Bicycle Trip Forecasting Model: Cincinnati

Transcription

1 Bicycle Trip Forecasting Model: Cincinnati Metropolitan Case Study Prepared by: Heng Wei Qingyi Ai Maria F. Ramirez-Bernal Prepared for: The Ohio Department of Transportation, Office of Statewide Planning & Research State Job Number August 2013 Final Report

2 Technical Report Documentation Page 1. Report No. 2. Government Accession No. 3. Recipient's Catalog No. FHWA/OH-2013/12 4. Title and Subtitle 5. Report Date Bicycle Trip Forecasting Model: Cincinnati Metropolitan Case Study August Performing Organization Code 7. Author(s) 8. Performing Organization Report No. Heng Wei (PI) Qingyi Ai Maria F. Ramirez-Bernal 9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) University of Cincinnati 2600 Clifton Ave, Cincinnati, OH Contract or Grant No. SJN Sponsoring Agency Name and Address 13. Type of Report and Period Covered Ohio Department of Transportation 1980 West Broad Street Columbus, Ohio Sponsoring Agency Code 15. Supplementary Notes 16. Abstract The bicycle mode share is still very low in the United States. The bicycle mode share in Ohio is only 0.4%. To encourage more people to choose bicycle as their travel mode, the improvement of conditions for bicyclists has become important for relevant planning and policymaking efforts. A bicycle travel demand model can be a helpful analysis tool for bicycle-related projects. However, challenges remain in the capability of providing adequate and accurate data for bicycle travel demand modeling. In this study, the GPS-based Household Travel Survey (HTS) database for Greater Cincinnati has been adopted as the primary data source to study bicycle travel in the Cincinnati region. Extraction of bicycling facilities data and other required modeling data can be explored with the HTS data. Relevant statistical analysis reveals the characteristics of the extracted bike trips. The multiple regression technique is applied to identify significant variables for bicycle travel demand forecasting. As a result, eight significant variables have been identified and bicycling facilities are found to be vital in attracting bicyclists. The GPS-based HTS data proved to be a reliable and accurate data source for the estimation of travel forecasting models. Finally, a methodology for forecasting bike trips or travel demand is given for the Greater Cincinnati area. 17. Keywords 18. Distribution Statement Bicycle, travel demand forecasting, GPS-based HTS, significant variables, Cincinnati No restrictions. This document is available to the public through the National Technical Information Service, Springfield, Virginia Security Classification (of this report) 20. Security Classification (of this page) 21. No. of Pages 22. Price Unclassified Unclassified 36 Form DOT F (8-72) Reproduction of completed pages authorized

3 BICYCLE TRIP FORECASTING MODEL: CINCINNATI METROPOLITAN CASE STUDY Prepared by: Heng Wei (PI) Qingyi Ai Maria F. Ramirez-Bernal The University of Cincinnati Final Report Date: August 2013 Prepared in cooperation with the Ohio Department of Transportation (ODOT) and the U.S. Department of Transportation (US DOT) Federal Highway Administration (FHWA)

4 Project Final Report on Bicycle Trip Forecasting Model (SJN ) i DISCLAIMER The contents of this report reflect the views of the author(s) who is (are) responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the Ohio Department of Transportation or the Federal Highway Administration. This report does not constitute a standard, specification, or regulation.

5 Project Final Report on Bicycle Trip Forecasting Model (SJN ) ii ACKNOWLEDGMENTS The authors wish to acknowledge the following technical panel members from the Ohio Department of Transportation who have provided assistance throughout the entire project: Rebekah Anderson, ODOT Heather Bowden, ODOT Jeremy Raw, USDOT, FHWA The authors also want to express their sincere thanks to the following experts from the Ohio-Kentucky-Indiana Council of Governments (OKI) who have provided with the GPS-based House Travel Survey (HTS) data and the Bike Route Guide Map, as well as constructive suggestions on applying the data: Andrew Rohne Don Burrell David Shuey Finally, big thanks go to graduate students in the Art-Engine Lab at the University of Cincinnati, Mr. Hao Liu, Mr. Zhuo Yao, and Miss Hui Ren, for their great assistances to data analysis and participations in discussions of the project results.

6 Project Final Report on Bicycle Trip Forecasting Model (SJN ) iii TABLE OF CONTENT CHAPTER 1: INTRODUCTION... 1 CHAPTER 2: RESEARCH OBJECTIVES... 3 CHAPTER 3: GENERAL DESCRIPTION OF RESEARCH BIKE TRIP BOUNDARY IDENTIFICATION OF SIGNIFICANT VARIABLES MODELING PROCEDURE FOR BIKE TRIP FORECASTING ANALYSIS... 6 CHAPTER 4: FINDINGS OF THE RESEARCH DATA COLLECTION AND DESCRIPTION GPS HTS Data Bike Route Guide (BRG) Map Weather Data Census Data CHARACTERISTICS OF BICYCLE TRIPS IN CINCINNATI Trip Purposes Bike Trip Lengths and Times Times of Day Weather Influence Distributions of Age and Gender of Bicyclists SIGNIFICANT VARIABLES OF BICYCLE TRAVEL DEMAND IN CINCINNATI Significant Variables Identified from Previous Studies Selection of Significant Variables using Linear Regression Selection of Significant Variables using Other Methods Finaliz Significant Variables A GENERAL PROCEDURE FOR BICYCLE TRAVEL DEMAND FORECASTING IN CINCINNATI Case Study of Four Typical Areas in Cincinnati Method for Bicycle Trip Forecasting Modeling: Case Study of Cincinnati Area CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS CONCLUSIONS RECOMMENDATIONS CHAPTER 6: RECOMMENDATIONS FOR IMPLEMENTATION OF RESEARCH FINDINGS BIBLIOGRAPHY... 35

