injury data, and/or represent all injuries, not bicycle injuries. Years also vary. i. For Graz, NOT GIVEN BUT: for the province of Styria (1,1 Mio inhabitants) where Graz is the capital ( inhabitants) in Styria we had the following number of fatalities: 2006/6, 2007/4, 2008/11, 2009/10, 2010/6 and we had following number of injuries: 2006/910, 2007/930, 2008/900, 2009/879, 2010/766 ii. Comaccio: 97 accidents with no fatalities Total number of severe road-injurious caused by cars per year A severe injury is considered an injury that required treatment at a hospital, please specify if different criteria where applied. Discussion:

51 Page 51/152 Partners found it very difficult to obtain this type of information. In some cases total injuries were found, possibly not for the city in question but for a larger area, and year varied. Clearly better data is needed. Potential sources include Eurostat and the Citizen Indicator Project (see references). I found these here where there are also many statistics on accidents: < Total number of road fatalities caused by cars per year If no information are available on the cause of the accident then please give the total number of accidents but note that the number given refers to the total. In this case we can estimate the share of accidents caused by cars. Discussion: It may be ambiguous to some whether this tally would be mode-specific or for total deaths. Again, partners reported it very difficult to obtain this type of information. In some cases total injuries were found, possibly not for the city in question but for a larger area, and year varied. Clearly better data sources are needed. Cost of New Bicycle Facilities (Lanes, Paths, and Parking) Ferrara Comacchio Marked Bikeway Cost Min Max New Bike Path Cost Min Max Cost, Park for 10 Bikes Min Max Ravenna Cervia Graz Erfurth Košice-R Košice-C Velenje Budaörs SNV Koper Michalovce Prague No data Figure B.5: Cost of both marked and separated bikeways (bicycle lanes and bicycle paths), and cost of parking for 10 Bikes, as provided by partners (in euros). Some outstanding data questions are evident (e.g., Prague and Budaörs). Construction costs of segregated bike path by markings ( /km per lane per direction) This is the cost for drawing lines, colors and signposting only. The road space is assumed to be available. If costs estimates are not available we need to estimate the costs from average costs of other partners. Discussion: It is conceivable that the conversion of cost per lane was missed by partners. Unfortunately no partner provided specifics of the projects that the costs were obtained from. Therefore we cannot consider details which would help compare between estimates. The costs may only represent a single project, and thus may not be representative of averages or typical costs. In a large scale (all at once) network installation, considerable cost savings should be expected due to efficiencies, when compared with a many individual projects approach. There were clearly errors for Prague and Budaors, corrected by assumptions later.

52 Page 52/152 Construction costs of new segregated bike path which are physically separated ( /km per lane per direction) This costs include the approximate building costs of a new, physically separated bike path in your country, either on a green field or as extra layer on an already existing road. It is anticipated that there may be large differences that depend on site-specific implementation details. Use average values if needed or give multiple values with comments on details. If costs estimates are not available we need to estimate the costs from average costs of other partners. Discussion: As above. Costs for installing one bicycle rack for 10 bikes Costs include material plus installation and signposting. If costs estimates are not available we need to estimate the costs from average costs of other partners. Data of local public transport operator From the local public transport services the following numbers are required in order to better estimate energy consumptions and emissions: Total number of passenger km per year or the total number of transported passengers per year and an estimate of average trip length Liters of fuel or cube meters of gas burned during one year for the entire bus fleet. kwh of electricity consumed (if trams or metro are part of the fleet) If possible: more detailed information on fleet composition, fuel type and respective emission standards Supplemental data The following supplemental data would be good to have for comparison, but is secondary to the calculation of the different quantities for this project. Modal split The proportional rates of travel by mode (modal split), including cycling, taken from recent census data or surveys is sought. Apart from cycling, any other set of modes or trip purposes are useful. Bicycle Pedestrian Bus Train Car Car+Motorbike Motorbike Pub Transport Ferrara Comacchio Ravenna Cervia Graz Erfurth Erfurt Erfurt Košice-R Košice-C 0.03 Michalovce 0.06 SNV Erfurt 2: 2003 data Prague Velenje Koper Budaörs Figure B.6: Modal split data (as provided by partners; very incomplete and some are admittedly guesstimates.)

53 Page 53/152 Discussion: The modal split is an extremely useful indicator data for comparing places. Unfortunately, it is typically one of the hardest to obtain throughout the world, and there are often difficulties with and differences in the process of its collection. Graz and Erfurt provided modal split estimates from representative mobility surveys. There are still differences in the detail, for example how to count a cyclist who is not riding bike during the winter. Ferrara s modal split is based on non representative phone interviews. It was hoped that the survey would correct for this, but unfortunately Ferrara didn t report the survey sources so corrections for this type of error aren t possible (see Survey Methodology Annexes). Although some estimates have been made, official modal splits for Velenje, Košice, Koper, Prague and Budaörs were not available throughout the process because they have actually never been determined (either from a census or other studies). Some partner figures were estimated, literally by guessing, demonstrating the larger need for and importance of the contribution of the BICY Project Mobility Survey. Modal split by km Km driven by each mode per day per person. Discussion: No partner was able to provide this. Distance-based modal splits are the most standard figure, although they may do a disservice in under-representing the utility and actual percent use of modes which are used for shorter distances, but potentially more trips (e.g., cycling and walking). The definition here asked for even more detail than a typical modal split, where only percentages are requested. Transport related energy consumption or emissions (CO2, and any others, in MJ per person per day or grams of emissions per person per day) No partner was able to provide this. Despite its importance, this is a quantity that is usually not available. Partners were asked to provide if possible and if so, to please provide also the methods with which it has been calculated. This data can be useful in better understanding a variety of aspects of an area which may affect cycling rates. Air pollution can discourage cycling. Energy consumption may help indicate local efficiences and other effects which could be supportive or discouraging to cycling, particularly if tied to economic activity. Differences in vehicle fleets (their emissions standards, age, fuel efficiency, etc.), cargo/freight information, and more would also be potentially useful. Additional data requested: Population of Age Categories for Survey Correction In error-checking and validating the survey, as discussed elsewhere in this report, it was seen that the proportions of survey respondents in each of the demographic categories were different from their true proportions in the population. In some cases these differences were very large, which threatens the validity of the survey. Obtaining true age category shares was thus essential to survey adjustment.

54 Page 54/152 Partners were thus asked for survey age category population data for the purpose of survey correction, and additional data regarding parking characteristics. Survey age categories were: Minor, ages Adult, ages Senior, ages 60+ This second round of population data was much more problematic for partners, illustrating a major gap in the accessibility of official EU country data even to professionals from or working with their own governments, in their own countries. Although many partners had great difficulties finding population data on their own countries. UNIBO-DICAM was able to locate official sources of census data and process it in order to obtain population by survey age categories. This was possible thanks to global search engines. Using search queries in the Partners native language, and machine translators to analyze the results and explore official websites, census data was obtained even where partners were unable. The final survey population age category share data obtained is detailed in the following table: Population Minors ages12-16 (<17) Adults ages Population >60 Population (male) Population (female) Population >8 years Population 8 years and below Population >12 Share interviewed (per 1000) Car ownership/1000 country 2009 Share children 8 years and below share of males share of females share of minors(12-16) share of adults(17-59) share of elderly (60 and over) share of car-ownership (2009) Population province Carownership province Ferrara Comacchio Ravenna Cervia , ,672 Graz Erfurth Košice-C Michalovce SNV Prague Koper Velenje Budaörs Figure B.7: demographic shares of males, females, minors (age 12-16), adults (ages 17-59) and seniors (ages 60+) for correction of survey response, from official population data found by research. Car ownership and provincial population was also found. Only Velenje, Budaörs and Erfurt age group data were provided by partners (3/13 places), although some partners provided incorrect data. Sources used to locate, collect, and process age category population data: Košice: only country-level census data by age was located.7 Koper: corrections to estimates successful8 7 Košice: 8 Koper: the partner provided estimates which were very round numbers and didn t match expectations, so further research was done. Partner data differed, particularly for minors (3.96% not 10%).

55 Page 55/152 Prague: found and calculated using citywide population data (not specific to District 5), Italian Partner Place/Area data was available from ISTAT.10 Graz was able to provide a dynamic population tree (animated image) from which data was recorded and evaluated11 Car ownership in the EU was found thanks to the European Environmental Agency (EEA 2007); another EEA source was also considered.12 In a number of cases, age categories were found for a larger area (e.g., Prague and Košice cities and region). In some cases, age categories provided did not match, and had to be calculated based on the assumption of proportional annual shares across two or more years. Completeness of data from project partners: As discussed in some detail above, for each type of data requested, project partners have supplied much of the requested data. Reporting has now been received from all partners. As discussed above under Discussion for each data element requested, and as illustrated below, the task of assembling Indicator Data from official sources has been anything but simple, and remains incomplete. Nevertheless, this dataset represents a huge advance. Some incompatibilities existed between some types of data, for instance: Age data is not always available to identify age categories. E.g., for those above and below 8 years of age. For Budaörs, data is only given for those under 8, in For Slovakia, the data splits at age 15. Severe road injuries and fatalities data: some is missing, and most is inconsistent (differing in area, year, and/or which modes are represented). Construction costs vary by several orders of magnitude, needs to be harmonized/verified. No resolution after one year, no details provided to better understand the estimates. Some gave helpful ranges (average, maximum and minimum costs). Roadway length is being generated again by a unified mapping approach using OpenStreetMap In general all partners have some missing or incompatible data. After many iterations of requests, many issues not listed here were resolved (e.g., Ravenna/Cervia length data was specified as area not length, by units of mq (meters squared)). However, some questions for verification remained, in addition to the above, including: For Košice, there were very high numbers of people 8 years and below (approximately 35% for each city, but only 18.43% for the region as a whole) Population density appears unreliable because the areas given do not well represent urban area (particularly in the cases of the Italian provinces; e.g., Ravenna would be the size of Paris) umber_population/20_05c40_population_obcine/&lang=1 9 Prague: Czech Statistical Office Italy:ISTAT, Graz graph.

56 Page 56/152 New age categories were needed for the survey, which required a special research effort, detailed in the survey adjustments Annex. Apparently there were differences in the amount of data provided by the partners and it is important to note that no partner has been been able to provide all data requested. As mentioned above, some of the data has never been collected, surveyed or calculated by some of the partners. On the plus side, some partners provided additional data in a number of cases. For example, sometimes a range was provided; in these cases, maximum and minimum fields were added, and an average calculated for primary cross-comparisons with other partners. Examples of partners providing additional data include: Koper provided data on additional regional rail stations, and split roadway data by local and state Some partners provided bus station information in addition to that for rail stations Regarding the verification of data and sourcing: In many cases, data sources were not provided by partners. In all cases unless otherwise specified, it is assumed that Partners, or DICAM, used the best available official data sources. The process of obtaining the data was extensive, spanning more than a year, with many follow-ups. Provided data was scrutinized and given reality checks including cross-indicator verification calculations; consideration for consistency with our understanding of urban planning and transport; along with comparison for consistency with other partner data and other places. Wherever there were questions, or clearly problems, partners were asked to review their sources and verify or update their data. WORKING KEY: The following table helps illustrate the data collection effort. PROBLEM QUESTION MISSING CALCULATED MORE INFO GIVEN THAN ASKED FOR Official name of city under investigation Reference Area Population (male) Population (female) CHECK TOTAL POPULATION Budaörs Erfurt Ferrara Ferr'a MAX Citta di Com Comacch MAX Košice REGION Spišská Nová Michalo vce Košice Koper KOP MAX Prague RAVENN Comune A di Cervia Velenje VEL MAX Unit km2 37,148 75,982 13,639 16,636 Persons 41,890 80,015 14,903 16,539 Persons ,500 Population >8 years , ,951 26,458 Population 8 years and below % 93.62% 5.52% 6.12% 8.75% 18.43% 35.41% 33.53% 36.54% 7.27% 10.70% 9.00% 7.30% 11.08% N/A km 120 N/A km 800 N/A N/A N/A N/A km 3.5 N/A km x CHILD POPULATION PERCENTAGE Population density by city section or population by section and section area+ map CHECK POPULATION DENSITY Total road length Exclusive Bicycle path length Length of road with traffic calming Length of pedestrian areas where bikes are allowed Number of bike parking or racks N/A N/A Small Car Park ( Graz MIN MAX GRAZ NUMBER OF CAR PARKS CHECK 600m Total number of Light-Rail/Local-Rail/Metro stations Number of Light-Rail/Local-Rail/Metro stations with bike parking Total number of severe road-injurious caused by cars per year Total number of road fatalities caused by cars per year Construction costs of segregated bike path by markings Construction costs of new segregated bike path which are physically separated , accidents with no , N/A N/A N/A 2 NOT GIVEN BUT: for N/A N/A N/A No data N/A N/A N/A N/A No data : Koper railway 1 station is Costs for installing one bicycle rack for , bikes Data of local public transport operator Urban 98,9 Calculated on the basis of 2004 data: Public transport person/day in two directions Car Various traffic: person/day in two directions. Petro use in 1 year: no data Various network: Mio. PT- State Roads: 145,300 4 NO DATA IS AVAILABLE x N/A Fascia costiera: CoastalKarst region, CoastalKarst region, , , Total number /km2 800 Various Various /km per lane per direction /km per lane per direction 5000 Various Figure B.8: A recent iteration of a table used in assembling answers to the original non-supplemental Indicator Data request. Over a year s time, hundreds of communications, and supplemental research, achieved this state of completion.

