Journal of Transport and Land Use

Non-linear effects of built environment factors on mode choice: A tour-based analysis

Jia Fang

University of Florida

https://orcid.org/0000-0002-7337-0163

Xiang Yan

University of Florida

https://orcid.org/0000-0002-8619-0065

Tao Tao

Carnegie Mellon University

Changjie Chen

University of Florida

https://orcid.org/0000-0001-6036-2239

DOI: https://doi.org/10.5198/jtlu.2024.2403

Keywords: random forest, non-linear effects, built environment, tour-based mode choice, SHAP

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Abstract

Understanding the connections between the built environment and travel mode choice is a major research topic in transportation. However, existing studies usually examine the relationship through trip-based analyses rather than tour-based approaches. A tour consists of multiple trips that originate and end at the same place, which is increasingly considered the more appropriate analysis unit for travel behaviors. Applying a tour-based approach, this study employs random forest to investigate the non-linear impacts of built environment factors and tour attributes on different mode combinations of a tour. We find that tour attributes and connectivity-related variables (e.g., block size and intersection density) have a strong association with the use of active travel modes when their values are within a certain threshold. In addition, capturing mode change behaviors offers more nuanced understanding of how various built environment variables shape people’s decision to combine modes in a tour.

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References

Anabtawi, R., & Scoppa, M. (2022). Measuring street network efficiency and block sizes in superblocks—Addressing the gap between policy and practice. Buildings, 12(10), 1686. http://doi.org/10.3390/buildings12101686

Antipova, A., & Wang, F. (2010). Land use impacts on trip-chaining propensity for workers and nonworkers in Baton Rouge, Louisiana. Annals of GIS, 16(3), 141–154. https://doi.org/10.1080/19475683.2010.513150

Axhausen, K. W. (2007). Definition of movement and activity for transport modelling. In D. A. Hensher & K. J. Button (Eds.), Handbook of transport modelling (handbooks in transport) (2nd ed.). Amsterdam: Elsevier.

Azimi, G., Rahimi, A., Lee, M., & Jin, X. (2021). Mode choice behavior for access and egress connection to transit services. International Journal of Transportation Science and Technology, 10(2), 136–155. https://doi.org/10.1016/j.ijtst.2020.11.004

Barnes, G. (2001). Population and employment density and travel behavior in large U.S. cities. St. Paul, MN: Minnesota Department of Transportation.

Bhat, C., Guo, J., Srinivasan, S., & Sivakumar, A. (2004). A comprehensive econometric microsimulator for daily activity-travel patterns. Transportation Research Record, 1894, 57–66. https://doi.org/10.3141/1894-07

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324

Chatman, D. (2003). How density and mixed uses at the workplace affect personal commercial travel and commute mode choice. Transportation Research Record, 1831(1), 193–201. https://doi.org/10.3141/1831-22

Chen, C., Judge, J., & Hulse, D. (2022). PyLUSAT: An open-source Python toolkit for GIS-based land use suitability analysis. Environmental Modelling & Software, 151, 105362. https://doi.org/10.1016/j.envsoft.2022.105362

Chowdhury, T., & Scott, D. M. (2018). Role of the built environment on trip-chaining behavior: An investigation of workers and non-workers in Halifax, Nova Scotia. Transportation, 47, 737–761. https://doi.org/10.1007/s11116-018-9914-3

Cirillo, C., & Axhausen, K. (2010). Dynamic model of activity-type choice and scheduling. Transportation, 37(1), 15–38. https://doi.org/DOI: 10.1007/s11116-009-9218-8

Cirillo, C., & Toint, P. (2001). An activity-based approach of the Belgian National Travel Survey. Namur, Belgium: FUNDP, Faculté des Sciences. Département de Mathématique.