7 Project Final Report on Bicycle Trip Forecasting Model (SJN ) iv TABLE OF FIGURES Figure 1. Circle Range Diagram of Bike Trip Boundaries... 4 Figure 2. Trips Displayed in a GIS Map... 8 Figure 3. Bike Lane... 8 Figure 4. Shared Use Path and Side Path... 9 Figure 5. Wide Shoulder and Wide Curb Lane... 9 Figure 6. Signed Bike Route Figure 7. Bike Hills and Bike Rack Figure 8. Trip Length Distribution of HBW and HBS Figure 9. Trip Length Distribution of HBO and NHB Figure 10. Bike Trip Starting Time in One Day Figure 11. Weather Conditions of Bike Trips Figure 12. Bike Trip Distribution under Cold, Moderate, and Hot Weather Figure 13. Percentage of Male and Female Bicyclists Figure 14. Age Distributions of Bicyclists Figure 15. Study Boundary of Each Household Figure 16. Bike Hills in BRG Map Figure 17. Locations of Four Selected Areas in Cincinnati Figure 18. Close-ups of the Four Selected Areas in Cincinnati Figure 19. Relationship between % of Recommended Routes and Bike Trip Rates TABLE OF TABLES Table 1. Sample Data of GPS HTS... 7 Table 2. Sample of Weather Data Table 3. Distribution of Trip Purposes Table 4. Trip Length Distribution Table 5. Distribution of Bike Trip Time Table 6. Distribution of Bike Trip Starting Time Table 7. Bike Trips Generated under Different Temperatures Table 8. Statistical Analysis of Bicyclists Ages Table 9. Age Distributions of Bicyclists Table 10. Significant Variables of Bicycle Travel Demand in Previous Studies Table 11. Possible Significant Variables for Bike Travel Demand in Cincinnati Area Table th Percentile Bike Trip Length Table 13. Independent Variables... 21

8 Project Final Report on Bicycle Trip Forecasting Model (SJN ) v Table 14. Results of Multiple Linear Regression Modeling Table 15. Summary of Bike Hills Table 16. Final Significant Variables Table 17. Bicycle Trip Generation Rates of the Four Selected Areas Table 18. Percentage of Different Bike Routes of the Four Study Areas Table 19. Example of Area Types Table 20. Independent Variables and the Variable Combinations for Different Area Types Table 21. Criteria for Defining Bike Routes... 31

9 Project Final Report on Bicycle Trip Forecasting Model (SJN ) vi LIST OF ABBREVIATIONS 0 C: Celsius 0 F: Fahrenheit BRG: Bike Route Guide CAGIS: Cincinnati Area Geographic Information System CBD: Central Business District d: day DOT: Department of Transportation FHWA: Federal Highway Administration GIS: Geographical Information System GPS: Global Positioning System HB: Home-Based HBO: Home-Based Other HBS: Home-Based School HBW: Home-Based Work HH: Household HTS: Household Travel Survey MPO: Metropolitan Planning Organization NHB: Non-Home-Based NOAA: National Oceanic and Atmospheric Administration ODOT: Ohio Department of Transportation OKI: Ohio-Kentucky-Indiana Council of Governments R 2 : Coefficient of determination sq: square t: temperature USDOT: United States Department of Transportation

10 Project Final Report on Bicycle Trip Forecasting Model (SJN ) vii SI* (MODERN METRIC) CONVERSIONN FACTORS APPROXIMATE CONVERSIONS TO SI UNITS Symbol When You Know Multiply By To Find Symbol LENGTH in inches 25.4 millimeters mm ft feet meters m yd yards meters m mi miles 1.61 kilometers km AREA in 2 square inches square millimeters mm 2 ft 2 square feet square meters m 2 yd 2 square yard square meters m 2 ac acres hectares ha mi 2 square miles 2.59 square kilometers km 2 VOLUME fl oz fluid ounces milliliters ml gal gallons liters L ft 3 cubic feet cubic meters m 3 yd 3 cubic yards cubic meters m 3 NOTE: volumes greater than 1000 L shall be shown in m 3 MASS oz ounces grams g lb pounds kilograms kg T short tons (2000 lb) megagrams (or metric ton ) Mg (or t ) TEMPERATURE (exact degrees) F Fahrenheit 5 (F-32)/9 or (F-32)/1.8 Celsius C ILLUMINATION fc foot-candles lux lx fl foot-lamberts candela/m 2 cd/m 2 FORCE and PRESSURE or STRESS lbf poundforce 4.45 newtons N lbf/in 2 poundforce per square inch 6.89 kilopascals kpa APPROXIMATE CONVERSIONS TO SI UNITS Symbol When You Know Multiply By To Find Symbol LENGTH mm millimeters inches in m meters 3.28 feet ft m meters 1.09 yards yd km kilometers miles mi AREA mm 2 square millimeters square inches in 2 m 2 square meters square feet ft 2 m 2 square meters square yard yd 2 ha hectares 2.47 acres ac km 2 square kilometers square miles mi 2 VOLUME ml milliliters fluid ounces fl oz L liters gallons gal m 3 cubic meters cubic feet ft 3 m 3 cubic meters cubic yards yd 3 MASS g grams ounces oz kg kilograms pounds lb Mg (or t ) megagrams (or metric ton ) short tons (2000 lb) T TEMPERATURE (exact degrees) C Celsius 1.8C+32 Fahrenheit F ILLUMINATION lx lux foot-candles fc cd/m 2 candela/m foot-lamberts fl FORCE and PRESSURE or STRESS N newtons poundforce lbf kpa kilopascals poundforce per square inch lbf/in 2 *SI is the symbol for the International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380. (Revised March 2003)