57 Page 57/152 Additional Data Inquiries for Consideration: Although not requested from project partners, additional types of data continue to be sought. Many things can influence cycling levels. Fluctuations in fuel prices, or transit fares, for example, have been known to quickly increase or decrease the numbers on bicycle. Topography: A flat city is ideal for cycling, and the top cycling cities of the world are typically very flat (Amsterdam and Copenhagen, for example; Shanghai and Guadalajara, to add a few outside Europe). However, hills do not preclude cycling; consider San Francisco, California, in the United States. It is now one of the top cycling cities, in large part because the people built a culture of cycling, and a political body, and demanded it. (See discussion below, in Factors Affecting Cycling: Topography.) BICY Partner Places exhibit a range of topography, from flat to hilly. Weather patterns: Weather has a major influence. Heat may actually be more of a deterrent than cold, however. Rain is not prohibitive, although type of rain may be; harder, infrequent rains may discourage cycling more than light, constant rains. (Again the examples of Copenhagen and Amsterdam; and in North America, Portland, Oregon, USA.) Weather can be quantified in many ways: number of days of rain or sunshine; cloud cover; intensity of rain, snow or sunshine; characteristics of season; average, high and low temperatures; etc. Political and cultural considerations: Many inquiries could be made into the sociopolitical variations in cycling. How does culture affect the propensity to build a culture of cycling? Political system and citizen power of participation? Religious mores? Sense of worth, in regard to material possessions and other signs of social status? How is a cyclist likely to be treated on the road, and off the road? These are all worthy of consideration, although quite open topics which may be difficult to assess and compare, particularly from afar. Religious and other cultural inquiries, in particular, might also be sensitive topics. Anecdotally it has been suggested in the field of bicycle research that cycling rates tend to be higher in predominately Protestant countries, and lower in predominately Catholic countries. 13. Additional Sources of Modal Share 13 Then there are those cyclists so zealously committed that they are referred to as religious cyclists (or born again bicyclists ) by some, in some cases with derogatory intent. The name Bike Church has been used in places to describe community-based bicycle repair centers.

58 Page 58/152 A related project of indicator collection which can be useful in the BICY process, although the data is older, having been primarily benchmarked from the year 2000 and released in 2006, is the source of data, is the Citizen s Network Benchmarking Initiative (EC, 2006), which sought to collect urban transport indicators, including those for cycling. A variety of compendiums of modal share exist (usually older by 8-12 years, and not for all partner places). Wikipedia: For 2009 there is the However it says it is incomplete and Refers to EPOMM has an online resource with hundreds of modal splits, although sources are not clear. Perhaps the data is available in spreadsheet form. These can be used to further test conclusions here across a wider array of places, with additional data sources for indicator data. The data provided by the mobility survey allows many views into travel behavior and its relationships to the travel environment, as well as the individuals themselves. The range of questions allows many relationships to be explored. These exact questions in the English version of the survey are shown on the following page. The first pass relationships extracted from raw survey data are detailed in the below survey data inventory, which follows the survey questions. Extraction and analysis of additional survey data is intended to be obtained and utilized in further refinement of the projections by Common Indicator relationships, and so should be valuable for the Transational Strategy Report.

59 Page 59/152 C. OpenStreetMap The OpenStreetMap effort was conducted at UNIBO-DICAM by Anton Pashkevich under the direction of Joerg Schweizer with participation and assistance from Jason N. Meggs. The following slides were prepared by Anton Pashkevich, for a presentation describing the nature of the data used and the methodology by which it was imported and utilized for BICY WP3, and are included here to serve as an introduction to its use here. Figure C.1: Example map produced in the online OSM interface. As evident in the image, OSM can be quite similar to google maps or other commercially-sponsored services, however there are major differences, most notably that the content is user-developed, and it is freely available for use. OSM data is made up of XML records which can be downloaded and processed independently.

60 Page 60/152 Figure C..2: Underlying XML data which generates OSM maps and in the case of BICY, summary data and further analyses. Figure C.3.

61 Page 61/152 The XML data describes elements: first there are nodes, single points on the map. Between two or more of these, a relation can be drawn, creating a way (a chain of nodes that represents something like a street, a river, a topography line, or a building outline). If the way loops back on itself it is a closed way or area. Figure C.4.

62 Page 62/152 Figure C.5. Figure C.6.

63 Page 63/152 Figure C.7. Figure C.8.

64 Page 64/152 Figure C.9.

65 Page 65/152 Figure C.10. Figure C.11.

66 Page 66/152 Figure C.12. OpenStreetMap Framework OpenStreetMap, or OSM, is an open-source geospatial database developed by hundreds of participants around the globe. The benefits of OSM for the BICY project include the fact that it is freely available, without restrictions on use, and its content is improving on a daily basis. OSM has enabled a method of cross-checking of indicator data such as length of bikeways and length of roadways; in the future we aim to conduct spatial analyses including combined zonal and network analysis to inform policy analysis and proposal of an optimal future cycle network for each Partner Place. The OpenStreetMap software developed by UNIBO-DICAM continues to evolve, however results are available for this report. It is expected that the groundwork laid for this report will contribute substantially to the findings in the Transnational Strategy Report.

67 Page 67/152 Future work includes a network analysis evincing much deeper insight to the functionality of cycling infrastructure. Measures like connectivity, accessibility, and the temporal competitiveness of the cycling network with respect to the road network, can be determined. Figure C.13: a view of part of the center of Bologna, one of the many visualizations possible OSM Methodology The methods used for this report have focused on obtaining data describing roads and cycling facilities, leading to a comparative analysis with the similar data received from partners. Development Environment The software was developed in a Linux environment using Python 2.5.2, an open source language and development environment with sophisticated extensible and customizable modules available. Principal modules utilized include MatPlotLib, and NetworkX, which allows the generation of visual maps such as the one pictured below (Figure). In-house Python modules developed by UNIBO-DICAM include OpenStreetLib.

68 Page 68/152 Figure C.14: visualization of example geographic extract from OpenStreetMaps Pieces of the Puzzle Four graphs and three maps are produced in the process of generating the highways data. The graphs are used to make the maps. They represent different sets of information that can be used to make a map. They could also be called layers. The layers, when combined, offer a more complete map. The graphs are also diagnostic tools, used to create maps for visual review at checkpoints in the process, in order to monitor the accuracy of the process. After the data is obtained, many more graphs are made, one for each type of highway. Data Challenges OSM presently restricts the amount of data that can be downloaded at one time: approximately 10,000 nodes. There is also a daily limit. Because we are downloading large portions of European cities and regions, unfortunately it was not possible to download Partner Places in one piece. A workaround was developed whereby smaller portions were downloaded, then recombined. Ironically, this means downloading much more data than needed in the end, because any long chain of connected dots (e.g., a road, known technically as a way), that crosses the small box is downloaded in full thus many roads are downloaded many times, rather than just once. Obviously this also created extra work for processing. Overview of Steps

69 Page 69/152 A basic description of the higher-level sequence of steps in the processing of data to obtain highways data is as follows: 1. Obtain the data: a. Designate a box encompassing the study area b. Divide the study area into the smaller boxes that OSM allows c. Filter out all unnecessary tags and attributes d. Exclude all invisible nodes (typically less than 1%) 2. Combine the data from the many small boxes into one piece of data for graphing (a graph is a data file, not yet an image, although it is used to draw maps representing the graph) a. Ways (long chain of segments, e.g., drawing a road) must be cut into single segments i. Assign all tags and attributes of the Mother Way to the new short segments 3. Create Graph 1: comprised of all nodes a. This includes trees, buildings, everything described as a point in space b. Save for next 4. Create Graph 2: comprised of all the ways a. This is made of the minimized segments created above, just two points and an edge) b. Save for next use as picklefile (Python-specific *.dat file, small and fast) 5. Create Map 1: comprised of Graphs 1 and 2. a. Review visually for accuracy 6. Create Graph 3: official administrative boundary a. This will be used as the cutting edge to find only the highways inside the area 7. Create Map 2: all roads and the administrative boundary (Graphs 2 and 3) 8. Create Graph 4: All roads in bound a. Program generates a center, draws a line to each segment. i. If the line drawn crosses the administrative boundary, it is kept in Graph 4 ii. Otherwise, it is discarded (this loses less than half a percent of information) b. Manual review: did a single center work? Boundaries are often not simple enough. i. Multiple centers are made manually as necessary ii. All subgraphs created (one run per center) are combined into master Graph 4 9. Create Map 3: All highways within administrative boundary (Graph 3 + Graph 4) 10. Calculate totals of all ways of interest (e.g., roads, cycleways, etc.) 11. Divide highways into many layers (Graphs), one per type of road 12. Create Map 4: all highways of interest combined, plotted by color code NEXT STEPS: 1. Function in development for calculation of area within official boundary 2. Spatial Analysis: future goals include a. Identify urban areas b. Identify zones within urban area, e.g., i. Shopping districts ii. Industrial areas iii. Sleeping areas (quiet residential)

70 Page 70/152 c. Network analysis i. Quality of existing bike network ii. Generation of various ideal future networks 1. Based on existing bikeways 2. Envisioned freshly without considering existing bikeways 3. Manual review by stakeholders and improvement of the model 3. Setting policy course through the tools and insights garnered above Example Subset of Highways Data An example of summary data which is culled after the above process has run, is provided in the below table (Figure). Road attributes: amount of highway motorway [meters] amount of highway motorway_link [meters] amount of highway primary [meters] amount of highway primary_link [meters] amount of highway secondary [meters] amount of highway tertiary [meters] amount of highway residential [meters] amount of highway unclassified [meters] amount of highway road [meters] amount of highway living_street [meters] amount of highway service [meters] amount of highway track [meters] amount of highway footway [meters] amount of highway path [meters] amount of highway steps [meters] amount of highway platform [meters] amount of highway pedestrian [meters] Other attributes: amount of highway footway [meters] amount of highway path [meters] amount of highway steps [meters] amount of highway platform [meters] amount of highway pedestrian [meters] Attribute "Cycleway": amount of highway cycleway [meters] Ways allowed for bicycles: amount of footway: bicycle - yes [meters] Control of previous results: amount of bicycle - yes [meters] Figure C.15: portion of data from an example geographic extract from OpenStreetMaps

71 Page 71/152 Figure C.16: OpenStreetMaps data was used to produce diagnostic maps, Map 2 (above) and Map 3 (below) for Budaörs. Map 3 shows only a subset of highways within the administrative boundary.

72 Page 72/152 OSM Analysis As a result of this extensive effort, a diversity of summary data for highways (including bikeways) has been obtained and compared with partner data. This data is then available for the analyses in this report and for the further sophistication and use in the Transnational Strategy Report. COMBINED MANUAL AND OFFICIAL INDICATORS Official Data Urban Area (km2) Bikeways (km) Paved Roads (km) Population OSM Results Total Roads (km) Bikeways km OSM v. Official (%) Paved Roads (km) Bikeways km Ferrara Comacchio Ravenna Cervia Graz Erfurt Košice-C Michalovce SNV Prague Koper Velenje Budaörs Ferrara Comacchio Ravenna Cervia Graz Erfurt Košice-C Michalovce SNV Prague Koper Velenje Budaörs Ferrara Comacchio Ravenna Cervia Graz Erfurt Košice-C Michalovce SNV Prague 5 Koper Velenje Budaörs % 26.95% 72.65% % % % % % % % 41.08% 88.86% % % 12.15% 67.32% % % 88.52% % % 20.36% 45.95% Figure C.17: Preliminary comparison of summary data between OpenStreetMaps and official data provided by partners. Comparison of percent (OSM/Official) in bottom rows. This data must be qualified to be best understood. In some cases the data seems to better fit the models (e.g., the 64% larger value of bikeways length, for Ferrara). In contrast, for Comacchio, which is in the Province of Ferrara, the eight-fold difference is extreme. However, this is explained by the fact that there is confusion sometimes between a city and its larger municipal boundary; Comacchio is both a city and a comune, which includes a larger area encompassing other small cities. This problem of matching urban boundaries to Partner Places is also seen when calculating urban area and urban densities. It has become clear that we must calculate our own areas in order to have an apples and apples comparison. OSM tools are in development for this purpose (see discussion for Network Indicator). Comparing OSM Results Given a subsequent calculation of OSM streets and bicycle facilities (after the above), calculated from boxes drawn around central urban areas, the following results were generated. These are also then compared with official data in the below table (Figure C.18). The three types of Standard Indicators (NCI for Network Coverage Index; NDI for Network Density Index; and CI for Cycling Index) are calculated for each of three sets of bikeways (official, then cycleways plus pedestrian ways allowing bikes ( yes or designated ), then the preceding plus all yes and designated segments). See Figure C.18, below. R-squared values are generated for each. Despite the uncertain nature of this set of preliminary OSM data, a positive relationship is seen for all nine different trials, most of which are strong (see Figure C.19, below). Thus despite the problems with the OSM method at this time, the results are promising.

