Abstract
Many cities plan to grow cycling as a prominent mode to improve accessibility and environmental and financial sustainability. However, relatively few cities have made meaningful headway in this direction. Policymakers would be more inclined to implement the necessary interventions when they have certainty about potential demand, especially knowing where it is located in space. This paper introduces an approach to estimating potential cycling demand using agent-based modelling to determine who may benefit from switching from their current modes to cycling. People benefit when they obtain a similar or higher travel utility score when cycling between their daily activities than when using their existing modes. The model is based on individual mode selection, that all activities in the trip chain are included and can include detailed road and cycle network elements. The co-evolutionary mechanisms within the agent-based simulation allow us to test the potential for cycling relative to the performance of other modes on the network. The case for Cape Town, South Africa, shows that about 32% of those that travel would benefit from cycling based on their utility score. Understanding that travel time benefits are not the only criteria for mode selection, we apply a rejection sampling algorithm based on demographic factors to demonstrate that a more realistic, or pragmatic, cycling potential for Cape Town is in the region of 8%. The results also show that more than 46% of the observed pragmatic demand for cycling is concentrated in an area covering less than 7% of the study area. This has practical implications for policymakers to target interventions both in space and towards specific demographic market segments.
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Data availability
The datasets generated and analysed during the current study are not publicly available because they constitute an excerpt of research in progress but are available from the corresponding author upon reasonable request.
References
Adjei, E., Behrens, R.: Travel behaviour change theories and experiments: a review and synthesis. In: Proceedings of the 29th Southern African Transport Conference (SATC) (2010)
Aldred, R., Woodcock, J., Goodman, A.: Does more cycling mean more diversity in cycling? Transp. Rev. 36(1), 28–44 (2016). https://doi.org/10.1080/01441647.2015.1014451
Behrens, R., Del Mistro, R., Lombard, M., et al.: The pace of behaviour change and implications for TDM response lags and monitoring: Findings of a retrospective commuter travel survey in Cape Town. In: Proceedings of the 26th Southern African Transport Conference (SATC) (2007)
BEN: https://www.benbikes.org.za/ (2020)
Blondiau, T., van Zeebroek, B., Haubold, H.: Economic benefits of increased cycling. Transp. Res. Proc. 14, 2306–2313 (2016). https://doi.org/10.1016/j.trpro.2016.05.247
Buehler, R., Dill, J.: Bikeway networks: a review of effects on cycling. Transp. Rev. 36(1), 9–27 (2016). https://doi.org/10.1080/01441647.2015.1069908
Cervero, R., Sarmiento, O., Jacoby, E., Gomez, L., Neiman, A.: Influences of built environments on walking and cycling: lessons from Bogotá. Int. J. Sustain. Transp. 3(4), 203–26 (2009). https://doi.org/10.1080/15568310802178314
Charypar, D., Nagel, K.: Generating complete all-day activity plans with genetic algorithms. Transportation 32, 369–397 (2005). https://doi.org/10.1007/s11116-004-8287-y
Chatterjee, K.: (2001) Asymmetric churn-academic jargon or a serious issue for transport planning? Transport Planning Society Bursary Paper
Chillón, P., Molina-García, J., Castillo, I., Queralt, A.: What distance do university students walk and bike daily to class in Spain. J. Transp. Health 3, 315–320 (2016). https://doi.org/10.1016/j.jth.2016.06.001