11 Project Final Report on Bicycle Trip Forecasting Model (SJN ) 1 CHAPTER 1: INTRODUCTION Traffic congestion and traffic-related pollution have been a concern in most cities in the United States (US). Traffic congestion costs people time and exacerbates air pollution. Planners have begun to promote more sustainable means of travel for daily commutes, school, shopping, and other trip purposes. The more sustainable modes are expected to reduce transportation Greenhouse Gases (GHGs) and save energy. The bicycle travel mode has the potential to provide convenient mobility, reduce congestion, improve environmental air quality, and promote public health. A bicycle occupies less space than a standard passenger car on the road, which can potentially relieve traffic congestion and reduce travel time. Meanwhile, bicycling is also a good method of physical excise. These potential benefits of bicycle travel have received more attention over the last decade (US DOT & BTS 2000). However, the bicycle mode share is still very low in the US. Although the bicycle mode share is only 2.4% in the State of Oregon, it is much higher than most other states in the US (Steele, 2010). The State of Ohio s current rate of bicycle and pedestrian travel is even lower. The bike and pedestrian mode shares in Ohio are 0.4% and 7.4%, respectively (Steele, 2010). To encourage more people to choose bicycle as their travel mode, relevant planning and policymaking efforts have raised the need for improving conditions for bicyclists. As bicycle travel demand forecasting models become more advanced, they may have the potential to become a helpful analysis tool for estimating the benefits of a proposed project, planning bicycle or pedestrian paths and networks, and identifying and correcting deficiencies in existing networks based on desired travel patterns and facility characteristics. A Large amount of data is required to estimate a travel demand model, including bicycle trip data, census data, bicycle facilities data, and other supporting data. However, due to some deficiencies and limitations in existing sources for these data, it is still a problem to provide adequate and accurate data for bicycle travel demand modeling (US DOT & BTS, 2000). In this study, the GPS-based Household Travel Survey (HTS) was adopted as the primary data source for the investigation of bicycle travel in the Greater Cincinnati area. The GPS-based HTS was conducted by the Ohio Department of Transportation (ODOT) Research Division in cooperation with the Ohio-Kentucky-Indiana (OKI) Council of Governments (Wargelin, 2012). In the survey, GPS data loggers were equipped to all members of a recruited household over 12 years of age for a three-day recording period. The survey began in August of 2009, and lasted over 12 months. In the GPS HTS data, there are 77,209 recorded trips, in all modes of travel, including auto, bus, bicycle, walk, and others. Of all the recorded trips, only a total of 680 trips were identified as bike trips, or about 0.88% of the total. The GPS HTS database also contains household information, e.g., household size, income, age, gender, vehicle ownership, and bicycle ownership. The bicycle facilities data were acquired from OKI. OKI has integrated bicycle facility information, which includes location and length or number of bike lanes, bike trail or path, bike hills, and bike parking facilities, into a GIS map named the Bike Route Guide (BRG) Map. The BRG Map is the primary source of bicycle facility information for this study. Besides the GPS-based HTS data and BRG Map, the census data and weather data have been collected from the Census Bureau and the National Oceanic and Atmospheric Administration (NOAA). Statistical analyses are conducted to determine the features of bicycle trips generated in the Greater Cincinnati area; for example, the distributions of age and gender of the bicyclists, distributions of bicycle trip lengths, travel time, and times of day. The possible effect of weather is also considered. The HTS recorded that the total number of male bicyclists is slightly larger

12 Project Final Report on Bicycle Trip Forecasting Model (SJN ) 2 than that of female bicyclists, and the majority of the bicyclists are in their 40s. The average bicycle trip length is 1.42 miles, and the average travel time is 10.4 minutes. Over half (54%) the bicycle trips are generated during off-peak hours during the day (9:00 am - 4:00 pm). The findings help researchers better understand the characteristics of bicycle travel in Greater Cincinnati, and provide useful information for the estimation of a bicycle travel demand model. The multiple linear regression technique is employed to identify significant variables correlated to bicycle trip generation in the Cincinnati area. As a result of the multiple linear regression analysis, the following eight (8) variables are identified as significant: number of students in the household, number of workers in the household, and within the household s bicycle trip study boundary - the length of bike lanes, length of bike shared paths, length of bike shared roads, length of signed bike routes, number of bike racks, and area of parkland. In the BRG Map, bicycle routes are also categorized into recommended routes, use with caution routes, and not recommended routes by integrating the bicycle facility information into the route system. It was found that the relationship between bicycle trip generation rates and the percentage of recommended routes is linear. The result implies that bicycle facilities impact bicycle trip generation; more specifically, as the amount of bicycle facilities within a household s study boundary increase, that household is more likely to undertake bicycle trips. In this study, a general procedure for the estimation of bicycle travel demand is developed for the Greater Cincinnati area. Regression models by type of land use (or area type) were developed for the estimation of bicycle trip generation rates. Four area types are defined in this study: university area, downtown or Central Business District (CBD), residential area, and park.

13 Project Final Report on Bicycle Trip Forecasting Model (SJN ) 3 CHAPTER 2: RESEARCH OBJECTIVES The objective of this study is to determine the suitability of the HTS data for bicycle planning purposes, and to determine which factors affect the selection of bicycle as a travel mode in the Greater Cincinnati area. In order to achieve the goal, two tasks are formulated: 1) To extract bicycle trip data from the GPS-based HTS database and other supporting data sources; and 2) To identify significant variables that affect people s selection of bicycle as their transportation mode.