73 Page 73/152 STANDARD INDICATORS: OSM and Official Data Calculated & Compared Total Roads Cycleway Ped/path + bikes (km) Bikes allowed (incl. cycleways) Cycleway + ped/path Cycleway+ Bikes Allowed Ferrara Comacchio Ravenna Cervia Graz Erfurt Košice-C Michalovce CALCULATE STANDARD INDICATORS WITH EACH OF 3 KINDS OF SOURCE DATA Ferrara Comacchio Ravenna Cervia NCI official bways E-05 NCI CYC+PED E E NCI CYC+YES E E Ferrara Comacchio Ravenna Cervia NDI official bways NDI CYC+PED NDI CYC+YES Ferrara Comacchio Ravenna Cervia CI official bways CI CYC+PED CI CYC+YES COMPARE OSM TO OFFICIAL BIKEWAYS Ferrara OFF v CYC+PED OFF v CYC+YES Comacchio Ravenna Cervia 65.27% 55.42% % % % % 45.50% 44.50% COMPARE OSM TO HAND-GENERATED DATA ALSO FROM OSM Ferrara Comacchio Ravenna Cervia OSM road v. by hand OSM CYC+PED v. hand OSM CYC+YES v. hand 83.98% % % Graz 4.99E E Graz Graz Erfurt Erfurt Erfurt Košice-C Michalovce 1E E E E E E-06 Košice-C Michalovce Košice-C Michalovce 3.08E E E E E E-05 SNV Prague SNV 4.77E E E-05 SNV SNV E Prague E Prague Prague E Koper Velenje Budaörs Koper 4.93E Koper Koper Velenje 6.74E E E-05 Velenje Velenje Budaörs 1.4E E E-05 Budaörs Budaörs % 42.32% Erfurt 2 Košice-C Michalovce SNV Prague 5 Koper Velenje Budaörs 73.88% 52.04% % % 8.71% 27.43% 89.22% 88.78% 43.91% 46.99% % % 6.63% 20.87% 89.22% 88.78% Graz Erfurt 2 Košice-C Michalovce Graz 76.54% 98.71% % % % % % % % SNV Prague 5 Koper Velenje Budaörs % % % % Figure C.18: Preliminary comparison of summary data between OpenStreetMaps and official data provided by partners. The three types of Standard Indicators (NCI for Network Coverage Index; NDI for Network Density Index; and CI for Cycling Index) are calculated for each of three sets of bikeways (official, then cycleways plus pedestrian ways allowing bikes ( yes or designated ), then the preceding plus all yes and designated segments). Roadway data was generated by OSM from manually drawn boxes around urban centers. Area was taken from estimated subsections of the boxes, to approximate the true urban area. R-SQUARED NCI official bways NCI CYC+PED NCI CYC+YES NDI official bways NDI CYC+PED NDI CYC+YES CI official bways CI CYC+PED CI CYC+YES Figure C.19: Table depicting R2 values for bicycle mode share(time based) v. the three Standard Indicators, given three different sets of data (official; cycleways + pedestrian and path facilties allowing bikes; finally, cycleways plus all facilities allowing bikes.

74 Page 74/152 D. Survey Data Inventory As with the indicator data, in the case of the survey, a host of inquiries has resulted in groups of data available for analysis. Survey data exists in numerous interdependent files and is accessed by extraction using coded queries in Python programs. To print and summarise all data is beyond the scope of this report. Further analysis will be completed in pursuit of the final revision of the Transnational Strategy Report (WP3.4). A meaningful subset of survey data is presented in these Annexes, along with observations and the first round of analyses. Participant Demographics Demographic representations (% of survey respondents) were calculated for the following groups: o o o o o Share of males Share of females Share of minors (ages 12-16) Share of adults (ages 17-59) Share of elderly (ages 60+) Modal Splits Modal Split (maximum distance): Modal split based on the mode that is used regularly ( typical work day ) for any purpose. If multiple modes are used then the mode with the longest (estimated) distance will be taken into account. o o o o o Modal split (maximum distance) for Walking Modal split (maximum distance) for Automobile Modal split (maximum distance) for Bicycle Modal split (maximum distance) for Public Transport Modal split (maximum distance) for Motor Bike This distance-based modal split is calculated from the times reported in the survey. Calculating distance based on time required assumptions as to average speeds: Auto: 25 km/hr Motorbike: 20 km/hr Public Transport: 10 km/hr Bicycling: 12 km/hr Walking: 3.5 km/hr

75 Page 75/152 Modal Split (maximum time): Modal split based on the mode that is used regularly ( typical work day ) for any purpose. If multiple modes are used then the mode with the longest indicated trip time is chosen. This is the source data for all modal split calculations (survey respondents reported daily average travel times). o o o o o Modal split (maximum time) for Walking Modal split (maximum time) for Automobile Modal split (maximum time) for Bicycle Modal split (maximum time) for Public Transport Modal split (maximum time) for Motor Bike Multi-Modal Split: This modal split takes also into account the combination of the major modes. o o o o o o o o o Multi-modal split for Walking Multi-modal split for Motor Bike Multi-modal split for Public Transport And Auto Multi-modal split for Bike And Auto Multi-modal split for Bike And Public Transport Multi-modal split for Automobile Multi-modal split for Public Transport Multi-modal split for Any Other Combination Multi-modal split for Bicycle Cyclists Routine Experience Experience of Regular Cyclists: Regular cyclists, or regular bikers, are considered: (1) interviewees who reported that they travel by bicycle regularly; or, (2) interviewees who use their bike irregularly but for the purpose of work/study or for shopping and they have provided trip time information regarding their bike travel. For this special class of survey respondent, a host of questions detail their daily experience, including route quality; parking availability; traffic collisions, fear of collisions, and respect on the road; bicycle theft and fear of theft; use of public transport and bike and ride parking; experience with hills ( tiring grades ); and experience with weather (cold and rain): o Share of Regular Cyclists Daily cycling environment: o Good Bicycle Route is continuous (this includes traffic calmed/30-zone areas)

76 o o o o o Page 76/152 Good Bicycle Route is discontinuuos Existence of secure bicycle parking Entire bicycle path is illuminated Use of bike and ride parking Carry bicycle inside public transport Experiences: o Bicycle experience with survived accident o Bicycle experience with fear of an accident o Bike respected by car drivers o Bicycle experience with theft of bicycles o Bicycle experience with fear of bicycle theft o Bicycle experience with tiring grades o Bicycle experience with moderate rain o Bicycle experience with temperatures less than 10 C Conditions for Adopting Cycling Cyclists, Potential Cyclists, and Car Lovers : o share of male being biker share of female being biker o share of minors being biker o seniors o share of car lovers Car-lovers are considered person with a particularly strong attachment to their car. Car-lovers are unlikely to give up their car. Here we consider car-lovers as persons who satisfy simultaneously the following criteria: (1) use of the car everyday, (2) uses the car for all purposes (work study and shopping or other purposes) (3) never uses public transport, (4) uses the bike never or sometimes for other purpose than work and shopping (most likely recreation), (5) will never change to ride bike and (6) will under no circumstances change to public transport. o share of potential bikers This is the share all people who currently do not use regularly the bike as major mode for any purpose, but who could use the bike for the distance they do every day. For car and motorbike divers we assume that all trips up to 30 minutes per day (approximately 2x6km in urban environments) can be replaced by a bicycle trip. For public transport users we assume that all trips with a dayly trip-time of up to 30 minutes can be replaced by bike trips. All walkers are potential bikers. Creating Cyclists: self-reported requirements for cycling: What if? Here, survey respondents selected conditions they require to begin cycle. (This was also asked of current cyclists, in which case the responses would indicate their requirements to remain cyclists.) These are the shares of interviewees who currently do not use bicycling as a major mode but who state that they would, if at least the following condition is satisfied, e.g., "Required is a continuous bicycle path":

77 Page 77/152 o Share of non-regular bike user who would use the bike if at least:required is a continuous bicycle path o Share of non-regular bike user who would use the bike if at least:required is a safe bicycle parking o Share of non-regular bike user who would use the bike if at least:required is a bicycle path with rain, wind and sun protection o Share of non-regular bike user who would use the bike if at least:required are bikes and ride opportunities o Share of non-regular bike user who would use the bike if at least:required is the possibility to bring bicycles inside buses/trains/trams o Share of non-regular bike user who would use the bike if at least:required are bicycles for hire opporunities o Share of non-regular bike user who would use the bike if at least:required is a bicycle with electric support o Share of non-regular bike user who refuse to use the bike New Bicycle Use Scenarios Effect on Bicycle Modal Split of Interventions: Projecting future bicycle use, based on What if? survey responses, above, regarding the individual potential cyclists self-reported conditions necessary to begin cycling for transportation. These are then applied to three scenarios of intervention, in which the cycling environment is upgraded to induce more cycling. Scenarios are described below, in the section on survey analysis and projections. Data projections then produce a modal split (based on maximum distance) for each mode: Scenario 1: o Scenario 1: Modal split Walking o Scenario 1: Modal split Automobile o Scenario 1: Modal split Bicycle o Scenario 1: Modal split Public Transport o Scenario 1: Modal split Motor Bike Scenario 2: o Scenario 2: Modal split Walking o Scenario 2: Modal split Automobile o Scenario 2: Modal split Bicycle o Scenario 2: Modal split Public Transport o Scenario 2: Modal split Motor Bike Scenario 3: o Scenario 3: Modal split Walking o Scenario 3: Modal split Automobile o Scenario 3: Modal split Bicycle o Scenario 3: Modal split Public Transport o Scenario 3: Modal split Motor Bike

78 Page 78/152 New Cyclists Carbon Reduction Estimates Effect on Carbon Emissions as a result of Interventions: Following from the above projections of future bicycle use, carbon emissions reductions are calculated for each of the three scenarios of intervention, in which the cycling environment is upgraded to induce more cycling. o Scenario 1: CO2 reduction ratio o Scenario 2: CO2 reduction ratio o Scenario 3: CO2 reduction ratio Conditions for Adopting Public Transport Use o share of potential public transport users o Share of non-regular public transport user who would use the public transport if at least:required is a stop within 5 min walking distance o Share of non-regular public transport user who would use the public transport if at least:required are clean vehicles with air conditioning if needed o Share of non-regular public transport user who would use the public transport if at least:required is a service at least each 5 min o Share of non-regular public transport user who would use the public transport if at least:required is a direct service, without transfers o Share of non-regular public transport user who would use the public transport if at least:required is always a place to sit o Share of non-regular public transport user who refuse to use the public transport New Public Transport Use Scenarios For each PT scenario (here, Scenario PT1), values were generated: o Scenario PT1: Modal split Walking o Scenario PT1: Modal split Automobile o Scenario PT1: Modal split Bicycle o Scenario PT1: Modal split Public Transport o Scenario PT1: Modal split Motor Bike Additional Survey Results In the process of survey validation and correction, the following data was used, then compared with the real share based on official data sources, and then a corrective weight was provided to apply to all survey results. Survey population: o Share of males

79 o o o o o Page 79/152 Share of females Share of minors Share of adults Share of elderly Share of car owners NOTE: Car owners were not explicitly identified by survey question. Rather their share was calculated using survey data as to regular travel. Someone who used a car for everyday travel, as the driver, was presumed to be the owner. These place-based survey respondent shares were compared to national per-capita data regarding ownership of cars, to further generate survey correction weighting. Real population: o Real share of males o Real share of females o Real share of minors o Real share of adults o Real share of elderly o Real share of car owners Weights generated for survey correction: o Relative weight of males o Relative weight of females o Relative weight of minors o Relative weight of adults o Relative weight of elderly o Relative weight of car owner o adults going to work/study o adults going regularly to work/study o adults using bike to work/study o adults using bike regularly to work/study o time adults riding bike per workday o dist adults riding bike per workday o time adults riding bike per workday no compile o dist adults riding bike per workday no compile

80 Page 80/152 E. Survey Methodology Standardized Approach A detailed methodology guide was given to all partners in an attempt to achieve consistent and highly credible results. This English-language guide, including an English version of the survey, is reproduced in the Annexes, as is a section discussing adjustments made for potential sources of error. The goal for each Partner Area was 1500 total surveys each, to maximize the accuracy of the findings. Smaller places were allowed lower response. Locations Surveys were conducted in a variety of geographical locations which attract a broad cross section of people (e.g., centers). The survey was offered as an intercept (street) survey. An example tabulation of a survey history is provided below for areas in the Košice region. Figure E.1: An example tabulation of a survey history for areas in the Košice region (source, page 12, Košice report). Processing Method A computerized method of scanning each survey and tabulating all results into electronic form was created by DICAM-UniBo. The system was created in Linux using Python.

81 Page 81/152 Raw surveys were processed by scanning and computer processing using SurveyMaster open source software tailored to this survey by UNIBO-DICAM. The method afforded a high level of ability to impute and error-check hand-written responses. Survey results were then further processed by original software written in Python in a Unix environment by the UNIBO-DICAM team. Algorithms could thus be adjusted and specially tailored, and data assembled in myriad ways for analysis. Assumptions and algorithms are detailed below in the data analysis section. Methodology Manual Partners were provided the following detailed Mobility Survey Manual to ensure consistency of surveys. Nota bene that the English version of the survey contained within is not entirely accurate to the actual surveys that were used (in five other languages). In particular, there is language that researchers of cycling would find problematic, such as regards bike paths which are distinct from bike lanes, for instance; here the intended meaning was regular route (travel path). Fortunately, these issues were not replicated in the translated surveys, and moreover the surveys were administered by trained representatives whose job was to ensure consistency and understanding as detailed below in the training manual. The Mobility Survey Manual was provided as follows: Mobility Survey Manual Bicy working paper for Task 3.2 Common Indicators Revision 01 : 15/06/2010 by Joerg Schweizer Revision 02 : 02/10/2010 by Joerg Schweizer Revision 03 : 06/10/2010 by Joerg Schweizer The manual is organized in steps that need to be undertaken by the PP for a successful conclusion of the mobility survey. Note that steps 1+2 (localization and questionnaire evaluation) can be performed in parallel with steps 3+4 (choice of sites and preparation of event). In other words: steps 1 and 2 can be started immediately! The manual is based on the document Specification of data collection scheme, which explains why we need the different data from the questionnaire and why it should be implemented as street survey. And refers to the following documents, which are all provided on the bicy web-site, download area under WP3/Unibo: The questionnaire in different languages (in Exel format) Data collection form (in Exel format) A cost calculator (in Exel format) Localization of questionnaires Each PP needs to localize the questionnaire. Localization means that language and some other data of the questionnaire need to be adapted to the country and city in which the PP has foreseen the interventions of this project. Unibo can help you with the localization. The more data is provided in the Data collection form,

