Exploring factors affecting route choice of cyclists: A novel varying-contiguity spatially lagged exogenous modeling approach

Nick van Nijen

University of Twente

M. Baran Ulak

University of Twente

Sander Veenstra

Witteveen+Bos

Karst Geurs

University of Twente

DOI: https://doi.org/10.5198/jtlu.2024.2452

Keywords: Bicycle route choice, Varying-contiguity spatially lagged exogenous model, Bicycle infrastructure, Land use


Abstract

Cycling is one of the main transport modes and cycling infrastructure is strongly embedded in transport infrastructure in the Netherlands. Nonetheless, the bicycle network still undergoes frequent improvements and expansions. One of the critical elements in deciding on improvements and expansions is to understand the route choice of cyclists, which helps identify bottlenecks in bicycle flows and substantiate the need for new bicycle infrastructure. Yet, the factors affecting the route choice of cyclists are still not fully understood. To address this, we develop a varying-contiguity spatially lagged exogenous (VCSLX) model and analyze the probability of a cyclist choosing a certain segment based not only on the characteristics of that segment but also considering the characteristics of its neighbors along a route. Characteristics that are included in this study are the presence of bicycle infrastructure, traffic control installations and artificial lighting, as well as pavement type, bicycle and motorized-vehicle volumes and different land-use zones. The model involves the analysis of the observed routes extracted from cycling trajectories from Fietstelweek data, as well as corresponding hypothetical shortest path routes identified from the origin-destinations of the observed trips and the cycling network. The results of the study can help to understand the factors convincing cyclists to deviate from the shortest possible routes. The study contributes to the current literature by focusing on the underexplored aspect of spatial dependencies between route segments in the route choice of cyclists.


References

Alattar, M. A., Cottrill, C., & Beecroft, M. (2021). Modelling cyclists’ route choice using Strava and OSMnx: A case study of the City of Glasgow. Transportation Research Interdisciplinary Perspectives, 9, 100301. https://doi.org/10.1016/J.TRIP.2021.100301

Asadi, M., Ulak, M. B., Geurs, K. T., Weijermars, W., & Schepers, P. (2022). A comprehensive analysis of the relationships between the built environment and traffic safety in the Dutch urban areas. Accident Analysis & Prevention, 172, 106683. https://doi.org/10.1016/J.AAP.2022.106683

Bernardi, S., Geurs, K., & Puello, L. L. P. (2018). Modelling route choice of Dutch cyclists using smartphone data. Journal of Transport and Land Use, 11(1), 883–900. https://doi.org/10.5198/JTLU.2018.1143

Broach, J., & Dill, J. (2016). Using predicted bicyclist and pedestrian route choice to enhance mode choice models. Transportation Research Record, 2564, 52–59. https://doi.org/10.3141/2564-06

Broach, J., Dill, J., & Gliebe, J. (2012). Where do cyclists ride? A route choice model developed with revealed preference GPS data. Transportation Research Part A: Policy and Practice, 46(10), 1730–1740. https://doi.org/10.1016/J.TRA.2012.07.005

Campos-Sánchez, F. S., Valenzuela-Montes, L. M., & Abarca-Álvarez, F. J. (2019). Evidence of green areas, cycle infrastructure and attractive destinations working together in development on urban cycling. Sustainability, 11(17), 4730. https://doi.org/10.3390/SU11174730

CBS. (2017). Onderzoek verplaatsingen in Nederland (OViN) 2016 plausibiliteitsrapportage. Retrieved from https://www.cbs.nl/-/media/_pdf/2018/28/2017ep32-plausibiliteitsrapportage-ovin-2016.pdf

CBS. (2022). Hoeveel fietsen inwoners van Nederland? The Hague, Netherlands: Centraal Bureau Voor de Statistiek. https://www.cbs.nl/nl-nl/visualisaties/verkeer-en-vervoer/personen/fietsen

CBS. (2023a). Inwoners per gemeente. The Hague, Netherlands: Centraal Bureau Voor de Statistiek. https://www.cbs.nl/nl-nl/visualisaties/dashboard-bevolking/regionaal/inwoners

CBS. (2023b, December 15). Kerncijfers wijken en buurten - Dataset of 2022. The Hague, Netherlands: Centraal Bureau Voor de Statistiek. https://www.cbs.nl/nl-nl/maatwerk/2023/50/kerncijfers-wijken-en-buurten-2022

Chen, P. (2016). Built environment effects on bicyclists’ route preferences: A GPS data analysis. In Bicycling and the built environment: Route choice and road safety (pp. 19–54). Seattle, WA: University of Washington.