14 Project Final Report on Bicycle Trip Forecasting Model (SJN ) 4 CHAPTER 3: GENERAL DESCRIPTION OF RESEARCH 3.1 Bike Trip Boundary Bicycling is usually used as a short-trip mode. The trip length may vary with different areas. In this study, the HTS database is used to estimate statistical features of the bicycling trip lengths in the Greater Cincinnati area. Within the ArcGIS environment, the spatial boundary of each bike trip can be presented by a circle centered by the subject household s location. The bicycle trip lengths can be extracted from the GPS based HTS data. The 90 th percentile bicycle trip length is used as the radius of the spatial boundary (see the radius of circles as shown by Figure 1). Some roadway conditions that may impact the generation of bicycle trips (such as road slope) and conditions related to bicycle facilities (such as bike lanes and bike parking racks) are illustrated within the circle (as shown by Figure 1). Figure 1. Circle Range Diagram of Bike Trip Boundaries

15 Project Final Report on Bicycle Trip Forecasting Model (SJN ) Identification of Significant Variables The factors that influence a person s selection of the bike travel mode are determined to be significant variables (or contributing factors) if they are found to be statistically significant. Previous studies have suggested that such significant factors may vary between different regions or cities, due to different living habits, characteristics of the road network, types of terrain, etc. (Turner et al., 1997; Dill and Carr, 2003). Contributing factors or significant variables for bike trips are categorized into socioeconomic variables, bicycling facility variables, and environment variables. Socioeconomic variables are referred to as factors depicting household characteristics, such as income, household size, ownership of automobiles and bikes, and employment status. Bicycling facility variables refer to bicycle infrastructural conditions, such as bike lane information, signed bike routes, bicycle parking services, park-and-ride facilities, and grades of the bikeways. Environmental variables include factors that may influence the decision to choose the bike mode and trip length, such as land use type, terrain, temperature and weather conditions, as well as traffic flow and functional classification of the highways that are adjacent to bicycle facilities (Dill and Gliebe, 2008). In this study, the bike trip contributing factors will be considered within a specific spatial distance. Some modeling methods have been applied to estimate trip generation by researchers in previous studies, which include growth-factor modeling, multiple regression, and the crossclassification method (Ortuzar and Willumsen, 1994). The multiple regression technique has been widely used in estimating trip generation. This technique is a modeling method that can be easily understood. In this study, the multiple regression technique is employed to quantify bike trip travel demand in the Greater Cincinnati area. It is used to help identify the significance of the initially identified contributing factors with sample data, and then the significant variables will be selected as the independent variables for the regression model. While the relationship between bike trip generation and some contributing variables may not be linear, we use a scatter plot to investigate whether bike trip generation follows a linear or non-linear relationship with the independent variables. If it proves nonlinear, a transformation will then be needed. Possible transformations could take the form of logarithmic, inversion, quadratic, or exponential expressions. After variable transformation, both the forward selection procedure and backward selection procedure are used to generate the best combination of independent significant variables for bicycle trips. For example, the following steps in the forward selection procedure are included. In the first step, all possible one predictor models are tested to find the one with the smallest p-value. The variable with the smallest p-value indicates that this independent variable has the highest correlation with the dependent variable, and this independent variable will be kept in the model and similarly the second predictor will be found out in the next step. The procedure continues in this manner until the remaining candidate variables have p-values over the threshold, which is 0.10 in this study. The p-value represents the chance that the result is not significant. Normally a p-value of 0.05 is adopted by many studies, however, there are many socioeconomic factors and infrastructure factors involved, and significance was hard to come by in this analysis model. Accordingly, the level was lowered to 0.10 to obtain reasonable results. At the same time, the estimators of all significant variables are determined when the procedure discussed above is completed. In the backward procedure, the steps are similar to that of forward procedure. The main difference is that candidate variables are added into the model one by one to test their p-values.

16 Project Final Report on Bicycle Trip Forecasting Model (SJN ) Modeling Procedure for Bike Trip Forecasting Analysis After identification of significant variables, several typical land-use areas are selected as the case study sites in the Greater Cincinnati area. The selected areas include: university area, downtown, high income residential area, and low or medium income area. The bike trip generation rates and other bike trip features are analyzed in this study to reveal the quantitative relationship between number of bike trips and the contributing factors in different areas.

17 Project Final Report on Bicycle Trip Forecasting Model (SJN ) 7 CHAPTER 4: FINDINGS OF THE RESEARCH 4.1 Data Collection and Description In this study, the following types of data have been collected for the analysis: GPS HTS data, bike route map, census data, and weather data GPS HTS Data In 2009, an exclusively GPS-based HTS was conducted by the ODOT Research Division in cooperation with the OKI (Wargelin, L. et al., 2012). In the survey, a GPS device was equipped to all members of a recruited household over 12 years of age for a three-day recording period. The survey began in August of 2009, and lasted over 12 months. The survey includes 2059 completed households which are proportionally distributed. The GPS devices were carried by the survey participants and recorded their locations at an update interval of 1 second. Thus, for each surveyed trip, some detailed information can be extracted from the GPS data, including locations of origins and destinations, paths, travel speeds, etc. The survey data also include the household s information, such as income, size, age, number of students, number of workers, vehicle ownership, and bicycle ownership. Sample data records from the GPS HTS are illustrated in Table 1. Table 1. Sample Data of GPS HTS HHID HHSIZE WORKERS STUDENTS BICYC INCOME As the GPS data contain the trajectory of each trip in the format of x and y coordinates, each trip can be coded onto a GIS map to show its spatial distribution. Figure 2 illustrates examples of trips in the GIS map. The GPS HTS data samples involve a total of 77,209 trips, in modes of auto, bus, bicycle, and walk. There are 680 bike trips in total, 0.88% of all the trips Bike Route Guide (BRG) Map The BRG Map, obtained from OKI, contains information about bike routes, bike lanes, bike parking facilities, and bike hills, etc. In the BRG Map, the definitions of specific bike facilities are briefly described as follows: (1) Bike lane: striped bike lanes are established with appropriate pavement markings and signing along higher volume streets (Figure 3);

18 Project Final Report on Bicycle Trip Forecasting Model (SJN ) 8 (2) Shared use path or trail: provides its own right of way separate from the highway system. It is intended to be used by cyclists, walkers, runners, and wheelchair users, etc. (Figure 4). (3) Side path: along roads within the street right of way. Separated from traffic (Figure 4). (4) Shared roadway (no bikeway designation): streets and highways without bikeway designations. Usually these streets and roads are with low speeds and traffic volumes. Figure 2. Trips Displayed in a GIS Map (Source: Figure 3. Bike Lane