82 Page 82/152 the better Unibo can perform the localization for the PP. Please return the localized form to Unibo prior to proceed with step 3. The following steps are required to adapt the questionnaire to the local country/city: 4. Please translate questionnaire if not available for your language (currently available: English, Italian, German, Polish). Pay attention to the exact meaning of the questions. Ask yourself whether people would give the same answer hearing the question in their native language then you would reading the English question. 5. Distance home residence: Please insert the name of the city and possibly the place/street which is considered the center. 6. Adapt the residence block of the survey (top left block, see Fig. 2, Section 5). First you need to fill in the city where you intend to do the survey. Then the distances city center-residence need to be adjusted. Unibo can make you a suggestion, if the census sections and corresponding population is available to us (see Data collection form). The aim of choosing these distances is to reconstruct the urban density where people live. This can be done only in a very rough manner, but it is necessary in order to make many quantities more comparable between different cities. Some rules of thumb could be the following: First distance should be the approximate radius of the inner most city, characterized by the highest population density. This corresponds to the historic center or central business district. In most cases it will also correspond to the central section used by the population census. The maximum distance should correspond roughly to the radius of the city borders. The rest of the distances should be an equally spaced subdivision with round numbers between center and city and city borders. If possible the radii should coincide with the sections of the population census. If the city is small it is possible to delete one or two distances. In any in any case, the distance or difference between successive distance should not be smaller than 500m, otherwise people will not be able to make the choice (which will provoke questions and will cost time). In alternative to the various distances it is possible to make one box for each city quarter if it fits in the space. 7. Please check critically if other quantities in the survey need to be adjusted to local conditions, for example currencies! Choice of survey site(s) and budgeting The local choice of the site where the questionnaires are distributed and collected is made by the respective PPs. The target number of required samples is approximately 1500 for larger cities. For smaller towns (less than inhabitants) the number of samples will depend on the actual size. In general, the higher the number of survey, the higher the precision of the results. For example with 1500 questionnaires and an average of 5% cycle share, you could state that there is a 95% chance that in reality the cycling share resides in the interval from 3.5% to 6.5% (this is 5% +/- 1.5%). On the other hand, if the number of questionnaires decreases, than the precision decreases and the 95% confidence interval gets larger. For smaller towns the calculation is a bit different as it depends on the share of people that have been surveyed. Please contact Joerg.Schweizer@unibo.it for survey events in small towns ( The number of inhabitants will be required).

83 Page 83/152 The number of questionnaires depends also on your available budget: the promotion cost budget can be charged with the costs for printing the questionnaires, the gadgets and other event-publicity. External expertise costs of WP3 can be used to hire staff through a work-agency. Please clarify budget questions with P.G Sola. You can calculate the survey budget with the provided survey budget calculator. Just check and modify all input parameters, costs and event location and durations (white fields) and verify resources and budget. The choice location of the sampling sites and the date are crucial to the success of the survey. The critical factors are: In order to get an un-biased result, the mix of interviewees should be representative for the population. Good occasions are: Busy places like shopping areas in town centers, public parks (which are not predominantly used by mountain bikers), Festivals with a mixed audience (age, social groups, sportive and non-sportive persons). Bad places are: in front of a railway station or metro exit, cyclist meeting or in general sports events for active members. However, one event-day could be held at a cyclists-festival to obtain good information on cycling infrastructure. For efficiency reasons, there should be as many persons as possible willing to fill in the questionnaires. The willingness to compile questionnaires can be improved by the following: People are not in a rush but have time. An exciting gadget (however, the gadget should not be biased in promoting bikes, otherwise the survey will be biased) Each site and data (or particular event like folk-festivals, etc.) should be briefly described by compiling the site information form and discussed with UNIBO. The number of sampling sites depend on the expected number of interviewees and on the minimum number of the target sample size, which differs between PPs. This is an important number as it regards the quality of the survey as well as the required resources in terms of number of sampling sites, staff, equipment etc. Once the sampling sites are identified the permissions to make a survey should be obtained, if required. Preparation of survey event The local preparation of the survey event will be performed by the PPs. What is needed are essentially: Printing of questionnaires by PP Procurement of gadgets by PP Procurement of Information material Procurement of T-shirts with central Europe logo. Tables and chairs where the questionnaires are distributed, collected and where the assistant can fill in the questionnaires together with the interviewee. In general the event should take place in a positive and comfortable atmosphere, where people would like to stay for a while and take a rest. If outdoors: Umbrellas or tents should be provided rain and sun protection.

84 Page 84/152 The publicity should be used to attract attention and generate interest. Publicity for Central Europe and the local PP as institution would be sufficiently transport neutral. Places to display posters should be provided. A video projector, showing videos from the Central Europe web-site would be good to attract people. The big publicity (with posters) should not be made for the BICY project as this would strongly bias the selection of people willing to fill in the form. It is perfectly acceptable to distribute brochures and other information about the BICY project to interviewees who completed the interview. Assistants who hold the interviews need to be trained. The need to memorize the survey and learn how to use it and guide the interviewee through the questions. It is of paramount importances for assistants read and understand section 5 of this manual!! The survey event These are the instructions on how the questionnaires should be compiled during an event. The event requires at least 2, if possible 3 staff members, dependent on the expected number of people visiting the stand. From the experiments in Bologna it turned out to be most effective if each trained assistant, equipped with a questionnaire, is approaching individually the interviewee and fills in the questionnaire together with him/her. It gives more trust if the interviewee can see the questionnaire (as shown in the photos below), we do not want to give the impression to compile some secret files. Fig 1: How to make interviews. If a large number of people is expected to compile questionnaires, the staff number needs to be increased in order to guarantee a smooth and controlled process. Elderly may need more attention when compiling the form. If the interviewee feels competent to fill in the questionnaire by himself (after hearing the general instructions), he may do so. Some, often younger interviewees actually wanted to do it alone. However it is advised to make a control-question, for example: "do you use the bus really every day" in case he compiled the bus section. Corrections: Usually a tick should be a cross (X). Wrongly ticked boxes can be corrected by crisscrossing the

85 Page 85/152 cross (essentially filling the box). After the sampling event is completed, all questionnaires must be collected in a parcel and sent, together with a print out of the site information form (Appendix I) to : Joerg Schweizer University of Bologna DICAM - Transport Group Viale Risorgimento, Bologna, Italy Detailed instruction for compiling the questionnaire This sections needs to be read carefully and understood by all assistance who participate in the survey. For any questions or clarifications please contact joerg.schweizer@unibo.it. The survey has only one page (see Fig. 2) and is organized in several blocks. Each block focuses on different aspects of the interviewee's mobility, but in some cases questions from different blocke are interrelated. We now describe how to compile the survey block by block in a logical order, while highlighting the interdependency between blocks. The general order is to go from left to right and from top to bottom. Ticking a box means making a cross (X) in the white box to the right side of the question or data. Some questions ask simply for a confirmation (one white box only), others require yes/no (with two white boxes, the first for Yes, the second for No ). A false cross can be corrected by criss-crossing it. In general multiple answers are always possible, except for some cases where it does not make sense, for example it is not meaningful to tick yes and no of the same answer or to tick two different times.

86 Page 86/152 Fig 2: The one page mobility survey (this is not the most recent version, some errors have been corrected). The residence block

87 Page 87/152 The residence block (see Fig. 3) can be found on the top left of the survey. Attention the residence block is the only block that depends on the specific city where the survey is conducted. This means city name and distances may be different from those in Fig. 3. Instead of distances or inside/outside center also a list with suburbs could be seen. Fig 3: The residence block. The residence block is necessary to locate approximately the home of the interviewee because mobility behavior depends on the population density. If the interviewee answers no to the first question, means he/she does not live in the specified city, the all further answers of this block can be ignored. The general mobility block To the right of the residence block is the general mobility block, which should give information on how frequent the interviewee uses each transport mode (car/motorbike, bike, public transport and walking) and for what purpose they are used, see Fig. 4. Fig 4: The general mobility block. The block has a table form where each major row corresponds to a different transport mode (car or motorbike, bike, public transport, walking). In the first major column the frequency is requested while in the second major column the trip purpose for each mode is asked. It is recommended to go through this block mode by mode: first ask how often a specific mode is used (almost everywhere, from time to time, never) then ask for the trip purpose of the same mode (Work or study, shopping, other). Please note the transport mode that the interviewee uses almost every day but for any purpose. Because only for those modes the typical work-day block needs to be compiled, however see also instructions for this block.

88 Page 88/152 The typical work-day block The typical work-day block (see Fig. 5) occupies the central part of the survey. It is also the most challenging block to fill in. Fig 5: The typical work-day block. Like with the previous block, the major rows of the table correspond to the transport modes (car or motorbike, bike, public transport, walking). Attention: Fill in only the modes that the interviewee uses almost every day. This can be seen in the previous, general mobility block. If the interviewee has not identified a mode that he uses every day then he should be asked to identify the most typical day and compile the question in this block for this day. It is not allowed to mix the trips of different days! Here is an example when people do not hae a mode that they use almost every day: one person said he would use the bike when it does not rain and public transport when it rains. In this case he must only compile the bike row because most of the days it does not rain at least in central Europe. Another typical example is a person who sometimes uses the car to work outside the company premises or main office, otherwise he would go to work by public transport. In this case it would be good to ask the person what he regards more typical: working outside or working on the factories premises or office building. Yet other persons simply do not have any regular life. Often people ho are unemployed or people in pension. Again, in this case one should ask o think of the most typical day and compile the form accordingly. The three major columns have the following meaning: The time in minutes that the interviewee passes on each mode during 24 hours. The interviewee needs to be aware of this fact and may need to be reminded to include the time for the return trips, see also Fig. 6.

89 Information on trip and infrastructure Personal experience on their daily trip. Page 89/152 Fig 6: Illustrative examples of home-work trips and on how to estimate the time per mode per day. It is recommended to go through the typical work-day block in the following order: 1. First the time per day column for all modes. It is convenient to reconstruct the sequence of trips of an entire day and count the respective times (see Fig. 6). This is the most challenging task for the interviewee. 2. Then go through the columns trip and infrastructure characteristics together with the personal experience mode-by-mode. These are questions that require less concentration. The future transport block The future transport block asks under which conditions the interviewee is willing to change to public transport (bus, tram, metro, train) or to use the bike for his/her daily trips. Again, multiple answers are possible. However, the last question ( under no circumstances I would make regular use of... ) does automatically ignore all previous answers for the respective mode. Still, it is necessary to tell the interviewee all options so

90 Page 90/152 that he will at least think about them for an instance. As indicated in the yellow boxes, an interviewee who already uses public transport or the bike on a daily bases can still express what features she/he expects from each mode. Fig 6: The future transport block. Personal block The last block concerns sex and age of the interviewee. The answers can be guessed by the assistant and does not require further questions, only if not sure about the age. Fig 7: The personal block. The age groups are defined as follows: Minor: 0-16 years. Adult years. Elderly greater 60 years. Appendix 1: Site information form This page needs to be compiled printed for each event. It serves as a cover-page for the pile of collected questionnaires to be sent to the University of Bologna. Location (city) Date (dd/mm/yyyy)

91 Page 91/152 Type of event * * Please comment about the type of event for example: supermarket, biker festival, park, mobility week, etc. Send questionnaires to: Joerg Schweizer University of Bologna DICAM - Transport Group Viale Risorgimento, Bologna, Italy Figure E.2: Mobility Survey Manual F. Survey Error/ Bias Correction Bias in a survey can take many forms, but at the root it is any error that shifts answers, often but not always away from their true values. Bias takes many forms and to eliminate all bias is essentially impossible, it can only be minimized. Despite efforts to ensure balanced survey responses, all surveys introduce bias. It can only be minimized, and then an effort made to correct it. Bias is but one type of potential error that any survey faces. Despite the detailed methodology guide, survey administration was not entirely consistent, and even if it were, a design can never be perfect; the public introduces further error, and more. Likely types of errors in this survey include the following. Coverage Errors Coverage errors occur when some portion(s) of the target population is/are excluded from the survey. In this survey, that would certainly include those who travel infrequently or who do not travel at all. Coverage error

92 Page 92/152 probably helps account for the relatively low proportion of older respondents, and could increase or decrease representation of other groups, such as workers, depending also on time of day and location of the survey. Nonresponse errors Even for those who are present, there are always those who refuse to participate in a survey, so their potentially unique views are not able to be included. Measurement errors These could also be called errors of perception and representation. The can include question wording, question ordering, interviewer effect, and more. Question wording errors: Given the language and cultural diversity of Central Europe, this is a probable source of some errors. The survey was provided in English by non-native speakers, and then translated into six partner languages. Each translation differed in subtle or even not-so-subtle fashion, despite an effort to provide clear instructions for strict quality control. Partners conducting translations were instructed to pay attention to the exact meaning of the questions. Ask yourself whether people would give the same answer hearing the question in their native language than you would reading the English question. Because the survey was administered in an active fashion by BICY staff, with specific directions to minimize error and ensure standardization of response, the technical wording of the survey questions may be less important than the success of the BICY staff in carrying out the survey goal. For example, although the travel data could be confusing to respondents, they were not intended to fill in the boxes themselves; a key directive to staff was: Attention: Fill in only the modes that the interviewee uses almost every day. This can be seen in the previous, general mobility block. If the interviewee has not identified a mode that he uses every day then he should be asked to identify the most typical day and compile the question in this block for this day. It is not allowed to mix the trips of different days! Question ordering errors: The order in which a survey s questions are introduced can always introduce bias. However, there is no alternative but to have an order to questions, although bias can be minimized or at least, a direction chosen. To minimize bias, the survey flow opens with a broad question to determine all the modes a person ever uses, and then in the central block of questions, specifics about each mode are asked. In both cases, the car and motorbike subsections were always offered first, which would presumably tend to bias toward motorized answers, if anything. Only the very last question asked what a person needs to begin bicycling, and then after the question was asked for public transport. In this sense, if the survey was not neutral, at least de-emphasized bicycling, and may thus have biased responses away from bicycling. Bias cannot be avoided, so by steering bias away from cycling helps strengthen the validity of the findings, making them more cautious and conservative findings. Interviewer effect errors:

93 Page 93/152 Although interviewers were instructed to be unbiased, it is always possible that bias can creep in, even unconsciously. For example, an interviewer might approach people who appear to be cyclists, since the study motivation is cycling. This could artificially inflate the cyclists. Another case would be handing out the survey to a particular group, such as a cycling organization, or a group of workers from a certain employer, who might tend to be more uniform. In fact this happened, and was corrected for (see adjustments discussion). The fact that interviewers administered the questions person-to-person can also introduce subtle bias, for any number of reasons such as if some were shy to report everything, or because of other influences. It is important to note that surveys were tabulated by BICY staff, to minimize errors. The upside of interviewers effect on error in this case is to help reduce errors of perception, and help ensure completeness of the data, as incomplete survey would surely be higher without that help. In the cases where respondents wanted to fill the survey themselves, administrators were instructed to make a control-question, for example: do you use the bus really every day in case he compiled the bus section. They were also instructed, that older folks may need more attention when compiling the form. Another form of interviewer error would be estimation error. For example, survey administrators were required to guess at the age of the respondent (minor, adult, and senior). Incentivization bias: Although gadgets were provided as an incentive to participate, partners were instructed the gadget should not be biased in promoting bikes, otherwise the survey will be biased. Duplicate survey errors: In fact a large group of copies of the same survey were detected in one response group. The carex with which surveys were processed, as well as the computerization of results, helps to detect errors. Handwriting was recognized, for example. If the motivation is simply to complete the survey faster, the error may be different than if the goal is to, for example, artificially inflate the number of cyclists in a given place. Unfortunately, a sophisticated effort of counterfeit surveys would be difficult to detect. Failed to report location: A number of survey groups could not be matched to place, and in these cases their utility was reduced, or even had to be removed; for example, when surveys from two different cities were mixed. The bias here stems also from not being able to identify the diversity of locations. With good location data, additional interesting analyses would be possible. Assumptions and Algorithmic Errors: Assumptions were made regarding the interpretation of survey responses. This included the assumption of average travel times in order to find distances traveled (discussed in modal split sections). Another example was the calculation of the share of car owners. Because car ownership was not explicitly identified by survey question, it was arrived at by combining survey data. Someone who used a car for everyday travel, as the driver; was not a minor; and possessed a driver s license; was presumed to be the owner. These place-based survey respondent shares were compared to national per-capita data regarding ownership of cars, to further generate survey correction weighting. Here again an assumption is made: that survey respondents own cars at the rate of the country as a whole. This may not be true; generally urban car ownership is expected to be lower

94 Page 94/152 than rural ownership. However, the bias will be toward increasing the share of car owners, so it remains conservative regarding bicycle behavior. Correction of Bias By obtaining official data for population-level proportions of: Males Females Minors Adults Seniors ( elderly ) Car owners It was then possible to create weights for each group, for each city or region. An effort was made to correct all known sources of bias possible. Primarily this was accomplished through adjusting results based on shares of population groups found in survey given actual shares found in the general population. Naturally, certain types of people may be more likely to be found on the street, may be more open to participating, and the survey administrators themselves, both in who they approach for potential subjects as well as how they approach them, may have a conscious or unconscious preference which skews the results. For example, no survey had equal numbers of male and female respondents (see graph, below). Most dramatically, Budaörs had no male cyclists. Considering that a typical population has close to equal number of males and females, even small differences can be important. At the same time, to weight based on shares in the overall population will miss important differences. What if one group is more likely to travel? Certainly this is true for many groups. By focusing on times when most people travel, the attempt is made to minimize this source of bias error. Additional discussion of Ravenna: mysteriously, cycling modal split for Ravenna was 30 when taking all surveys into account, and only 17% when taking only those interviewed in Cervia (but with residence in Ravenna). The data is illustrated in the following table as well as the above graph.

95 Ferrara Graz Erfurt 3 Košice REGION Košice Michalovce SNV Prague Koper Velenje Budaörs Page 95/152 Official Survey % Diff % % 59.91% % % -3.45% % 66.67% % % % Figure F.1: Comparison table of modal split data by source (official/partner data and survey findings data). Percent difference calculated as (Survey Value Official Value) / Survey Value. Note, Košice region data was calculated as an average, not as proportionally weighted shares of survey.

96 Bicycle Pedestrian Car+Motorbike Pub Transport Bicycle Pedestrian Car+Motorbike Pub Transport Bicycle Pedestrian Car+Motorbike Pub Transport Bicycle Pedestrian Car+Motorbike Pub Transport Bicycle Pedestrian Car+Motorbike Pub Transport Bicycle Pedestrian Car+Motorbike Pub Transport Bicycle Pedestrian Car+Motorbike Pub Transport Page 96/152 Ferrara Ferrara Survey % Diff Graz Erfurt Prague Koper Velenje Graz Survey Erfurt Survey Prague Survey Koper Survey Velenje Survey Budaörs Survey % 39.85% % 25.00% % Diff % % 20.63% 6.54% % Diff 59.91% 2.07% % 37.20% % Diff 66.67% 0.43% -1.85% -3.86% % Diff % 40.42% 14.94% % % Diff % 65.07% % % Budaörs % Diff % % 41.68% Figure F.2: Comparison table of modal split data by source (official/partner data and survey findings data). Percent difference calculated as (Survey Value Official Value) / Survey Value.

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99 Page 99/152 Figure F.3: Graphs above and on preceding pages compare official modal split data (blue) with survey modal split data (red), where they were available, for Graz, Ferrara, Erfurt, Prague, Koper, Velenje, and Budaors. Modal split for car and motorbike were combined so all data would be consistent.

100 Page 100/152 Figure F.4: Number of surveys by place. (Places organized geographically, West to East.) The number of surveys is critical for the validity and usability of survey results. There were target minimums for survey response. In the ideal, at least 1500 questionnaires were needed to get the modal split at a precision of approximately +/- 1.5% (95% confidence interval ). Only Velenje achieved this, with an outstanding survey effort and 1535 surveys returned, although others came close. Interesting to compare normalized mode share to relative surveys collected (using a normalized metric for each). In this way we can compare relative amount of bicycling to relative amount of surveys collected to explore any pattern. There doesn t appear to be a pattern, which is as expected. Lowest survey response at left (in blue), highest on right. The statistical confidence intervals for each mode share for cycling found by the survey are depicted below The largest spreads correspond to the smallest survey response sizes. For example, the result for Graz is predicted to be around 13%, but we are only 95% statistically confident that the true value is within the given range, with a minimum of and a maximum. (Requiring the assumption that it is a normal distribution.) To be fair, it must be mentioned that the Graz survey pool is actually 400 surveys, however, these were not suitable for input and are being hand-coded. Likewise, some N given above were in the end not able to be used (e.g., Ravenna, only approximately 100 surveys were usable due to methodology errors).

101 Page 101/152 Figure F.5: Modal splits with their 95% Confidence Intervals (top, showing the range of statistical certainty for each answer); and observations as to the accuracy (bottom).

102 Page 102/152 As one of many error checks, and to see if there might be any difference in outcomes based on number of surveys, the following figure was generated comparing partners survey collection success (blue) with the amount of cycling indicated (red), sorted by amount of surveys collected. Fortunately there was no relationship seen, although it is noteworthy that most of the high cycling cities provided such a small fraction of the required surveys, weakening the potential strength of the insights about their cyclists garnered here. The fact that a large portion of the Ravenna surveys could not be used adds to this deleterious effect. Figure F.6: Lowest to highest relative survey response with relative bicycle mode share (both normalized). Shares by Group Figure F.7: Comparison of proportion of respondents who were male or female, by place.

103 Page 103/152 Survey Adjustment Weights In order to adjust the survey to match true population shares by survey age category, official data was found as discussed above (Indicator Data, Additional Data Requested section). A graph of the comparative results is below. Share of males Real share of males Ferrara Comacchio 57.0% 59.0% 46.7% 49.2% Ravenna 52.4% 48.7% Cervia 47.0% 47.6% Graz 54.1% 48.2% Erfurth 54.8% 48.1% Košice-C 51.5% 47.6% Michalovce 52.2% 48.3% SNV 49.8% 49.0% Prague % 47.0% Koper 47.2% 49.5% Velenje 53.6% 50.1% Budaörs 41.1% 47.7% Share of females Real share of females 42.5% 53.3% 42.2% 50.8% 38.0% 51.3% 48.0% 52.4% 60.5% 51.8% 36.6% 51.9% 47.1% 52.4% 47.5% 51.7% 49.5% 51.0% 46.9% 53.0% 52.2% 50.5% 45.0% 49.9% 58.9% 52.3% Share of minors Real share of minors 17.0% 3.3% 12.7% 3.6% 17.8% 3.9% 29.7% 3.5% 5.9% 7.7% 7.5% 7.7% 14.0% 5.8% 16.9% 5.8% 12.5% 5.8% 7.4% 3.9% 7.6% 4.0% 48.5% 4.5% 0.0% 5.4% Share of adults Real share of adults 62.6% 55.3% 58.0% 58.1% 68.4% 53.9% 51.3% 54.8% 84.3% 48.8% 75.3% 56.5% 73.9% 64.3% 82.1% 64.3% 69.4% 64.3% 75.3% 62.5% 72.2% 61.6% 41.0% 67.6% 69.8% 61.9% Share of seniors Real share of seniors 19.9% 33.6% 29.0% 29.9% 4.7% 29.2% 14.7% 28.9% 10.3% 31.4% 4.8% 27.0% 10.9% 17.7% 0.3% 17.7% 17.4% 17.7% 15.9% 23.2% 19.6% 23.5% 9.0% 12.6% 30.2% 19.5% Figure F.8: Table comparing of demographic shares of males, females, minors (age 12-16), adults (ages 17-59) and seniors (ages 60+) in survey response, with actual shares based on official population data found by research. Relative weight of males Relative weight of females Relative weight of minors Relative weight of adults Relative weight of elderly Ferrara Comacchio Ravenna Cervia Graz Erfurt SEX RATIO CHECK Košice Michalovce SNV Prague Koper Velenje Budaörs Figure F.9: Some major demographic differences were found. Red means > 2 or <0.5. Orange means > 1.3 or <0.75 Relative Weights by Group: Under- and Over-Representation The stark contrast of some survey population shares to true shares in the general population helps illustrate why weighting was necessary, as well as showing where deficiencies were in the survey. Without a doubt, seniors (those age 60 and above) were the most underrepresented of the three age categories.

104 Page 104/152 Figure F.10: Relative weights by group. Note how under-represented seniors are in Ravenna, Erfurt, and Michalovce, and to a lesser extent, Graz. Figure F.11: Relative weights by group, less weights for seniors to allow the others to stand out more. Note how underrepresented minors are in Budaörs.,

105 Page 105/152 Figure F.12: Relative weights by group, less weights for seniors and minors (just males, females and adults), to allow those others to stand out more. Note how under-represented adults are in Velenje, where minors were a very large group (48.5%) and how under-represented females are in most places. Figure F.13: Comparison of weighting of responses to adjust male/female proportions in survey to actual population. Note that females are particularly under-represented in Erfurt, as well as Ferrara, Comacchio, and Ravenna. Inverting these graphs, to see which ones are over-represented, we get the following:

106 Page 106/152 Figure F.14: Comparison of five survey primary relative weights (males, females, minors, adults and seniors). Minors are particularly over-represented in many places. This could mean that they are more open to taking a survey (more free time, more interest or curiosity); more likely to be in the places where surveys happen; or preferred by survey administrators for any number of reasons (more approachable, less threatening). Figure F.15: Comparison of four survey primary relative weights (males, females, adults and seniors, without minors). Looking at over-represented groups without the minors in the picture. Budaörs had a larger share of seniors. In general, males and adults are over-represented.