City of Cape Town 2013. Household survey report—final draft. Technical report, City of Cape Town
City of Cape Town: Cycling strategy for the city of Cape Town. Transport and Urban Development Authority (TDA) (2017)
Copenhagen, Good, better, best: the city of Copenhagen’s bicycle strategy 2011–2025. Technical report, City of Copenhagen (2010)
Damant-Sirois, G., El-Geneidy, A.M.: Who cycles more? Determining cycling frequency through a segmentation approach in Montreal, Canada. Transp. Res. Part A Policy Pract. 77, 113–125 (2015). https://doi.org/10.1016/j.tra.2015.03.028
de Dios Ortuzar, J., Luis, G.: Modelling transport, 2nd edn. Wiley (2011)
Department for Transport. Gear change: a bold vision for cycling and walking
Dias, A.M., Lopez, M., Silva, C.: More than cycling infrastructure: supporting the development of policy packages for starter cycling cities. Transp. Res. 2676, 785–797 (2022). https://doi.org/10.1177/03611981211034732
Dill, J., Rose, G.: Electric bikes and transportation policy: insights from early adopters. Transp. Res. Record 2314, 1–6 (2012). https://doi.org/10.3141/2314-01
DTU. The Danish National Travel Survey-fact sheet about cycling in Denmark. https://cyclingsolutions.info/embassy_category/cycling-facts-figures/ (2015)
Ekblad, H., Svensson, A., and Koglin, T.: Bicycle planning in an urban context: A literature review. Technical report, Bulletin 300, Transport and Roads, Dept of Technology and Society, Lund University, Lund
FLOW: The role of walking and cycling in reducing congestion: a portfolio of measures. Flow (2016)
Fonseca, F., Ribeiro, P., Neiva, C.: A planning practice method to assess the potential for cycling and to design a bicycle network in a starter cycling city in Portugal. Sustainability 15, 4534 (2023). https://doi.org/10.3390/su15054534
Goldman, K., Wessel, J.: Some people feel the rain, others just get wet: An analysis of regional differences in the effects of weather on cycling. Res. Transp. Bus. Manag. 40, 100541 (2021). https://doi.org/10.1016/j.rtbm.2020.100541
Goldsmith, S.: Case study no. 1: reasons why bicycling and walking are not being used more extensively as travel modes. In: National bicycling and walking study (1992)
Goodman, A., Rojas, I.F., Woodcock, J., Aldred, R., Berkoff, N., Morgan, M., Abbas, A., Lovelace, R.: Scenarios of cycling to school in England, and associated health and carbon impacts: application of the propensity to cycle tool. J. Transp. Health 12(2), 263–278 (2019). https://doi.org/10.1016/j.jth.2019.01.008
GTZ. Preserving and expanding the role of non-motorised transport, GTZ sustainable urban transport project. www.sutp.org (2003)
Heinen, E., van Wee, B., Maat, K.: Commuting by bicycle: an overview of the literature. Transp. Rev. 30(1), 59–96 (2010). https://doi.org/10.1080/01441640903187001
Hitge, G., Joubert, J.W.: A nodal approach for estimating potential cycling demand. J. Transp. Geogr. 90, 102943 (2021)
Hitge, G., Vanderschuren, M.: Comparison of travel time between private car and public transport in Cape Town. SAICE J. 57(3), 35–43 (2015). https://doi.org/10.17159/2309-8775/2015/V57N3A5
Horni, A., Nagel, K., Axhausen, K.W.: The multi-agent transport simulation. MATSim, Ubiquity Press, London (2016)
Hunt, J.D., Abraham, J.E.: Influences on bicycle use. Transportation 34, 453–470 (2007). https://doi.org/10.1007/s11116-006-9109-1
Irlam, J.H., Zuidgeest, M.: Barriers to cycling mobility in a low-income community in Cape Town: a best-worst scaling approach. Case Stud. Transp. Policy 6, 815–823 (2018). https://doi.org/10.1016/j.cstp.2018.10.003