19 Project Final Report on Bicycle Trip Forecasting Model (SJN ) 9 (5) Wide curb lane; wide shoulder (Figure 5). (6) Signed bike route: streets may be signed to indicate that there are particular advantages to these routes (Figure 6). (7) Steep slope for bicyclist: the slopes which are very hard for bicyclists to climb, and they can always remember the experiences. So these slopes are also called memorable hills (Figure 7). OKI relies on input from Cincinnati Cycle Club members to define the memorable hills. (8) Bike racks: the facility for bike parking. Better bike parking facilities may attract more bike riders (Figure 7). Shared Use Path Side Path (Source: FHWA (a), 1999; Source: Figure 4. Shared Use Path and Side Path (Source: Figure 5. Wide Shoulder and Wide Curb Lane

20 Project Final Report on Bicycle Trip Forecasting Model (SJN ) 10 (Source: ) Figure 6. Signed Bike Route (Source: myfountainsquare.com) Figure 7. Bike Hills and Bike Rack Weather Data Previous studies have shown that people s choice of a bicycle mode would be affected by weather conditions (Ashley et al., 1989; Nelson et al., 1997; Pucher et al. 1999; Nankervis, 1999). The OKI s GPS HTS lasted over 12 months, which resulted in the inclusion of weather conditions for all four seasons, including rain, snow, hot days or cold days. The weather data are collected from the NOAA website, and contain daily weather information for the study area, such as temperature, wind speed, and precipitation. A sample of the weather data appended to the household data is shown in Table 2.

21 Project Final Report on Bicycle Trip Forecasting Model (SJN ) 11 Table 2. Sample of Weather Data HHID TravelDate Temp ( 0 F) WindSpd (mi/h) Precip (Inch) /12/ /16/ /17/ /16/ /17/ /18/ /17/ Census Data The Census data are collected from the Cincinnati Area Geographic Information System (CAGIS) and OKI. The Census data include socioeconomic information, public facility, business, and land use, etc. 4.2 Characteristics of Bicycle Trips in Cincinnati Trip Purposes In this study, the bike trips are divided into two categories: Home-based (HB) trips and Non-home-based (NHB) trips. A HB trip is a trip where the home of the bike trip maker is either the origin or the destination of the journey, and a NHB trip is a trip where neither end of the trip is the home of the trip maker. The HB trips are further divided into three sub-categories: homebased work (HBW), home-based school (HBS), and home-based other (HBO) (which includes shopping, bank, post office, recreation, etc.). The sample distributions of HBW, HBS, HBO, and NHB are shown in Table 3. No. of Trips Table 3. Distribution of Trip Purposes Total HBW HBS HBO NHB Trips Cannot be identified Bike Trip Lengths and Times Trip length is the total distance between the origin and the destination of a trip. The average trip length of all bike trips is 1.42 miles. The average trip length of HBW trips is 2.03 miles, which is the longest for all trip purposes. Those of HBS, HBO, and NHB are 1.15 miles, 1.55 miles, and 1.35 miles, respectively. The results of the statistical analysis are shown in Table 4 and by Figure 8 and Figure 9.

22 Project Final Report on Bicycle Trip Forecasting Model (SJN ) 12 Table 4. Trip Length Distribution Overall Trips HBW HBS HBO NHB No. of Trips Mean (mile) Median (mile) Std. Dev No. of Trips HBW HBS Trip length (mile) Figure 8. Trip Length Distribution of HBW and HBS No. of Trips HBO NHB Trip length (mile) Figure 9. Trip Length Distribution of HBO and NHB

23 Project Final Report on Bicycle Trip Forecasting Model (SJN ) 13 Trip time is the travel time of a trip from its origin to destination. As shown in Table 5, the average trip time of all bike trips is 10.4 minutes, and HBO and HBW trips have the longest trip lengths and also longest trip times (13.8 minutes and 13.5 minutes, respectively). The average trip time of NHB trips is the shortest (8.9 minutes). Table 5. Distribution of Bike Trip Time Overall Trips HBW HBS HBO NHB No. of Trips Mean (min) Median (min) Std. Dev Times of Day The distribution of starting times of bicycle trips is illustrated in Table 6 and by Figure 10. The daytime is divided into four parts: morning peak hours (from 6:00 am to 8:59 am), daytime off peak hours (from 9:00 am to 3:59 pm), evening peak hours (from 4:00 pm to 7:00 pm), and other time (from 7:00pm to 5:59 am). Fifty-four percent of bike trips begin during the daytime off peak hours, i.e. between 9:00 am to 3:59 pm, and 19% of bike trips begin during evening peak hours. Only 13.8% of bike trips begin during morning peak hours, and the rest (13.2%) begin during 7:00pm to 5:59 am. Table 6. Distribution of Bike Trip Starting Time Trip Start Time No. of bike trips % of bike trips 6:00 am 8:59 am :00 am 3:59 pm :00 pm 6:59 pm Other time Total

24 Project Final Report on Bicycle Trip Forecasting Model (SJN ) Times of Day before 6 am 6 am 9 am 9 am 4 pm 4 pm 7 pm after 7 pm Figure 10. Bike Trip Starting Time in One Day Weather Influence Traveling by bike is much different from motorized vehicles like car or bus. Bicyclists are exposed to rain, hot, or cold temperature directly, and bad weather may prevent them from choosing bike as a transportation means. In this study, the weather conditions are classified as good weather and bad weather. Good Weather refers to a clear day with no precipitation, and not windy; otherwise, it is considered Bad Weather. In the 680 bike trips in the dataset, recorded weather information is not available for 22 trips. Of the 658 bike trips with available weather conditions, 358 trips on days classified as Good Weather, while 303 trips occurred on days classified as Bad Weather (Figure 11). 46% Good Weather 54% Bad Weather Figure 11. Weather Conditions of Bike Trips