107 Page 107/152 Figure F.16: Survey weights for Car Ownership. Car ownership was usually not found for actual city ownership, but rather on country ownership or a smaller area (in Italy, we found it for the provinces). Only for Graz did we find it for the city level. Actual ownership is expected to be lower in cities than in larger areas, so this can be an over-estimate. At the same time, some survey respondents are arriving from outside the city, so the focus is not entirely on the city center. Exclusions and Adjustments Despite the focused effort to assure consistent results, some unreliability invariably exists. Although efforts were made to assure accuracy and validity of the surveys collected by each partner, after a critical review, irregularities, detailed partly in the Annex on Survey Methodology, some problems were identified which were difficult to correct for. In particular, the results from Erfurt are considered unreliable at this time. With our survey, Erfurt has a mode share of 34% cycling and 26% Car+Motorcycle (by distance), which seems clearly incorrect. The first problem is the relatively small number of surveys collected (186/1500 required). A second explanation: there may have been too much focus on the city center where it is impossible to arrive by car. True, the population of Erfurt may have more retirees whose schedule and travel patterns allow more use of the bicycle, however, Erfurt s survey had only 4.8% seniors, whereas the real share of the population is 27%. No official modal share for Erfurt was provided, thus UNIBO-DICAM sought and found two official modal splits and have thus adopted the most recent modal split from a study (identified here as Erfurt 3, with 8.7% cycling, versus 34% in the survey). The second major problem is concerns Partner Place, Budaörs. Although many surveys were collected (1481, over 5% of the population!), it is striking that there were absolutely zero minors. Additional Survey Issues of Note

108 Page 108/152 Ravenna: a group of government workers accounted for 35% of the surveys, which makes them a large nonrepresentative sample (with a wonderful bikeshare of 35%, which happens to be able double the population rate found). The surveys in Ravenna s additional Partner Place, Cervia, included Ravenna residents, the primary means of identifying Ravenna respondents. Their results were quite different from the office worker-influenced group. Moreover, in Cervia there were approximately 200 false surveys (copies) mixed into the surveys provided for processing, which were fortunately identified. These had to be removed. In the end given these two major problems, only 100 surveys could be used for Ravenna, greatly reducing the validity of their survey. In this sense and in other cases of exclusions, the number of usable surveys differed from the totals given. Ferrara: we believe the bicycle modal split for Ferrara is too high for the same reasons as for Ravenna. However, Ferrara did not provide any location data, so it was possible to exclude or adjust for nonrepresentative groups or areas. Locations: some partners did not document locations as required. This reduced the ability to look at spatial patterns, and the ability to consider how representative the surveys were. Also serious, it reduced the ability to use some surveys, for example Ravenna residents were not identified. G. Modal Split Considerations The BICY Project modal splits were calculated relying first and foremost on people s reported travel times for the mode or modes they use on their most typical travel day (e.g., a work day). In other words, rather than being a modal split based on number of trips, or distance traveled, the primary modal split is calculated by the daily duration of travel for modes used on a typical workday. In this sense it aims to be a true representation of the behaviour of those present on the streets at any given time. The goal has been to gather the broadest possible representation of regular travel, starting from the point of view of work (which can include school, for students taking the survey). The trips are thus not necessarily exclusively work trips, but may include all travel for a typical workday. This is intended to capture additional regular workday trips such as shopping, picking up children from childcare, etc. Flexibility exists here for someone whose regular trips do not occur on workdays; someone who works or goes to school on weekends can also answer for those work day trips. The survey was administered in an active fashion, with specific directions to minimize error and ensure standardization of response. Because of this effort, the actual wording of the surveys is less important than the success of the BICY staff in carrying out the survey goal. A key directive to staff was: Attention: Fill in only the modes that the interviewee uses almost every day. This can be seen in the previous, general mobility block. If the interviewee has not identified a mode that he uses every day then he should be asked to identify the most typical day and compile the question in this block for this day. It is not allowed to mix the trips of different days! The survey has allowed a modal split to be found in a uniform manner for all partner places. For many places in the East this was the first time this was possible. Although other indicator data was not always able to be found, this is a critical indicator. The most important component of the modal split is the quantity of bicycling.

109 Page 109/152 Modal splits can be calculated in many ways. Trips, duration, time are three major categories of inquiry for finding a modal split. Type of trip is another sub-category. Frequently work trips are the most studied, because these are very regular. A modal split is imperfect; for example, travel patterns change throughout the year due to changes in activity (holidays, school year, peak activity times) and other factors such as weather, which affects all modes but is particularly important for bicycling and walking. This importance varies by place and culture. For example, in the strong bicycle nations such as Netherlands and Denmark, the bicycling population is almost undeterred by rain and cold; cyclist tend to travel by bicycle year-round. In other places, seasonal or even short-term variations in rain, cold, snow, and particulalry ice, can interrupt cycling patterns, which may only begin to recover when the weather improves. The BICY project attempts to deal with concerns of weather and bicycling through two survey questions, asking whether the respondent experiences cold and rain while cycling regular trips (see below). Comparison of Modal Splits (Time, Duration) and Regular Cyclists

110 Page 110/152 Figure G.1: Comparing the relative shares of Regular Bikers with the modal splits (both by maximum time and distance). The graph reveals special case cities: Velenje and Koper (cities with similarities, both from Slovenia); and Ravenna/Cervia (both cities from Ravenna province). The number of people who consider themselves regular cyclists is much higher than the share whose longest trip (by time or distance) on a typical work day is by bicycle. What can we infer? Graz also stands out for having a significant difference between time and distance. This suggests that travel is more efficient by bicycle in Graz, perhaps owing to its extensive, uninterrupted urban bikeways. Again, the maximum-distance-based mode share depends on the time of other modes; all survey modal split data begins as self-reported travel times, with trip distances found by multiplying by an assumed average speed. If more than one mode is reported, the one which is larger (more time, or more distance, depending) is the one that is assigned. The modal split is then calculated as the share of people assigned to each mode. Modal Split of All modes In comparing the BICY Project modal split found by survey, the clear pattern of relatively high cycling in western places versus relatively low cycling in eastern places, is not seen for other modes, although on balance, driving is overall lower in the eastern places. After finding the modal split by time, an average speed was applied to calculate modal split by distance.

111 Page 111/152 Using self-reported maximum time of travel to generate modal split has its own benefits and drawbacks. One benefit is it increases the potential for multimodal travel to be reported (e.g., someone who walks to a train, then takes a bicycle for errands at the jobsite). However, as with any public survey effort, individuals may introduce bias by guessing at the true time of some trips. The limitation of not counting trips, has another drawback, because it may in fact make slower vehicles, or those used only for longer trips, seem more important. Someone who takes a 1-hour train or car ride, but makes five 10-minute trips on bicycle during the day (e.g., for errands and lunch), would be counted as a public transit or automobile user, not a bicyclist; the time ratio was 6:5 even though the trip ratio was 1:5. Figure G.2: survey modal split data compared for all Partner Places (max distance).

112 Page 112/152 Figure G.3: Modal split: survey-based work day trips by time (based on max time duration) Figure G.4: official data compared to survey data for Partner Places where data for all modes was available (max distance)

113 Page 113/152 Figure G.5: survey modal split data compared for some Partner Places (max distance). Figure G.6: Survey bicycle modal splits with their 95% Confidence Intervals (showing the range of statistical certainty for each answer).

114 Page 114/152 Walking Figure G.7: Maximum distance (above) and maximum time (below) modal splits found by survey, without exclusions, for walking.

115 Page 115/152 Driving Figure G.8: Maximum distance (above) and maximum time (below) modal splits found by survey, without exclusions, for driving.

116 Page 116/152 Public Transport Figure G.9: Maximum distance (above) and maximum time (below) modal splits found by survey, without exclusions, for public transport.

117 Page 117/152 Motor Bike Figure G.10: Maximum distance (above) and maximum time (below) modal splits found by survey, without exclusions, for motorbikes.

118 Page 118/152 Multi-Modal Modal Splits Modal splits have been based on the mode that is used regularly for any purpose. This modal split takes also into account the combination of the major modes. Thus for Public Transport, public transport use is counted every time it is used, even if the survey respondent traveled more via another mode (e.g., bicycling, walking or riding in a car to get to transit). Multi-modal split for Walking 11.70% 21.80% 4.70% 3.10% 4.20% % 16.30% 29.90% 11.50% 31.00% 10.60% 0.00% Multi-modal split for Motor Bike 6.20% 4.20% 9.00% 19.40% 2.30% % 4.50% 2.70% 2.80% 14.40% 8.70% 1.60% Multi-modal split for Public Transport And Auto 0.40% 0.30% 1.00% 0.20% 3.10% % 11.40% 1.10% 0.80% 0.40% 2.90% 0.00% Multi-modal split for Bike And Auto 1.70% 1.20% 10.80% 5.70% 6.20% % 2.80% 0.40% 0.00% 2.50% 1.80% 0.00% Multi-modal split for Bike And Public Transport 0.50% 0.00% 1.20% 0.10% 6.90% % 2.90% 0.80% 1.30% 0.00% 0.40% 0.00% 48.70% 49.90% 38.10% 40.20% 32.70% % 36.60% 22.70% 28.60% 35.10% 37.60% 38.20% Multi-modal split for Public Transport 7.70% 4.40% 1.80% 0.90% 15.50% % 19.50% 36.10% 46.40% 5.60% 7.80% 58.30% Multi-modal split for Any Other Combination 0.50% 0.40% 8.70% 7.00% 16.30% % 2.20% 4.00% 6.20% 6.90% 26.90% 0.00% 22.50% 17.80% 24.60% 23.60% 12.80% % 3.70% 2.40% 2.40% 4.20% 3.20% 1.90% Multi-modal split for Automobile Multi-modal split for Bicycle Figure G.11: Graph and table showing multi-modal split

119 Page 119/152 Figure G.12: Multimodal splits for bike and car, and for bike and public transport, using maximum distance modal splits found by survey, without exclusions.

120 Page 120/152 Bike Mode Share data barriers: In some studies only short trips are considered (for example less than 5km). In other studies the modal split refers to a specific trip purpose (usually home-work trips). Some census and survey efforts identify only work trips, or only the mode which was used the most (typically, measuring distance, although time may be a more meaningful measurement). In order to make comparison with other studies, it would be necessary to collect also information on trip length and trip purpose. Thus bicycle mode share is a broad topic which is not clearly defined, and different data sources can thus be incompatible. As a case example: In the United States, only work trips are counted, and then only if the bicycle was used for the longest distance (so biking to a train would typically not be counted). Shopping trips, recreation trips, and short multimodal trips are not counted. Moreover, the survey considers only one week every ten years. If this is a rainy week, total bike trips might be artificially reduced. Bicycling varies by time of year with many factors, not just weather but also such factors as the school calendar, so such a survey is very limited. Even with reasonably reliable bike-share data source, bicycle mode share data has some major flaws when used in the context of sustainable transportation: the bicycle mode share does not say anything how far people cycled. It may well be that the remaining car-trips much longer than the bike trip. This means the car-traffic can remain unchanged even with a high bicycle mode share. This could be compensated by the km bicycle mode share = km by bike / total km driven It has been observed in many cities that the the increase of the bicycle share decreases in the first place the public transport share and leaves the car share almost unchanged. Comparing between modes by distance or even time is misleading for a car on an open road can cover large distances in a short time. While it is more fair to compare time, as people are generally thought to have an innate ravel budget for each day, even more meaningful may beẗ to compare trips or accopmlishments. For example, one car trip might cover more miles or take more time, but actually the bicyclist on the other hand might have accomplished much more: several stops to shop, return library books, socialize, etc., but crossing much less distance. These differences may also be an expression of different land use, so comparing. Bicycle usage has a significant positive environmental effect if it succeeds in reducing the distance driven by cars. Assuming walking, cycling and public transport are considered sustainable then an indicator for sustainable transport is: car km = km driven by car per citizen per day for trips below 5km. Ideally this indicator should tend to zero, if all trips are replaced by cycling, walking and public transport. Note that oil consumption, carbon- and other emissions, noise and caused fatal accidents are

121 Page 121/152 all proportional to the car km indicator. The car km indicator is therefore an important index which allows to estimate external costs, or external cost reduction due to cycling. In the current version of the report only the standard indicators and the bicycle mode share have been determined. More significant indicators are expected as an outcome of the mobility survey and the OpenStreetMap analyses. SURVEY ANALYSIS ANNEXES Analysis of the survey focuses here on details of regular travel behavior, including individuals travel environments, and their subjective experiences during those regular trips. People were also asked to predict what changes would be needed, for them to change their own travel behaviour. H. Cyclists Experiences Regular cyclists reported the experiences they have on their typical bicycle travel days. Types of Cyclists In the world of cycling, there are many, many different approaches. To some extent these have been studied in the scientific literature; this is discussed in more detail in the State of the Art report (WP3.2.10). Using survey data, quite a few different types of cyclists may be identified. In the survey, several primary categories of cyclists were identified, based on their frequency of cycling: From time to time cyclists Regular (commuter) cyclists Cyclists who described their experience cycling in a typical workday Potential cyclists The second two categories clearly overlap. Regular bikers are considered: (1) interviewees who use their bike regularly or (2) interviewees who use their bike irregularly but for the purpose of work/study or for shopping and they have provided also trip time information on their bike tours. It is important to distinguish the fact that the share of regular bikers is not the same as the modal split, although there may be a relationship, as illustrated in the following bar graph:

122 Page 122/152 Figure H.1: Comparing share of Regular Cyclists to Bicycle mode share (by maximum time) for each Partner Place. Typically, the second two categories overlap, and also serve as cross-checks. Of course variation and differing interpretations may exist; a regular cyclist may not have a job, but still answer for typical trips on weekdays. Another may have a weekend job, and only answer for those days. Someone that does errands every day by bicycle, might drive to work. Students might answer for their commute to school, or neglect to. The survey design was open enough to invite all regular trips, but no survey will have perfectly correct responses. The bias here will tend to under-represent cyclists, and is expected to be equal across all survey areas. Gender and Cycling Recent findings have identified that generally, in starter cities, women are less likely to cycle; yet in advanced cities, they are more likely to cycle. Reasons offered include that starter cities feel dangerous and inhospitable to cycle in, and cycling feels unsupported, and that women are more sensitive to these harsh conditions. Although a male may suffer blows to his testosterone levels due to the injuries and injustices suffered, and lack of status conferred, a woman is much less likely to go to war, so to speak, on the roads. The term militant cycling has been used in the bicycle advocacy community to describe cycling where conditions are harsh. In contrast, when conditions improve, women are more likely to be without a car, and/or to have more responsibility for local trips and short errands including child care, which are suitable for cycling. The large number of women with children and groceries carried by cargo bikes in Copenhagen lends credence to this theory. There were observations of gendered effects seen in the survey. For example, in Cervia, Italy, only 10% of cyclists felt respected by drivers and they admitted to high levels of fear as well and they were overwhelmingly male. This suggests females had been scared away. In contrast, in Graz, 80% felt respected and the female to male ratio of the cyclists was much more balanced.