Concas, S., & DeSalvo, J. (2012). The effect of density and trip-chaining on the interaction between urban form and transit demand. Retrieved from https://doi.org/10.2139/ssrn.2133602

Daisy, N. S., Liu, L., & Millward, H. (2020). Trip chaining propensity and tour mode choice of out-of-home workers: Evidence from a mid-sized Canadian city. Transportation, 47(2), 763–792. https://doi.org/10.1007/s11116-018-9915-2

Daisy, N. S., Millward, H., & Liu, L. (2018). Trip chaining and tour mode choice of non-workers grouped by daily activity patterns. Journal of Transport Geography, 69, 150–162. https://doi.org/10.1016/j.jtrangeo.2018.04.016

Ding, C., Cao, X., & Liu, C. (2019). How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds. Journal of Transport Geography, 77, 70–78. https://doi.org/10.1016/j.jtrangeo.2019.04.011

Ding, C., Cao, X., Yu, B., & Ju, Y. (2021). Non-linear associations between zonal built environment attributes and transit commuting mode choice accounting for spatial heterogeneity. Transportation Research Part A: Policy and Practice, 148, 22–35. https://doi.org/10.1016/j.tra.2021.03.021

Duncan, M. (2016). How much can trip chaining reduce VMT? A simplified method. Transportation, 43(4), 643–659. https://doi.org/10.1007/s11116-015-9610-5

Elldér, E. (2020). What kind of compact development makes people drive less? The "Ds of the built environment" versus neighborhood amenities. Journal of Planning Education and Research, 40(4), 432–446. https://doi.org/10.1177/0739456X18774120

Ewing, R., & Cervero, R. (2001). Travel and the built environment: A synthesis. Transportation Research Record, 1780(1), 87–114. https://doi.org/10.3141/1780-10

Ewing, R., & Cervero, R. (2010). Travel and the built environment: A meta-analysis. Journal of the American Planning Association, 76(3), 265–294. https://doi.org/10.1080/01944361003766766

Ewing, R., & Cervero, R. (2017). Does compact development make people drive less? The answer is yes. Journal of the American Planning Association, 83(1), 19–25. https://doi.org/10.1080/01944363.2016.1245112

Fang, J. (2022). Exploring the role of the built environment in studying mode choice using a tour-based approach [PhD dissertation], University of Florida, Gainsville, FL.

Fang, J., Yan, X., Bejleri, I., & Chen, C. (2022). Which trip destination matters? Estimating the influence of the built environment on mode choice for home-based complex tours. Journal of Transport Geography, 105, 103474. https://doi.org/10.1016/j.jtrangeo.2022.103474

Frank, L., Bradley, M., Kavage, S., Chapman, J., & Lawton, K. (2008). Urban form, travel time, and cost relationships with tour complexity and mode choice, Transportation, 5(1), 37–54.

Gehrke, S., & Welch, T. (2017). The built environment determinants of activity participation and walking near the workplace. Transportation, 44(5), 941–956. https://doi.org/10.1007/s11116-016-9687-5

Hagenauer, J., & Helbich, M. (2017). A comparative study of machine learning classifiers for modeling travel mode choice. Expert Systems with Applications, 78, 273–282. https://doi.org/10.1016/j.eswa.2017.01.057

Han, Y., Chen, C., Peng, Z., & Mozumder, P. (2021). Evaluating impacts of coastal flooding on the transportation system using an activity-based travel demand model: A case study in Miami-Dade County, FL. Transportation, 49(1), 163–184. https://doi.org/10.1007/s11116-021-10172-w

Handy, S. (1997). Travel behavior issues related to neo-traditional developments—A review of the research. Paper presented at the Urban Design, Telecommuting and Travel Forecasting Conference, October 27–30, 1996, Williamsburg, VA.

Harding, C., Miller, E. J., Patterson, Z., & Axhausen, K. W. (2015). Multiple purpose tours and efficient trip chaining. An analysis of the effects of land use and transit on travel behavior in Switzerland. Paper presented at the 94th International Conference of the Transportation Research Board, January 11–15, Washington, DC.

Hasnine, M. S. (2019). Tour-based mode choice model in activity-based modelling framework. Toronto: University of Toronto.

Hasnine, M. S., & Nurul Habib, K. (2021). Tour-based mode choice modelling as the core of an activity-based travel demand modelling framework: A review of state-of-the-art. Transport Reviews, 41(1), 5–26. https://doi.org/10.1080/01441647.2020.1780648

Ho, C., & Mulley, C. (2013). Multiple purposes at single destination: A key to a better understanding of the relationship between tour complexity and mode choice. Transportation Research Part A: Policy and Practice, 49, 206–219. https://doi.org///doi.org/10.1016/j.tra.2013.01.040

Hong, J. (2017). Non-linear influences of the built environment on transportation emissions: Focusing on densities. Journal of Transport and Land Use, 10(1), 229–240. https://doi.org/10.5198/jtlu.2017.815