25 Project Final Report on Bicycle Trip Forecasting Model (SJN ) 15 Temperature is another environmental factor that affects a person s choice of bicycling mode. Temperatures are classified into five levels: (1) t 32 0 F (0 0 C), (2) 32 0 F (0 0 C) < t 50 0 F (10 0 C), (3) 50 0 F (10 0 C) < t 70 0 F (21 0 C), (4) 70 0 F (21 0 C) < t 90 0 F (32 0 C), and (5) t > 90 0 F (32 0 C). The temperature is considered very cold when the temperature is lower than 32 0 F, and very hot when the temperature is higher than 90 0 F. The remainder is considered to be moderate. The database showed that 4.5% of bike trips were undertaken on very cold days, and 1.4% on very hot days. As shown by Figure 12, 94.1% of sample bike trips occurred under moderate temperatures. 43.7% of the total number of trips occurred during the temperature range from 70 0 F (21 0 C) to 90 0 F (32 0 C), and 32.5% occurred under temperatures ranging from 50 0 F (10 0 C) to 70 0 F (21 0 C), as shown in Table 7. Table 7. Bike Trips Generated under Different Temperatures Weather Conditions % of trips Number of trips t * 32 0 F (0 0 C) 4.5% F (0 0 C) < t 50 0 F (10 0 C) 17.9% F (10 0 C) < t 70 0 F (21 0 C) 32.5% F (21 0 C) < t 90 0 F (32 0 C) 43.7% 289 t > 90 0 F (32 0 C) 1.4% 9 * t: temperature. 1.40% 4.50% cold moderate hot 94.10% Figure 12. Bike Trip Distribution under Cold, Moderate, and Hot Weather

26 Project Final Report on Bicycle Trip Forecasting Model (SJN ) Distributions of Age and Gender of Bicyclists The GPS HTS data includes 680 bike trips, undertaken by 183 (53.5%) male bicyclists and 159 (46.4%) female bicyclists, as shown by Figure % male 53.50% female Figure 13. Percentage of Male and Female Bicyclists The statistical analysis results of bicyclists ages are listed in Table 8. The average age of bicyclists is about 43, either male or female. The age distributions of both male and female bicyclists are shown in Table 9 and Figure 14. Males aged 45 to 54 comprise the largest cohort (23.5%) of male bike riders in this survey; 71% of male bicyclists are in the age range from 25 to 64. Similarly, females aged 45 to 54 comprise the largest female cohort (25.8%), however the percentage of female bicyclists aged between 35 to 44 is also high (21.4%); 73.6% of female bicyclists are aged from 25 to 64. A surprising finding from the data is that young people aged between 13 and 24 are not well represented in the sample. Those aged between 13 and 15 comprise 9% of the total sample, but bike mode share drops significantly for those aged between 16 and 24. It must be pointed out that the above statistical phenomena are revealed primarily as a reference, rather than a conclusion, due to limited number of samples available from the HTS data source. Table 8. Statistical Analysis of Bicyclists Ages Male Female Male & Female Mean Standard Error Median Mode Standard Deviation Kurtosis Skewness Count

27 Project Final Report on Bicycle Trip Forecasting Model (SJN ) 17 Table 9. Age Distributions of Bicyclists Male Female Total Age range Frequency % Frequency % Frequency % younger than % 0 0.0% 0 0.0% 13 to % % % 16 to % 7 4.4% % % (Census)* 22.0% 18 to % 9 5.7% % 14.6% 25 to % % % 16.6% 35 to % % % 45 to % % % 55 to % % % 65 to % % % 75 to % 3 1.9% 6 1.8% 85 and over 1 0.5% 0 0.0% 1 0.3% *2010 Census Data, Cincinnati 36.0% 10.8% Percentage 30.0% 25.0% 20.0% 15.0% 10.0% male+female male female 5.0% 0.0% Age Figure 14. Age Distributions of Bicyclists

28 Project Final Report on Bicycle Trip Forecasting Model (SJN ) Significant Variables of Bicycle Travel Demand in Cincinnati Significant Variables Identified from Previous Studies Before estimating the bicycle travel demand model, it is necessary to identify what factors affect potential bicyclists to choose the bike mode or not. Previous studies suggested a variety of possible factors affecting people s decision to use bikes or not, which are listed in Table 10. These significant factors include employment population, number of students in middle school or college, household income, bicycling facilities, weather conditions and terrain. The bicycling facilities are viewed among the most important factors. For example, the more bike lanes, bike pathway, and bike parking facilities, the more bicycle trips will be generated to the streets. Table 10. Significant Variables of Bicycle Travel Demand in Previous Studies Category Identified Significant factors Researchers Employment Rhode Island Department of Transportation, 1982; Ridgway, 1995; NCTCOG, 1996; School enrollment Rhode Island Department of Transportation, 1982; Landis, 1996; Ridgway, 1995; Nelson et al., 1997 Socioeconomic population Rhode Island Department of Transportation, 1982; NCTCOG, 1996; Ridgway, 1995; Barnes et al., 2005 Household income NCTCOG, 1996; Ridgway, 1995; Pucher et al. 1999; Pucher et al Bicycling facility Ashley et al., 1989; Pucher et al. 2006; Stinson et Bike pathway/bike al. 2003; Nelson et al., 1997; Tilahun, 2007; lane/bike shared Pucher et al. 1999; Dill, J. et al. 2003; Badgett, lane/bike parking 1994; Barnes et al., 2005, Borach et al., 2010; facilities, etc. Xing, 2012 Land use NCTCOG, 1996; Landis, 1996; Ridgway, 1995 Environment Weather conditions Ashley et al., 1989; Nelson et al., 1997; Pucher et al. 1999; Nankervis, 1999 Traffic volume on street Stinson et al. 2003; Borach et al., 2010 Terrain Temperature Nelson et al., 1997 Topographical factors Ashley et al., 1989; Nelson et al., 1997; Borach et al., 2010; Xing, Selection of Significant Variables using Linear Regression As discussed earlier, some of the significant factors for bike travel demand identified by other researchers have been listed in Table 10. In order to reveal what factors affect the potential