123 Page 123/152 Unfortunately, gender balance in the survey cyclists was not representative across all partners, so detailed findings are more challenging. However, when we compare the ratio of women cycling to men cycling with the amount of total cycling, where good data exists, a rather linear pattern emerges, strongly supporting that yes, as cycling increases, women are more likely to cycle. Future comparison with values from advanced cycling cities such as Copenhagen and Amsterdam will be of interest. What is the overall shape of this relationship? What other patterns might there be (e.g., effects of weather, lack of continuity, etc.)? East Meets West It is interesting to consider the different geographic compositions of responses for a variety of questions. In some cases, such as quality of bikeway network, or amount of cycling (modal split, above), there is a clear division between east and west. For other inquiries, there is no such pattern; either the concern is universal (e.g., fear of cars), or there may be other patterns operating which could be such things as such as size of place, urban density, cultural differences, or some other factors. Figure H.2: Comparing cycling as found in the survey, for west and east Partner Places.

124 Page 124/152 Young and old. Although representation of older cyclists was generally low, the amount of cycling by older folks can be substantial. Cycling has many benefits for aging people. Further analysis of this important topic is planned for the Transnational Strategy Report. Similarly, youth cycling is critically important; for one, younger cyclists are more at risk than adults. In addition, starting cycling at a young age can help set healthy travel habits for life. Ideally, the BICY Project will realize a special focus on the needs of older cyclists. Factors Affecting Cycling Beyond the initial inquiry of indicators calculated from official data, the survey affords a great deal of opportunity to assess the impacts of the environment and other factors on cycling. As described earlier, the survey also affords a unified effort at quantifying the amount of cycling: either by the number of regular and sometimes cyclists, or by the modal split (percent of travel made by cycling). The modal split used here, unless otherwise indicated, is that calculated by maximum time, because it is the most direct data. Perception is important in cycling. If people think it is a positive experience safe, fun, fast, economical, and comfortable, for instance they are much more likely to do it. NOTE: Because of the evident heavy bias with Erfurt, and the tiny sample size of cyclists in Budaörs, those cities are often omitted from these comparisons. Effects of Topography Although data on topography (hills or grades) was not requested in the standard indicators, a survey question for cyclists asked whether the cyclist rides up steep grades.

125 Page 125/152 Figure H.3: Percent of regular cyclists who experience tiring grades in their regular everyday travel. It s no secret that hilly terrain discourages cycling, but the relationship has not been adequately investigated in the past. How much does terrain inhibit cycling? Certainly climbing hills takes additional energy and imposes potentially serious discouragement. Too, certainly the top bicycle cities of the world are flat including Shanghai, China the original Bicycle City. Yet the local bicycle culture can potentially come to grips with and surmount this effect. San Francisco, California is famous for its steep hills, yet in the 2012 Benchmarking Report of the Alliance for Biking & Walking, it is featured for being a leader in the USA despite those hills. (Alliance for Walking & Cycling 2012) As new technology takes hold, such as lightweight battery-assisted vehicles, particularly bicycles, hills become even less of a burden and can even charge batteries through regenerative braking, as well as increase the utility of cycling, allowing loads including groceries and kids to be carried up even steep hills. A recent video regarding a cargo bike renaissance in North America features this (LIZCAN, 2011).

126 Page 126/152 Figure H.4: San Francisco has a booming culture of cycling despite steep hills. Despite its hilly terrain, San Francisco has the fourth highest share of commuters who bike to work among major U.S. cities. Photo by Frank Chan, San Francisco Bicycle Coalition. (Page 100, 2012 Benchmarking Report.) Although the photo depicts a large number of cyclists climbing a hill with a marked bicycle lane, in fact San Francisco s cyclists have developed routes which avoid hills, most notably The Wiggle, which follows a historic stream bed, the lowest point between higher elevations, turning left and right repeatedly ( wiggling ) in order to do so. The cute name attests to the cycling culture s adaptation to hills. Hills cannot always be avoided, and their presence greatly increases the effort of cycling. Thus, much can be told by whether a regular cyclist encounters hills during her or his workday travels, and there is a clear trend when we compare the share of survey respondents who are regular cyclists, with their encountering hills during workday travel, as the following graphs illustrate.

127 Page 127/152 Figure H.5: Comparison of share who are regular cyclists, with how many of them experience tiring grades (hills) during thir regular travels. Top, all partners, sorted by regular cyclists Bottom, a selected group (8/12) sorted by Tiring

128 Page 128/152 Grades showing a close and inverse correlation between regular/commuter cycling and experiencing tiring grades (hills). Put another way: Figure H.6: Climbing the hill to being a regular cyclist. Another view of the data. Tiring grades, and share regular cyclists. Effects of Weather Weather is a wiggly topic in its own right, in predicting cycling rates. Cultural mores count for a lot. There has been some study of cyclist behavior in weather, but far from enough. One Australian study of college students found that weather effects were less determinate than predicted (Nankervis, 1999). However, although data on weather and seasonal climate patterns were not requested from partners (e.g., average numbers of clear sky days, rainy days, cold days, hot days, etc.), weather can have a strong effect on cycling rates. For this reason, the survey was designed to ask about behavior with regard to weather, and preferences for weather protection. Questions included: Do you experience moderate rain? Do you experience cold below 10 C?

129 Page 129/152 Of course the actual weather patterns may vary greatly from place to place, but by asking perception questions, the hope is this is minimized. Moderate rain in one place may be different than in another place, however, the approach to moderate rain may also be different. Heavy rains were considered too severe to consider, as even most regular cyclists will try to avoid heavy rain.

130 Page 130/152 Figure H.7: Effects of weather and cold associated compared with bicycle mode share (from maximum time). Figure H.8: Bicycle mode share (Series 1) compared with a combination of cyclists experiences with two types of weather (Cold * Rain Series 2). It is interesting that in the east there is a group that appears to have a strong inverse-linear relationship (Košice, SNV, Prague, Velenje, Michalovce) as well as in the West (Graz, Ravenna, Comacchio, Cervia). Ferrara and Koper break these trends. Ferrara could be explained by its higher levels of cycling (a culture of cycling may better persist despite weather fluctuations), and relatively mild weather. Koper s resilience in the face of cold and rain is many times its nearest comparison places by this metric, which remains to be explained. Cold may turn out to be less a deterrent to cycling that heat; a future question might ask about whether regular cyclists experience unusual heat when riding. Alternate questions might have focused more on how weather affects cycling, e.g., Do you ever avoid cycling because of [heat/cold/rain]? And how likely..? It is important to consider high cycling cities are often in colder northern Europe. Cyclists warm up quickly when riding, but in hot climates may quickly feel very uncomfortable. Driver Behavior Certainly the culture of traffic, and in particular of driving how motorists operate their motor vehicles affects cycling. Many surveys and endless personal anecdotes attest to cyclists who have felt victimized on the roads, and many who do not cycle tout such experiences as having been determinate in their decision that cycling was too unsafe. Thus it could be expected that in places with higher cycling, cyclists would perceive their treatment by motorists as being better or at least, more respectful. The BICY Project survey asked specifically about whether cyclists felt respected on the roads, with an interesting, if geographically delineated, relationship found.

131 Page 131/152 Figgure H.9: Percent of regular cyclists who feel drivers respect them on their regular travel days. When sorted for bicycle mode share, there is no clear correlation, suggesting other factors are more determinant: Figure H.10: Bicycle mode share compared with percent of regular cyclists who feel respected by drivers.

132 Page 132/152 Safety and Fear Do cyclists feel safe? Regular cyclists gave reported whether they have fear of an accident in the survey. Figure H.11: percent of cyclists reporting that they fear an accident during their regular travel pattern. To make matters worse, many regular cyclists have been hit before, or at least, report having been in an accident : Figure H.12: percent of regular cyclists reporting that have actually had an accident while riding their bicycle.

133 Page 133/152 It is interesting that such a high proportion of Graz cyclists report having survived a collision (not accident, see discussion of this term). Comparison with data on feelings of respect from drivers, feelings of fear of an accident, and official injury data, is of interest for further analysis and inclusion in the Transnational Strategy Report. Culture of Driving Is there a correlation between drivers who are wedded to their cars ( Car Lovers ), and whether or not bicyclists feel respected on the roads? This may be is in part a cultural question (what feels like respect in one place may not feel the same in another). Figure H.13: comparing relative number of Car Lovers with relative levels of cyclists who feel respected (both normalized, and sorted by Car Lovers ) Continuity of Bicycle Routes The BICY survey asked specifically about whether cyclists enjoyed an uninterrupted bike path and whether there was partially a bike path along [their] daily route. (Note that path does not refer to a facility-type bicycle path, but rather to a route comprised of various bicycle facilities including 30 zones).

134 Page 134/152 Figure H.14: Presence of a Partial Path, and also of an Uninterrupted Path, along cyclists daily route, compared with the share of regular cyclists. Figure H.15: Presence of a Partial Path, and also of an Uninterrupted good cycling route ( path ), along regular cyclists daily route, compared with the share of regular cyclists and the bicycle mode share.

135 Page 135/152 Bicycle Parking (Quality of Destinations) Do cyclists find secure bicycle parking? This varies by place. What is secure bicycle parking in one place may not be considered secure in another, for example due to higher theft concerns, or preferences for certain types of parking. Figure H.16: Do regular cyclists find secure bicycle parking? This varies by place. I. Standard Indicators Analysis The Standard Indicators are measures calculated from the data obtained, and then viewed for their ability to describe cycling and ultimately to predict future cycling resulting from policy decisions and investments in cycling infrastructure. In the process of analysis, many efforts were conducted. The following graph helps illustrate the combined predictive power of all three standard indicators:

136 Page 136/152 Figure I.1: Average of all Standard Indicators compared with Bicycle Mode Share (% cycling, by distance. All normalized). The following memo shows one stage in the process, at a time when OSM data was not available but survey data was available, and additional sources were obtained for comparison and assessment of the preliminatry predictive power of these three indicators. Further development of predictive equations can build on these explorations and experiments. MEMO: A review of Standard Indicators using new data sources: (Truncated to fit report.)

137 Page 137/152 BICY PROJECT MEMO: To: All UNIBO-DICAM BICY Project Team Members First, the latest greatest graph with the strongest relationship of cycling mode share to the Standard Indicators (using R2 for goodness of fit of the linear trend line). The indicators are: Cycling index (km of cycle track per person) Network coverage index ( road km per km of cycle track) Network density index (cycle track km / area in km2 ) The strongest relationship found uses the direct max time-based survey data: Memo Figure 1: Cycling Index shown as it relates to Bicycle Mode Share (% cycling). Note that some partners are excluded from or changed for this graph. Mode share by max time was used because it is direct survey data. These graphs are repeated below, for max distance. Here we substitute a more believable mode share for Erfurt (because it found 34% mode share by max distance!), and remove Cervia (because its indicator data was too questionable) as well as Budaörs (because there were no minors in the survey). Removing Budaörs is not so important as it is a starter city, but removing Cervia is an important change due to the high cycling rate and large indicator data.

138 Page 138/152 Memo Figure 2: Coverage Index shown as it relates to Bicycle Mode Share (% cycling). Memo Figure 3: DensityIndex shown as it relates to Bicycle Mode Share (% cycling).

139 Page 139/152 It is also interesting that the max time version has a stronger fit by this test. One wonders if changing the average speed estimates that were used to calculate the distances traveled from the survey times reported would have much effect. Repeating these graphs for max distance, using our assumptions on average travel speed, in order to compare using the standard method of calculating mode share: Memo Figure 4: Cycling Index shown as it relates to Bicycle Mode Share (% cycling) after distance assumptions are applied to generate a standard-type mode share. Again there is a high value, although not as high. One had to wonder why Cycling Index had the strongest fit, when there were hypotheses that coverage index or density index would be better. Perhaps it is because population is the most reliable data we have! It is also noteworthy that the other two indicators are relatively weak, but much stronger for Max Distance than for Max Time here (see R2 chart).

140 Page 140/152 So let us look further Second, BICY data was partially updated by manually estimating better values from online mapping sources. Bikeways, urban area, and roadways were all replaced for some partners: COMBINED MANUAL AND OFFICIAL INDICATORS AUTO INSERT MANUAL IF EXISTS - wish there were a way to auto color the cell too in that case. Urban Area (km2) Urban Area (km2) - Center only Bikeways km Bikeways km - center only Road km (center if manual OSM) Population NEW STANDARD INDICATORS CENTRAL ONLY Cycling Index (Raw) E E E E E E E Coverage Index (Raw) Density Index (Raw) BOTH CENTRAL & SATELLITE Cycling Index (Raw) E E E E E Coverage Index (Raw) Density Index (Raw) Modal Split (Max Time) Bike Mode Share (Dist) Comacchio Ravenna Ferrara 23.50% 18.60% 15.70% 23.40% 18.60% 17.00% Cervia % Graz 11.70% 18.10% Erfurt 2 Košice-C Michalovce 9.00% 2.20% 5.80% % 5.80% SNV Prague % 3.00% 2.50% 3.60% Koper 4.90% 5.60% Velenje Budaörs 4.20% 1.90% 5.10% 1.90% Memo Table 1: manual effort to improve data over official sources. Going beyond the BICY data, next, I took the five cities with all needed data (only five had modal splits) from the Citizen Benchmarking project: Copenhagen Glasgow population of city London Malmo The Hague City/Region Bikeway km City/region roadway km Modal split data from Report Cycling Index (Raw) Coverage Index (Raw) Area of city Density Index (Raw) Cycling (from report) Memo Table 2: investigation of data from Citizen s Benchmark Project.