Jarass, J., Scheiner, J.: Residential self-selection and travel mode use in a new inner-city development neighbourhood in Berlin. J. Transp. Geogr. 70, 68–77 (2018). https://doi.org/10.1016/j.jtrangeo.2018.05.018
Jennings, G., Petzer, B., Goldman, E.: When bicycle lanes are not enough: growing mode share in Cape Town, South Africa: an analysis of policy and practice, Chapter 13. Routledge, Cornwall (2017)
Joubert, J.W.: Synthetic populations of South African urban areas. Mendeley Data (2021)
Joubert, J.W., de Waal, A.: Activity-based travel demand generation using Bayesian networks. Transp. Res. Part C Emerg. Technol. 120, 102804 (2020). https://doi.org/10.1016/j.trc.2020.102804
KiM 2019. Mobiliteitsbeeld 2019 (mobility report: Technical report, Ministry of Infrastructure and Water Management. Netherlands Institute for Transport Policy Analysis (KiM) Het Kennisinstituut voor Mobiliteitsbeleid, The Hague (2019)
Kuzmyak, R., Walters, J., Bradley, M., Kockelman, K.: Estimating bicycling and walking for planning and project development: a guidebook. https://doi.org/10.17226/22330
Lovelace, R., Goodman, A., Aldred, R., Berkoff, N., Abbas, A., Woodcock, J.: The propensity to cycle tool: an open source online system for sustainable transport planning. J. Transp. Land Use (2017). https://doi.org/10.5198/jtlu.2016.862
Mahfouz, H., Arcaute, E., Lovelace, R.: A road segment prioritization approach for cycling infrastructure. https://doi.org/10.48550/arXiv.2105.03712
Midgley, P.: Bicycle-sharing schemes: enhancing sustainable mobility in urban areas. United Nations Department of Economic and Social Affairs, vol. 8, pp. 1–12 (2011)
National Department of Transport. Shova Kalula. Available online at https://www.transport.gov.za/ (2020)
Nello-Deakin, S., Nikolaeva, A.: The human infrastructure of a cycling city: Amsterdam through the eyes of international newcomers. Urban Geogr. 42(3), 289–311 (2021). https://doi.org/10.1080/02723638.2019.1709757
Nordic Council of Ministers. CBA of Cycling. Copenhagen: ProQuest Ebook Central (2005). Accessed 22 December 2021
OECD: Managing urban traffic congestion. OECD (2007)
Olmos, L.E., Tadeo, M.S., Vlachogiannis, D., Alhasoun, F., Espinet Alegre, X., Ochoa, C., Targa, F., González, M.C.: A data science framework for planning the growth of bicycle infrastructures. Transp. Res. Part C Emerg. Technol. 115, 102640 (2020). https://doi.org/10.1016/j.trc.2020.102640
OSM. OpenStreetMap. http://www.openstreetmap.org (2021)
O’Connor, J.P., Brown, T.D.: Riding with the sharks: serious leisure cyclist’s perceptions of sharing the road with motorists. J. Sci. Med. Sport 13(1), 53–58 (2010). https://doi.org/10.1016/j.jsams.2008.11.003
Parkin, J., Wardman, M., Page, M.: Estimation of the determinants of bicycle mode share for the journey to work using census data. Transportation 35(1), 93–109 (2008). https://doi.org/10.1007/s11116-007-9137-5
Pucher, J., Buehler, R.: Cycling for everyone: Lessons from the Netherlands. Denmark and Germany. Transp. Rev. 28(4), 495–528 (2008). https://doi.org/10.1080/01441640701806612
Qhubeka. Qhubeka. https://qhubeka.org/ (2020)
Reid, L.W., Konrad, M.: The gender gap in fear: assessing the interactive effects of gender and perceived risk on fear of crime. Sociol. Spectr. 24(4), 399–425 (2004). https://doi.org/10.1080/02732170490431331
Riggs, W.: Cargo bikes as a growth area for bicycle vs. auto trips: exploring the potential for mode substitution behavior. Transp. Res. Part F Traff. Psychol. Behav. 43, 48–55 (2016). https://doi.org/10.1016/j.trf.2016.09.017
Rodrıguez, D.A., Joo, J.: The relationship between non-motorized mode choice and the local physical environment. Transp. Res. Part D Transp. Environ. 9(2), 151–173 (2004). https://doi.org/10.1016/j.trd.2003.11.001