De Jong, T., Böcker, L., & Weber, C. (2023). Road infrastructures, spatial surroundings, and the demand and route choices for cycling: Evidence from a GPS-based mode detection study from Oslo, Norway. Environment and Planning B: Urban Analytics and City Science, 50(8), 2133–2150. https://doi.org/10.1177/23998083221141431/FORMAT/EPUB

Elhorst, J. P., & Vega, S. H. (2017). The SLX model: Extensions and the sensitivity of spatial spillovers to W. Papeles de Economía Española, 152, 34–50.

Ensing, N., & Janssen, K. (2020). Actieplan Fiets 2020-2022. Retrieved from https://gemeentebestuur.haarlem.nl/bestuurlijke-stukken/20200219065-2-Bijlage-1-Actieplan-Fiets-2020-2022-1.pdf

Faghih-Imani, A., & Eluru, N. (2016). Incorporating the impact of spatio-temporal interactions on bicycle sharing system demand: A case study of New York CitiBike system. Journal of Transport Geography, 54, 218–227. https://doi.org/10.1016/j.jtrangeo.2016.06.008

Fietsersbond. (2021). Fietsersbond – Dataset of 2021. Retrieved from https://www.fietsersbond.nl/

Fietstelweek. (2016). Nationale fietstelweek 2015, 2016 en 2017 – Dataset of 2016. Retrieved from http://opendata.cyclingintelligence.eu/

Goetzke, F. (2008). Network effects in public transit use: Evidence from a spatially autoregressive mode choice model for New York. Urban Studies, 45(2), 407–417. https://doi.org/10.1177/0042098007085970

Khatri, R., Cherry, C. R., Nambisan, S. S., & Han, L. D. (2016). Modeling route choice of utilitarian bikeshare users with GPS data. Transportation Research Record, 2587(1), 141–149. https://doi.org/10.3141/2587-17

Koch, T., & Dugundji, E. R. (2021). Taste variation in environmental features of bicycle routes. Proceedings of the 14th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2021, article 2, (pp. 1–10). https://doi.org/10.1145/3486629.3490697

Lee, K., & Sener, I. N. (2021). Strava Metro data for bicycle monitoring: A literature review. Transport Reviews, 41(1), 27–47. https://doi.org/10.1080/01441647.2020.1798558

Li, Z., Wang, W., & Ragland, D. R. (2012). Physical environments influencing bicyclists’ perception of comfort on separated and on-street bicycle facilities. Transportation Research Part D, 17, 256–261. https://doi.org/10.1016/j.trd.2011.12.001

Łukawska, M., Paulsen, M., Rasmussen, T. K., Jensen, A. F., & Nielsen, O. A. (2023). A joint bicycle route choice model for various cycling frequencies and trip distances based on a large crowdsourced GPS dataset. Transportation Research Part A: Policy and Practice, 176, 103834. https://doi.org/10.1016/J.TRA.2023.103834

Lv, H., Li, H., Sze, N. N., Zhang, Z., Ren, G., & Zhang, Y. (2023). Analysis of factors influencing cycling count: A micro-level study using road segment count data in London. International Journal of Sustainable Transportation, 17(10), 1113–1128. https://doi.org/10.1080/15568318.2022.2152404

Meister, A., Felder, M., Schmid, B., & Axhausen, K. W. (2023). Route choice modeling for cyclists on urban networks. Transportation Research Part A: Policy and Practice, 173, 103723. https://doi.org/10.1016/J.TRA.2023.103723