Huang, Y., Gao, L., Ni, A., & Liu, X. (2021). Analysis of travel mode choice and trip chain pattern relationships based on multi-day GPS data: A case study in Shanghai, China. Journal of Transport Geography, 93, 103070. https://doi.org/10.1016/j.jtrangeo.2021.103070

Kim, E. J. (2021). Analysis of travel mode choice in Seoul using an interpretable machine learning approach. Journal of Advanced Transportation, 2021, 1–13, https://doi.org/10.1155/2021/6685004

Krygsman, S. C., Arentze, T., & Timmermans, H. (2007). Capturing tour mode and activity choice interdependencies: A co-evolutionary logit modelling approach. Transportation Research Part A: Policy and Practice, 41(10), 913–933. https://doi.org/10.1016/j.tra.2006.03.006

Lee, J. (2016). Impact of neighborhood walkability on trip generation and trip chaining: Case of Los Angeles. Journal of Urban Planning and Development, 142(3), 1–13. https://doi.org/10.1061/(asce)up.1943-5444.0000312

Ley, C., Martin, R. K., Pareek, A., Groll, A., Seil, R., & Tischer, T. (2022). Machine learning and conventional statistics: Making sense of the differences. Knee Surgery, Sports Traumatology, Arthroscopy, 30(3), 753–757. https://doi.org/10.1007/s00167-022-06896-6

Liu, J., Wang, B., & Xiao, L. (2021). Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach. Journal of Transport Geography, 92, 103034. https://doi.org/10.1016/j.jtrangeo.2021.103034

Liu, S., Yamamoto, T., & Yao, E. (2022). Joint modeling of mode choice and travel distance with intra-household interactions. Transportation, 50(5), 1527–1552. https://doi.org/10.1007/s11116-022-10286-9

Lu, Y., Sun, G., Sarkar, C., Gou, Z., & Xiao, Y. (2018). Commuting mode choice in a high-density city: Do land-use density and diversity matter in Hong Kong? International Journal of Environmental Research and Public Health, 15(5), 920. http://doi.org/10.3390/ijerph15050920

Lund, H., Cervero, R., & Willson, R. (2004). Travel characteristics of transit-oriented development in California. Sacramento: Caltrans.

Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., … & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56–67. https://doi.org/10.1038/s42256-019-0138-9

Lundberg, S. M., Erion, G. G., & Lee, S.-I. (2019). Consistent individualized feature attribution for tree ensembles. arXiv.org. https://doi.org/10.48550/arXiv.1802.03888

Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems, 4768–4777.

Miriam, P., & Marco, D. (2016). Classification of tours in the U.S. National Household Travel Survey through clustering techniques. Journal of Transportation Engineering, 142(6), 04016021. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000845

Molnar, C. (2021). Interpretable machine learning: A guide for making black box models explainable. Retrieved from https://christophm.github.io/interpretable-ml-book/index.html

Park, K., Ewing, R., Sabouri, S., Choi, D., Hamidi, S., & Tian, G. (2020). Guidelines for a polycentric region to reduce vehicle use and increase walking and transit use. Journal of the American Planning Association, 86(2), 236–249. https://doi.org/10.1080/01944363.2019.1692690

Primerano, F., Taylor, M., Pitaksringkarn, L., & Tisato, P. (2008). Defining and understanding trip chaining behavior. Transportation, 35(1), 55–72. https://doi.org/10.1007/s11116-007-9134-8

Reilly, & Landis. (2002). The influence of built-form and land use on mode choice: Evidence from the 1996 bay area travel survey. Retrieved from https://escholarship.org/content/qt46r3k871/qt46r3k871_noSplash_4e162f031fb4e3d0da1b5bb571de55f8.pdf

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, 25(2), 80–91. https://doi.org/10.1207/S15324796ABM2502_03

Saleem, M., Västberg, O. B., & Karlström, A. (2018). An activity-based demand model for large scale simulations. Procedia Computer Science, 130, 920–925. https://doi.org/10.1016/j.procs.2018.04.090

Shao, Q., Zhang, W., Cao, X., Yang, J., & Yin, J. (2020). Threshold and moderating effects of land use on metro ridership in Shenzhen: Implications for TOD planning. Journal of Transport Geography, 89, 102878. https://doi.org/10.1016/j.jtrangeo.2020.102878

Stead, D., & Marshall, S. (2001). The relationships between urban form and travel patterns: An international review and evaluation. European Journal of Transport and Infrastructure Research, 1(2), 113–141.