29 Project Final Report on Bicycle Trip Forecasting Model (SJN ) 19 bicyclists in Cincinnati, possible significant factors are selected based on previous studies while considering the features of Cincinnati and available data; potential factors are listed in Table 11. Table 11. Possible Significant Variables for Bike Travel Demand in Cincinnati Area Variable Will be Variable Type adopted? Reasons No. of student in HH Yes No. of worker in HH Yes Socio- No. of vehicle in HH Yes economic No. of bicycle in HH Yes Data Available Household income Yes Road classification No No obvious relationship found in previous studies, and no clear implication from survey Facility Facilities for bicycle: bike Factor influencing the choice of lane, signed bike route, and Yes bike with support of data available bike parking facilities, etc. Terrain Steep slope Yes Meaningful to bike trip & datasets available Terrain type No Too general and not accurate enough Temperature Yes Weather condition Yes Environment Land use: available services Data available (shop, post office and bank, Yes etc.), park, etc. Individual perception Attitude Safety concerns No No No relevant survey data available Table th Percentile Bike Trip Length Overall Trips HBW HBS HBO NHB 90 th Trip Length (mile) As the distance of most bike trips is fairly short, the bicycle travel variables considered in this study will be considered within a certain spatial distance. The spatial boundary of each trip is represented by a circle which is centered by the subject household s location. The 90 th percentile bicycle trip length is used as the radius of the circle. The potential variables, such as bike lanes, shared bike paths, number of bike racks, and number of business, are summed within the household s boundary in GIS using the spatial analysis function. The 90 th percentile bicycle trip length of each trip category is listed in Table 12. As discussed earlier, the numbers of trips in the HTS of the three home-based trip purposes (i.e. HBW, HBS, HBO trips) are 17, 19, and 195, respectively. Therefore, the sample

30 Project Final Report on Bicycle Trip Forecasting Model (SJN ) 20 sizes of HBW and HBS trips are too small to possess statistical significance. Only the HBO trips were used to identify significant variables for bike travel demand in the Cincinnati area. The study boundary of each household that made HBO trips by bicycle is illustrated by Figure 15. The radius of circle is the 90th length of HBO, i.e., 3.7 miles. Figure 15. Study Boundary of Each Household The OKI BRG Map is added into the GIS map, as illustrated by Figure 15. Using the spatial analysis function, the length of bike lanes, shared bike paths, and the number of bike racks which are located within the study boundary, can be calculated. At the same time, the number of businesses and the area of parking places within the study boundary, can also be calculated. As mentioned earlier, the multiple linear regression method is applied to identify significant variables; its general form is expressed as the following equation: n Y a0 ai xi (1) i 1 Where, Y = number of bike trips per household per day; a0 = intercept; ai = estimator for xi; and xi = independent variables. Table 13 lists 16 independent variables that are tested in the multiple regression model.

31 Project Final Report on Bicycle Trip Forecasting Model (SJN ) 21 Table 13. Independent Variables Variable Type X Variable X 1 No. of student in HH X 2 No. of worker in HH Socioeconomic X 3 No. of vehicle in HH X 4 No. of bicycle in HH X 5 Household income X 6 Length of bike lanes X 7 Length of shared paths Bicycling Facility X 8 Length of shared roads X 9 Length of signed bike routes X 10 Length of steep slopes X 11 No. of bike parking facilities X 12 Temperature X 13 Wind speed Environment X 14 Precipitation X 15 No. of services (shop, post office and bank, etc.) Area of parkland X 16 The results of the multiple regression estimations are listed in Table 14. The significant level is α=0.1, and when the p value is less than α=0.1, the corresponding independent variable is considered statistically significant. During the analysis, the relationship between bike trips and the number of workers in the household (HHworker) is identified as nonlinear by using the scatter plot, which means a transformation is needed. The quadratic formation was adopted after several transformations were tested. So the relationship between the number of bicycle trips and square of the number of workers in the household is linear. Based on the multiple linear regression outputs, eight of the candidate variables are identified as significant variables for the bicycle travel demand model. They are: number of students in the household (HHstu), number of workers in the household (HHworkersq2), length of bike lanes (L_BL), length of bike shared paths (L_sharedPath), length of bike shared roads (L_sharedRd), length of signed bike routes (L_SignedBikeRoute), number of bike racks (No_BRack), and the area of parkland (A_Park) within the study boundary. Household with students are more likely to generate bicycle trips. It is noticed that the square of Household Workers has the estimator of , meaning that as household workers increase, the less likely that household is to make bike trips. It implies that in Cincinnati, households that have a higher number of workers are not as likely to make a trip by bike as those with fewer workers. Household income is not identified as a significant variable, which is consistent with some findings of previous studies (Ashley et al., 1989; Stinson et al. 2003; Nelson et al., 1997; and Tilahun, 2007).