PBL. (2018). PBL Dataportaal, RUDIFUN1, Dataset of 2018. Retrieved from https://dataportaal.pbl.nl/RUDIFUN1

PDOK. (2021). Basisregistratie Grootschalige Topografie (BGT), Dataset of 2021. Retrieved from https://www.pdok.nl/introductie/-/article/basisregistratie-grootschalige-topografie-bgt

Prato, C. G., Halldórsdóttir, K., & Nielsen, O. A. (2018). Evaluation of land-use and transport network effects on cyclists’ route choices in the Copenhagen region in value-of-distance space. International Journal of Sustainable Transportation, 12(10), 770–781. https://doi.org/10.1080/15568318.2018.1437236

Saelens, B. E., Sallis, J. F., & Frank, L. D. (2003). Environmental correlates of walking and cycling: Findings from the transportation, urban design, and planning literatures. Annals of Behavioral Medicine: A Publication of the Society of Behavioral Medicine, 25(2), 80–91. https://doi.org/10.1207/S15324796ABM2502_03

Segadilha, A. B. P., & Sanches, S. da P. (2014). Identification of factors that influence cyclistś route choice. Procedia Social and Behavioral Sciences, 160, 372–380. https://doi.org/10.1016/J.SBSPRO.2014.12.149

Siegel, A. F., & Wagner, M. R. (2022). Multiple regression: Predicting one variable from several others. In Practical business statistics (pp. 371–431). Cambridge, MA: Academic Press. https://doi.org/10.1016/B978-0-12-820025-4.00012-9

Strauss, J., & Miranda-Moreno, L. F. (2013). Spatial modeling of bicycle activity at signalized intersections. Journal of Transport and Land Use, 6(2), 47–58. https://doi.org/10.5198/JTLU.V6I2.296

Uijtdewilligen, T., Baran Ulak, M., Wijlhuizen, G. J., & Geurs, K. T. (2024). Effects of crowding on route preferences and perceived safety of urban cyclists in the Netherlands. Transport Research Part A: Policy and Practice, 183, 104030.

Uttley, J., Fotios, S., & Lovelace, R. (2020). Road lighting density and brightness linked with increased cycling rates after-dark. PLoS ONE, 15(5). e0233105. https://doi.org/10.1371/JOURNAL.PONE.0233105

Van Nijen, N. (2022). The influence of infrastructure and land use allocation on the route choice of cyclists. Enschede, Netherlands: University of Twente. https://essay.utwente.nl/91571/1/Nijen_Nick_van.pdf

Veenstra, S. (2021). FietsMonitor/Witteveen+Bos, dataset of 2021. Retrieved from https://digitalsolutions.witteveenbos.com/ruimtelijke-ontwikkeling-wonen-en-mobiliteit/mobiliteit/fietsmonitor

Velthuijsen, T. (2020). Calculating cycling delay at signalized intersections using smartphone data. Enschede, Netherlands: University of Twente. https://essay.utwente.nl/85605/1/Velthuijsen-Tim.pdf

Winters, M., Brauer, M., Setton, E. M., & Teschke, K. (2010). Built environment influences on healthy transportation choices: Bicycling versus driving. Journal of Urban Health: Bulletin of the New York Academy of Medicine, 87(6), 969–993. https://doi.org/10.1007/S11524-010-9509-6

Winters, M., Davidson, G., Kao, D., & Teschke, K. (2011). Motivators and deterrents of bicycling: Comparing influences on decisions to ride. Transportation, 38(1), 153–168. https://doi.org/10.1007/S11116-010-9284-Y

Zhao, Y., Lin, Q., Ke, S., & Yu, Y. (2020). Impact of land use on bicycle usage: A big data-based spatial approach to inform transport planning. Journal of Transport and Land Use, 13(1), 299–316. https://doi.org/10.5198/JTLU.2020.1499

Zimmermann, M., Mai, T., & Frejinger, E. (2017). Bike route choice modeling using GPS data without choice sets of paths. Transportation Research Part C: Emerging Technologies, 75, 183–196. https://doi.org/10.1016/J.TRC.2016.12.009