Saleh, W., Farrell, S.: Investigation and analysis of evidence of asymmetric churn in travel demand models. Transp. Res. Part A Policy Pract. 41(7), 691–702 (2007). https://doi.org/10.1016/j.tra.2006.09.016
Saneinejad, S., Roorda, M.J., Kennedy, C.: Modelling the impact of weather conditions on active transportation travel behaviour. Transp. Res. Part D Transp. Environ. 17, 129–137 (2012). https://doi.org/10.1016/j.trd.2011.09.005
Silva, C., Teixeira, J., Proença, A.: Revealing the cycling potential of starter cycling cities. Transp. Res. Procedia 41, 637–654 (2019). https://doi.org/10.1016/j.trpro.2019.09.113
StatsSA.: South African Census 2011. https://www.statssa.gov.za/publications/P03014/P030142011.pdf (2011)
StatsSA: 2011 census data. Technical report, Statistics South Africa (2012)
StatsSA: Census 2011 municipal report—Western Cape. Technical report, Statistics South Africa (2012)
StatsSA: National household travel survey february to March 2013. Technical report, Statistics South Africa, Pretoria (2013)
Sun, L., Erath, A.: A Bayesion network approach for population synthesis. Transp. Res. Part C Emerg. Technol. 61, 49–62 (2015). https://doi.org/10.1016/j.trc.2015.10.010
Sweet, M., Kanaroglou, P.: Gender differences: The role of travel and time use in subjective well-being. Transp. Res. Part F Traffic Psychol. Behav. 40, 23–34 (2016). https://doi.org/10.1016/j.trf.2016.03.006
Taylor, B.D., Ralph, K., Smart, M.: What explains the gender gap in schlepping? Testing various explanations for gender differences in household-serving travel. Soc. Sci. Q. 96(5), 1493–1510 (2015). https://doi.org/10.1111/ssqu.12203
Tortosa, E.V., Lovelace, R., Heinen, E., Mann, R.: Infrastructure is not enough: interactions between the environment, socioeconomic disadvantage, and cycling participation in England. J. Transp. Land. Use. 14(1), 693–714 (2021). https://doi.org/10.5198/jtlu.2021.1781
UNESCO: International standard classification of Education-ISCED 2011. UNESCO Institute for Statistics (2012)
Van Cauwenberg, J., Bourdeaudhuij, I.D., Clarys, P., de Geus, B., Deforche, B.: E-bikes among older adults: benefits, disadvantages, usage and crash characteristics. Transportation 46, 2151–2172 (2019). https://doi.org/10.1007/s11116-018-9919-y
Wardman, M.R., Tight, M.R., Page, M.: Factors influencing the propensity to cycle to work. Transp. Res. Part A 41(4), 339–350 (2007). https://doi.org/10.1016/j.tra.2006.09.011
Wooliscroft, B., Ganglmair-Wooliscroft, A.: Improving conditions for potential New Zealand cyclists: an application of conjoint analysis. Transp. Res. Part A Policy Pract. 69, 11–19 (2014). https://doi.org/10.1016/j.tra.2014.08.005
Young, M., Savan, B., Manaugh, K., Scott, J.: Mapping the demand and potential for cycling in Toronto. Int. J. Sustain. Transp. 15(4), 285–293 (2020). https://doi.org/10.1080/15568318.2020.1746871
Zhang, D., Magalhães, D.J.A.V., Wang, X.C.: Prioritizing bicycle paths in Belo Horizonte city, brazil: analysis based on user preferences and willingness considering individual heterogeneity. Transp. Res. Part A Policy Pract. 67, 268–278 (2014). https://doi.org/10.1016/j.tra.2014.07.010
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GH: conceptualisation; formal analysis; validation; writing—original draft; writing—review and editing. JWJ: conceptualisation; data curation; formal analysis; methodology; resources; software; supervision; visualisation; writing—original draft; writing—review and editing.
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Hitge, G., Joubert, J.W. The survivability of cycling in a co-evolutionary agent-based model. Transportation (2023). https://doi.org/10.1007/s11116-023-10422-z
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DOI: https://doi.org/10.1007/s11116-023-10422-z