32 Project Final Report on Bicycle Trip Forecasting Model (SJN ) 22 Table 14. Results of Multiple Linear Regression Modeling Variable X Estimate Error t Value Pr> t Type II SS Intercept < HHstu X Hhworkersq2 (X 2 ) L_BL X L_sharedPath X L_sharedRd X L_SignedBikeRoute X No_BRack X A_Park X R The final regression model of bike travel demand in Cincinnati is: Y = X (X 2 ) X X X X X X 8 (2) Where, Y = number of bike trips per household per day; X 1 = number of students in household; X 2 = number of workers in household; X 3 = length of bike lanes within the study boundary (miles/sq miles); X 4 = length of bike shared paths within the study boundary (miles/sq miles); X 5 = length of bike shared roads within the study boundary (miles/sq miles); X 6 = length of signed bike routes within the study boundary (miles/sq miles); X 7 = number of bike racks within the study boundary; X 8 = area of parkland within the study boundary (sq miles) Selection of Significant Variables using Other Methods In the last session, the length of steep slope was not identified as a significant variable with the application of the multiple regression method. However, previous studies indicated that terrain, especially very rolling terrain, would greatly impact the decision to choose bicycle (Ashley et al., 1989; Nelson et al., 1997). The Greater Cincinnati area has a rolling terrain where many long and steep road slopes exist. It is reasonably assumed that those steep slopes should be a factor influencing the decision to use the bike mode for travel. In this study, due to the unavailability of sufficient data, the significance of long and steep slopes could not be statistically verified as the sample data was statistically small. Thus, a spatial analysis in ArcGIS was conducted as a supplementary approach to further investigate if the terrain in Cincinnati affects bike trips. In the OKI s BRG map, bike hills are defined as long and steep slopes for bicyclists. Within the ArcGIS environment, the GPS survey data of bike trips and bike hills data can be analyzed by using the spatial analysis function in GIS to calculate their intersection. For each bike trip, it can be easily determined if the route of the trip contains one or more segments of bike hills. The red lines in Figure16 represent bike hills in the OKI BRG Map. There are a total of 680 observed bike trips from the survey datasets in this study. Only 27 out of the 680 bike

33 Project Final Report on Bicycle Trip Forecasting Model (SJN ) 23 trips (4% of the sample) involved bike hills on their routes. These 27 bike trips have a total length of 16.9 miles, while the total length of all 680 bike trips is miles. The bike miles from trips whose routes include bike hills comprise only 1.7% of the total bike miles of travel from the HTS dataset. The total length of bike hills in Cincinnati is miles, and the total length of bike routes in Cincinnati is 4,884.5 miles. Thus, the total length of bike hills is 6.1% of the total length of bike routes in Cincinnati. Table 15 gives a summary of the above statistical figures. As shown in Table 15, despite 6.1% of bike paths in Cincinnati being considered as bike hills, only 1.7% of bike trips involve paths using the bike hills. That implies that in reality the bicyclists select their path in an attempt to avoid bike hills in their trips. On the other hand, although the study is unable to show bike hills within the household s study area as statistically significant in the regression analysis, many previous studies have suggested that bike hills are a significant factor. Therefore, the bike hills factor is considered as one of the significant factors influencing the bike mode choice. Figure 16. Bike Hills in BRG Map

34 Project Final Report on Bicycle Trip Forecasting Model (SJN ) 24 Table 15. Summary of Bike Hills No. of bike trips whose route includes bike hills 27 Total No. of bike trips 680 percentage 4% Total length of bike trips whose route includes bike hills (miles) 16.9 Total length of bike trips (miles) percentage 1.7% Total path length of bike hills within Cincinnati (miles) Total path length of bike routes within Cincinnati (miles) 4,884.5 percentage 6.1% Finaliz Significant Variables Based on the above analysis, a total of 9 significant variables are accepted for the bike travel demand model in the case study of the Greater Cincinnati area. The final list of significant variables is shown in Table 16. Variable Type Socioeconomic Facility Terrain Environment Table 16. Final Significant Variables Variable No. of students in HH No. of workers in HH Length of bike lanes Length of shared paths Length of shared roads Length of signed bike routes No. of bike parking facilities Length of steep slopes (Bike hills) Area of parkland 4.4 A General Procedure for Bicycle Travel Demand Forecasting in Cincinnati Case Study of Four Typical Areas in Cincinnati In this chapter, four typical area types in Cincinnati are selected to perform a case study for modeling the bike trip generation rates and other bike trip features relevant to the relationship between bike trips and the identified contributing factors in different types of areas, as shown by Figure 17. The selected area types include university area, downtown area, high income residential area, and low or medium income area: University of Cincinnati (UC) area: large amount of students and average income is low;

35 Project Final Report on Bicycle Trip Forecasting Model (SJN ) 25 Downtown Cincinnati: typical CBD area, with high-density of business services; Mason, OH: suburban residential area with high income; Trenton, OH: residential area, medium income. Figure 17. Locations of Four Selected Areas in Cincinnati Using the Census data about the OKI region, the total number of households and population of each of the selected areas can be calculated, as shown in Table 17 and by Figure 18. Table 17. Bicycle Trip Generation Rates of the Four Selected Areas UC Downtown Trenton Mason No. of Trips No. of HH Total Pop No. of Trips/HH/D No. of Trips/Person/D It can be seen that Trenton has the highest bike trip generation rate of 0.50 bike trips/hh/d, while downtown Cincinnati has the trip generation rate of 0.31 bike trips/hh/d.

36 Project Final Report on Bicycle Trip Forecasting Model (SJN ) 26 Mason has the lowest rate of 0.03 bike trips/hh/d, and the University of Cincinnati has the rate of 0.09 trips/hh/d. The trip generation rate is calculated as follows: No. of bike trips per day in the area sampling rate bike trip generation rate (3) No. of household in the area Where, No. of bike trips per day in the area: calculated from HTS data; Sampling rate: determined according to OKI s report; and No. of households in the area: obtained from TAZs information. Note: green line - recommended route; blue line - use with caution; red line - not recommended; black line - TAZ boundary Figure 18. Close-ups of the Four Selected Areas in Cincinnati As the selected areas are relatively small, for each selected area in the OKI s BRG Map, there is no adequate detailed information about bike lanes, shared bike paths, or shared bike roads. However, the BRG Map provides other bike route system information: three category bike route system. In this system, all roads in the road network of OKI which could be used by the bicyclists (e.g., the interstate highways cannot be used by bicyclists, so they are not included in this system) are classified into three categories for bike use: recommended, use with