Gdynia s bicycle model

Transcription

1 Gdynia s bicycle model Problems related to cycling network and demand modelling Michał Miszewski, city of Gdynia

2 Development of tool for transport analysis - MST Statistical data (baseline and forecasts) - Demographics - Economical (economic development, unemployment) - Land use Street network parameters - Public Transport - Private Transport - Bicycle trips and network Stated Preference Survey Traffic counts PT passengers counts Travel times measures (to calibrate and verify models) Four stage model - Trip Generation - Trip Distribution - Modal split/shift models - Assignment/simulation - Trip matrices - Traffic forecasts - Indicators

3 3 Cycling network Coding a macroscopic cycling model of the city Developement of present transport model Addition of a new means of transport - bicycle Admission of cycling on majority of roads Marking the cycling infrastructure available in a whole city

4 4 Detailing the network Precise definition of the links available for cycling Location separeted cycle path, shared bike-pedestrian path, bike lane (contralane), mixed with other traffic. Pavement type asphalt, paved curb, sidewalk, cobblestone, land path, stairs Gradient (%) Main goal calculate speed and mpedance/utility for different connections

5 5 Slopes Forest paths

6 Speed [km/h] 6 Ride speed depending on the link gradient Survey Formula Slope [%] IF([SLOPE]>0,6+15.5*EXP(-0.25*[SLOPE]), *EXP(0.25*[SLOPE]))

7 7 Demand modelling Data gathering for modeling purposes Over 1300 reviews of cycling infrastructure from participants of local Cycling to work campaign Preparation of two types of measurements (realized in May/June 2016): Counting cyclists on over 40 crossroads Surveys conducted on almost 30 crossroads GPS data from European Cycling Challenge

8 8 Data processing Biannual transport survey (conducted by local public transport authority). Used to calibrate, verify the sum of all travel arrangements and the modal split. Data collected during European Cycling Challenge 2016 and 2017 (GPS track of all trips made within competition). Used to verify bicycle traffic dispersion, calibration and speed verification. Data collected during local cycling competition for companies (general information about all trips made within the competition). Necessary data for parameterisation of section types and selection of improvements variants.

9 9 Data processing Cyclist traffic research in key city locations along with surveys (dedicated for FLOW). Used for calibration, travel matrix verification, travel motivation, cycling intensity and parameterisation of sections, as well as to choose the improvement options. Public and private transport traffic parameters taken from existing transport models supported with data from TRISTAR system.

10 10 Data processing Data collected during the surveys was processed in order to build an origin-destination matrix. First, the journey database has been designed and filled. All journeys are connected with attributes describing: start time of the journey, the origin and destination zones, the journey purpose.

11 11 Integration of new transport modes Modal split and shift model Procedure to distribute journeys between three transport modes private transport (cars), public transport (buses, trolleybuses, trains) and cycling. Utility comparison based on perceived journey time: Private transport Volume-Delay functions, Public transport actual ride time, waiting time, transfers, Cycling actual ride time, quality factors. This part of modelling plays key role in assesing how planned changes in cycling infrastructure would make cycling more attractive.

12 12 Bicycle traffic distribution Calculation of the bicycle traffic flow has been done by the stochastic assignment. This kind of model includes a number of factors not all due to randomness, such as: Road users do not have full knowledge about the traffic network, which means they do not choose routes rationally according to their perceived preferences, different routes are often chosen due to variation, cyclists travel times along different paths may vary from day-to-day due to other road users, traffic lights, different degrees of congestion, the weather, etc., different riding habits make different paths optimal for cyclists. cyclists may have different riding preferences and tolerance for bad weather conditions.

13 13 Running the model Analyse variants of cycling improvements Macroscopic model is going to be used for verifying different scenarios describing the potential development of the city cycling network. Gdynia has several cycling projects that may be fulfilled in the future. In the FLOW project, six scenarios were analyzed. The results enabled not only the assessment of planned changes utility in the transport system but also the verification how the model parameters affect the output.

14 14 Scenario 1 new railway overpass

15 15 Scenario 2 connection over port canal

16 16 Scenario 3 filling the gaps in cycle network city south

17 17 Scenario 4 seaside path in Orłowo

18 18 Scenario 5 17th December Avenue new path in park

19 19 Scenario 6 filling the gaps in cycle network city centre

20 20 Result comparison Scenario Traffic demand [cyclists/hour] Traffic demand increase [%] Travel time benefit [%] Cycling network performance [pers-h] [pers-km] Base , ,11 S ,02 0,03% 27, ,06 S ,67 0,03% 27, ,50 S ,43 0,01% 27, ,53 S ,02 0,02% 26, ,07 S ,61 0,03% 27, ,02 S ,55 0,00% 27, ,58

21 21 Conclusion Multimodal transport modelling including cycling needs a lot of different data. Model including very precisely projected network regarding type of surface, separation from other traffic and path steepness can be useful to predict route choice and travel time using bicycle. Further research on modal shift models implementation should be done (eg. weather conditions influence, seasonality). Need for cyclical surveys in smaller transport regions for more accurate capture of cycling trips. Results show that every cycling network improvement has positive impact on whole transport system, but the most efficient is creating new connections.

22 Thank you for your attention! Michał Miszewski, City of Gdynia