Stopher, P. R., Hartgen, D. T., & Li, Y. (1996). SMART: Simulation model for activities, resources and travel. Transportation, 23(3), 293–312. https://doi.org/10.1007/BF00165706

Tao, T., Wang, J., & Cao, X. (2020). Exploring the non-linear associations between spatial attributes and walking distance to transit. Journal of Transport Geography, 82, 102560. https://doi.org/10.1016/j.jtrangeo.2019.102560

Tao, T., Wu, X., Cao, J., Fan, Y., Das, K., & Ramaswami, A. (2020). Exploring the non-linear relationship between the built environment and active travel in the Twin Cities. Journal of Planning Education and Research, 43(3), 637–652 https://doi.org/10.1177/0739456X20915765

Thabtah, F., Hammoud, S., Kamalov, F., & Gonsalves, A. (2020). Data imbalance in classification: Experimental evaluation. Information Sciences, 513, 429–441. https://doi.org/10.1016/j.ins.2019.11.004

Van Acker, V., & Witlox, F. (2011). Commuting trips within tours: How is commuting related to land use? Transportation, 38(3), 465–486. https://doi.org/10.1007/s11116-010-9309-6

Vande Walle, S., & Steenberghen, T. (2006). Space and time related determinants of public transport use in trip chains. Transportation Research Part A: Policy and Practice, 40(2), 151–162. https://doi.org///doi.org/10.1016/j.tra.2005.05.001

Wang, K., & Ozbilen, B. (2020). Synergistic and threshold effects of telework and residential location choice on travel time allocation. Sustainable Cities and Society, 63, 102468. https://doi.org/10.1016/J.SCS.2020.102468

Xiao, L., Lo, S., Liu, J., Zhou, J., & Li, Q. (2021). Non-linear and synergistic effects of TOD on urban vibrancy: Applying local explanations for gradient boosting decision tree. Sustainable Cities and Society, 72, 103063. https://doi.org/10.1016/j.scs.2021.103063

Xu, Y., Yan, X., Liu, X., & Zhao, X. (2021). Identifying key factors associated with ridesplitting adoption rate and modeling their nonlinear relationships. Transportation Research Part A: Policy and Practice, 144, 170–188.

Wali, B., Frank, L. D., Chapman, J. E., & Fox, E. H. (2021). Developing policy thresholds for objectively measured environmental features to support active travel. Transportation Research Part D: Transport and Environment, 90(January), 102678. https://doi.org/10.1016/j.trd.2020.102678

Wang, W., Rahimi-Golkhandan, A., Chen, C., Taylor, J., & Garvin, M. (2022). Measuring the impact of transportation diversity on disaster resilience in urban communities: Case study of Hurricane Harvey in Houston, TX. Computing in Civil Engineering, 2019, 555–562. https://doi.org/10.1061/9780784482445.071

Yang, J., Cao, J., & Zhou, Y. (2021). Elaborating non-linear associations and synergies of subway access and land uses with urban vitality in Shenzhen. Transportation Research Part A: Policy and Practice, 144, 74–88. https://doi.org/10.1016/j.tra.2020.11.014

Yang, L., Ao, Y., Ke, J., Lu, Y., & Liang, Y. (2021). To walk or not to walk? Examining non-linear effects of streetscape greenery on walking propensity of older adults. Journal of Transport Geography, 94, 103099. https://doi.org/10.1016/J.JTRANGEO.2021.103099

Yang, Y., Sasaki, K., Cheng, L., & Liu, X. (2022). Gender differences in active travel among older adults: Non-linear built environment insights. Transportation Research Part D: Transport and Environment, 110, 103405. https://doi.org/10.1016/J.TRD.2022.103405

Ye, X., Pendyala, R. M., & Gottardi, G. (2007). An exploration of the relationship between mode choice and complexity of trip chaining patterns. Transportation Research Part B: Methodological, 41(1), 96–113. https://doi.org/10.1016/j.trb.2006.03.004

Zhang, M. (2004). The role of land use in travel mode choice: Evidence from Boston and Hong Kong. Journal of the American Planning Association, 70(3), 344–360. https://doi.org/10.1080/01944360408976383

Zhao, X., Yan, X., Yu, A., & Van Hentenryck, P. (2020). Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models. Travel Behaviour and Society, 20, 22–35. https://doi.org/10.1016/j.tbs.2020.02.003