141 Page 141/152 Graphing this Benchmark data gave compelling results. It is interesting that the Coverage Index now has such a high value! R 2=0.862! However results were quite clustered, which may indicate a stronger relationship or perhaps is attributable to chance and the inherently stark contrast in data between starter and advanced cities. Next, I combined the new Citizen Benchmarks data with the BICY data, including partial updates to BICY data from the manual estimations of urban area, and both bikeways and roadway length: COMBINE OFFICIAL INDICATOR DATA WITH CITIZEN BENCHMARK DATA, INCLUDING OSM BY HAND Ferrara Comacchio Ravenna Cervia Graz Erfurt 2 Košice-CMichalovce Bikeways km Population Total road length Urban Area (km2) SNV Prague 5 Koper Velenje Budaörs Copenhagen Glasgow London Malmo The Hague Cycling Index (Raw) E E E E E Coverage Index (Raw) Density Index (Raw) Bike Mode Share (Dist) Modal Split (Max Time) 23.40% 23.50% 18.60% 18.60% 17.00% 15.70% 27.30% % 11.70% % 2.20% 2.20% 5.80% 5.80% 2.50% 2.50% 3.60% 3.00% 5.60% 4.90% 5.10% 4.20% 1.90% 1.90% Memo Table 3: combined Citizen s Benchmark Data with manual effort to improve BICY data over official sources. Note that it s tempting to see two linear groups here, the top group and the bottom. The top group in fact is mostly larger cities. On a hunch that the BICY roadway length is way off, especially after comparing it to the Benchmark roadway data, I excluded the cities that seemed off and got a much better result (also highly clustered!): Next I went to the Wikipedia modal split data, it consists of many European cities, data within last 10 years. (Soon to include BICY data!!!). This was joined with the Citizen Benchmark data. City Alicante Budapest Cologne Copenhagen Copenhagen Dresden 2 Glasgow Helsinki Lisbon London Madrid Malmö Malmö 2 Naples Rome RotterdamStuttgart The HagueThe HagueVienna 2 Warsaw walking population of city Area of city City/Region Bikeway km City/region roadway km Bike (Wik or Report) Memo Table 4: Introducing modal splits from wikipedia page with the data of Table 3. It was interesting that there was walking data, so I graphed it as well against the Standard Indicators. The results are quite odd: They seemed to suggest that walking and cycling were inversely correlated, which seemed preposterous, but then, we know about the trade-off (competition) between cycling and transit in the various scenarios. So I did a quick comparison:

142 Page 142/152 Memo Figure 6: Walking v. Cycling visualized(do they compete?) Next, I made the biggest comparison table yet, combining our data with the Citizen Benchmark data with the Wikipedia data (because many of the Wikipedia bike mode share cities corresponded to Citizen Benchmark cities that didn t have cycling data yet, so I joined them). COMBINE WITH WIKIPEDIA AND CITIZENS DATA PLUS INDICATORS INCLUDING OSM BY HAND Ferrara Comacchio Ravenna Cervia Graz Erfurt 2 Košice-CMichalovce Bikeways km Population Total road length Urban Area (km2) SNV Prague 5 Koper Velenje Budaörs Alicante Budapest Cologne Copenhagen Copenhagen Dresden 2 Glasgow Helsinki Lisbon London Madrid Malmö Malmö 2 Naples Rome RotterdamStuttgart The HagueThe HagueVienna 2 Warsaw Cycling Index (Raw) E E E E E E E E Coverage Index (Raw) Density Index (Raw) Bike Mode Share (Dist) Modal Split (Max Time) Walking 23.40% 23.50% 18.60% 18.60% 17.00% 15.70% 27.30% % 11.70% % 2.20% 2.20% 5.80% 5.80% 2.50% 2.50% 3.60% 3.00% 5.60% 4.90% 5.10% 4.20% 1.90% 1.90% Memo Table 5: joining all data sources to test with BICY Standard Indicators I then made the standard graphs with this new table. While we re still improving the source data, and many more cities could be considered, this wider analysis helps bolster the significance of the Standard Indicators relationships. There s also an intriguing issue of the trade-offs not only of cycling and transit that we ve already seen, but walking and cycling. It would be good to investigate the survey data for walking, and look for other relationships in light of all survey/scenario/indicators/etc. data (e.g., walking and transit use in various environments).

143 Page 143/152 Very interesting that the strongest relationship occurred when some of the current BICY values for street lengths were removed from the graph. The immediate reason is that most of those left are starter cities, who cluster near zero and support any fit line. Also very interesting that Density Index came into its own here, where it s been floundering with the BICY data (the area data for BICY has long been an issue, I actually hand-generated data and got a much better fit, because administrative boundaries can be wildly different from true urban areas. This is definitely an issue in Italy, where Provinces and Comunes are named for the city, yet their areas evidently are given as the same, with the same official boundary, yet the larger area includes other cities and huge open spaces. This will be improved in time.

144 Page 144/152 A brief summary table of the R2 values found for the various analyses: R2 for variety of calculations Cycling Index Coverage Index Density Index BICY data (Dist, no Cervia, Budaors) BICY data (Time, no Cervia, Budaors) BICY data modified (max dist) BICY data modified (max time) Citizen Benchmark BICY modified + CB BICY mod w/o bad road + CB CB+Wiki BICYmod+CB+WIKI Walking, CB+Wiki MAX HIGH MIN Walking v Cycling Memo Table 6: R2 values for the investigations described herein. I invite your insights and observations! Jason

145 Page 145/152 J. Common Indicators Additional Analysis and Steps As data was always in question and developing, with many ways to be viewed, this section illustrates some of the steps and potential promising leads for future development. In particular, the problem of urban area, which further affects calculation of bikeways and roadways, remains. Some urban areas are huge, for a small city, foiling our attempts to develop indicators related to density. The actual mix of urban area, open space, etc., and the relevant highways, remains a research problem; it is exciting that the BICY Project may address it. OSM data acquisition proved much more time consuming than expected, so at one point manual approach was attempted. Preliminary OSM data (found visually using OSM, not using calculations) gave: Figure J.1: Graph of bicycle mode share v. Density Index found with manually generated data from OSM. Which was a promising result. If we compare to one of the results using partner data for urban area, the fit is much worse (R2 of.212 rather than the notably high.696 above; slope slightly higher):

146 Page 146/152 Figure J.2: Graph of bicycle mode share v. Density Index found with officiallyprovided data (compare with above graph where manually generated OSM data is used instead). K. Exploration of Additional Indicators Several literature searches have been conducted regarding indicators. The State of the Art Report (WP ) investigates this as well. Interesting data points to seek: how long did it take the bikeway network to develop? Can we find longitudinal data for Partner Places or other locations? Also, methodology: what is the relationship of programs, and other infrastructure, over time? Are bikeways enough to induce all changes? Or at least, enough of a proxy of all the cultural, and institutional, as well as physical, changes that take place? Into what timeline can we compress a major upgrade with resultant increase in cycling? Bicycle use indicators The ultimate goal of the BICY Project is to increase bicycle use, making measurements of actual levels of cycling activity critical (see discussion in Modal Split section). The above infrastructure indicators are helpful because they are relatively easy to determine because no interviews or traffic counts need to be conducted. The most used Bicycle use indicators is the modal split, a proportion of total travel, e.g., by trips: bicycle mode share = number of trips by bike / total number of trips

147 Page 147/152 However, bicycle mode share numbers are difficult to come by, and unreliable, around the world, and Central Europe is no exception. Thus we have carefully constructed the mobility survey to assist in determining bicycle mode share, as outlined above. What are the best ways to gauge actual cycling? Modal split, as we have seen, is a time consuming and unreliable metric. The stakeholder Interviews report identified one alternative, flow measurements at key points. The benefits are many: quick and inexpensive to conduct, allowing comparison at different times of year to better understand seasonal fluctuations, as well as to identify whether cycling is increasing or decreasing over time. Limitations include the problem of changing bicycle flow patterns in cities, which may not be a problem at key points such as bridges, major destinations and other major bikeways; and also comparison between partners. How can we identify one, or a set, of key locations for counting cycling, that are comparable between different cities, for example? This method is promising however and can be attempted, and deserves further study. Experimental Additional Indicators Female-to-male ratio. Based on recent observations in the literature. An excellent proxy. Age. Income Cargo share. (Deliveries? See Sao Paolo and sister project Cycle Logistics.) Varied population categories, e.g., % adult cyclists. Minors. Seniors. Females (esp female-to-male ratio below, but also other windows on gender ) Injuries Center v. outside center. Poverty, etc. Bike theft rates available? Or just fear from survey. Extra-EU trade by transport mode - Monthly data - NSTR Nomenclature - Supplement No. 3/2011 (DVD) This DVD is published yearly. It contains data by transport mode from 2003 to BICY Indices The initial BICY Index consists of two Indices, one of which is the Coverage Index, calculated earlier. The second is the End-Point index, which aims to be a proxy for a spatial analysis of equality of access to secure parking. End-point index = number of public bicycle parking areas / number of public car parks

148 Page 148/152 BICY Index= Coverage Index * Endpoint Index Additional BICY indices are developed below. End-Point Index Unfortunately, data for the End-Point Index (EPI) is very sparse, particularly regarding car parks. The hypothesis that there is a car park regularly in all cities (e.g., every 600m was hypothesized) was tested but did not match the three data points partners provided. Because this is a spatial analysis, more detail as to where the parking is (and its scope, e.g., in the center only or throughout the entire city) is needed. More detailed data on capacity and informal parking is preferred as well. Cyclists in small numbers can typically find a tree, pole, or someone s fence or gate to park to informal parking but this is not always the case, and is quickly exhausted, as cycling increases. Completely apart from the obvious risks of bicycle theft, lacking availability of anyplace at all to park is certainly a problem and discouragement for cyclists, and can become quite problematic as an externality of cycling as well, when areas become blocked or cyclists feel forced to bring bicycles with them, even into buildings and shops. BICY Index 1 The originally conceived BICY Index is not available at this time, due to lack of data from Project Partners. However, the wealth of alternative data sources generated by the BICY Project has allowed a variety of similar indices to be conceived of and tested. These are numbered, BICY Index 1, 2, 3, N, with the originally intended BICY Index taking the name BICY Index 1. For example, there is something of a correlation between those finding parking on their regular route, and the Cycling Index. Keeping with the goal of spatial analysis by proxy, comparing route coverage with other bike parking proxies is also tested. BICY Index 2 Builds an index using Coverage Index, as originally intended, and cyclists perception of the availability of secure bicycle parking. In this way a measure of the quality of destinations is incorporated. BICY Index 2 = Coverage Index * Share of Regular Cyclists Finding Parking in their Regular Travels. The result is then normalized, to create a 0-1 relative value between the places under evaluation. The limitation with using Regular Cyclists Experience Finding Secure Bike Parking on their Most Regular Travels is that those who do not regularly commute are not queried and some of those surely do not exist, due to a lack of bicycle parking therefore we miss a true measure of the availability of bike parking, let alone a comparison with the availability of car parking, as intended for the original BICY Index (BICY Index 1). Comacchio Ferrara Ravenna Coverage Index (Raw) Secure Bicycle Parking BICY Index BICY Index 2, normalized Graz Koper Velenje Budaörs Košice-C Prague 5 Michalovce SNV Alternatively, more info:

149 Comacchio Ferrara Ravenna Bikeways km Coverage Index (Raw) Secure Bicycle Parking BICY Index BICY Index 2, normalized Modal Split (Max Time) Page 149/152 Graz Koper Velenje Budaörs Košice-C Prague 5 Michalovce SNV Figure J.3: Two tables showing data and calculated values for BICY Index 2 (BICY 2). BICY Index 3 Builds an index combining Cycling Index (the Standard Indicator of bikeways per person), with regular commuter cyclists perception of the availability of secure bicycle parking. In this way a measure of the quality of destinations is incorporated. The limitation again is that those who do not regularly commute are not queried and some of those surely do not exist, due to a lack of bicycle parking therefore we miss a true measure of the availability of bike parking. BICY Index 3 = Cycling Index * Share of Regular Cyclists Finding Parking in their Regular Travels. The result is then normalized, to create a 0-1 relative value between the places under evaluation. Comacchio Ferrara Ravenna Graz Bikeways km Cycling Index (Raw) BICY Index 3, normalized Modal Split (Max Time) Košice-C Michalovce E E Koper Velenje Budaörs Prague 5 SNV E Figure J.4: Table showing data and calculated values for BICY Index 3 (BICY 3). Comparing Standard Indicators The considered indicators are, in the first place, a tool to understand the current mobility situation and allow a comparison of the state of bicycle circulation between BICY Project partners. By comparing cycling indicators between partners (and other best-practice cities), it is possible to identify weak points and to deliver numerical evidence for actions to be taken within BICY as well as for future EU projects. The usage of the Common Indicators as a measure of the overall success of actions taken within this project is important, but not the main emphasis. It is usual to make site interviews to demonstrate the effect of local infrastructure changes on users. These interviews will be dealt with in at a later stage of the project.

150 Page 150/152 Figure J.5: Comparison of four primary Common Indicators (all normalized on 0 to 1 scale). Averages were taken of all normalized indicators, and cross-graphed with bicycle mode share. Figure J.6: Comparison of average of all three Standard Indicators with Bicycle Mode Share (all normalized on 0 to 1 scale). Cycling Index compared with Regular Bike Commuters Particularly interesting finding here; the average commute time for a regular cyclist decreases as the index increases. This might be explained by a combination of a faster commute due to more bikeways, or a closer commute due to density; additionally, those who wouldn t have cycled before for short trips may have been convinced. This deserves further investigation.

151 Page 151/152 Figure J.7: Cycling Index compared with Travel Time for Regular Cyclists Cycling Index compared with Regular Bike Commuters Regular bicycle commuters is a different measure of cycling, and one not commonly known let alone used. Thus it is of interest to experiment with it here. However, the preliminary look did not find a strong correlation. Figure J.8: Cycling Index compared with Regular Cyclists Statistical Modeling of Common Indicators Modeling using statistical and other methods is planned for the next project phase, culminating in the Transnational Strategy Report (WP3.4). However, it is outside the scope of the current report.