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Understanding cycling distance according to the prediction of the XGBoost and the interpretation of SHAP: A non-linear and interaction effect analysis

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  • Ji, Shujuan
  • Wang, Xin
  • Lyu, Tao
  • Liu, Xiaojie
  • Wang, Yuanqing
  • Heinen, Eva
  • Sun, Zhenwei

Abstract

Cycling benefits both the individual and society in terms of public health promotion, traffic congestion relief and vehicle emissions reduction. To better understand cycling behaviors, we analyze non-linear relationships and interaction effects between the built environment and cycling distance. Few studies explore the interaction effects on cycling distance in which road network patterns interact with the demographic, trip, and other built environment characteristics to produce complex effects. Previous research has not examined the effect size or relative contribution of various variables have on cycling distance. Thus, this study adopts eXtreme Gradient Boosting (XGBoost) to examine the non-linear relationships among road network patterns, demographic, trip, and bike lane infrastructure, and other built environment characteristics and cycling distance, and employs SHapley Additive exPlanations (SHAP) to discover complex interaction effects on cycling distance, based on a bike travel survey in Xi'an, China. The results show that road network patterns have the greatest contribution; bike lane infrastructure is also quite important and has larger collective contributions than land use patterns and socioeconomics. Average geodesic distance and network betweenness centrality, two topological indices to describe road network structure, interact with bike lane infrastructure, land use and demographic characteristics to produce interaction effects on explaining cycling behaviors. When the average geodesic distance is smaller than 2.8 and the network betweenness centrality is smaller than 50%; as average geodesic distance increases, the network betweenness centrality has a positive effect on cycling distance. A network with a lower average geodesic distance, and with a higher intersection density makes cyclists ride a longer distance. A network with a higher value of average geodesic distance discourages cyclists to detour.

Suggested Citation

  • Ji, Shujuan & Wang, Xin & Lyu, Tao & Liu, Xiaojie & Wang, Yuanqing & Heinen, Eva & Sun, Zhenwei, 2022. "Understanding cycling distance according to the prediction of the XGBoost and the interpretation of SHAP: A non-linear and interaction effect analysis," Journal of Transport Geography, Elsevier, vol. 103(C).
    • Handle: RePEc:eee:jotrge:v:103:y:2022:i:c:s0966692322001375
    DOI: 10.1016/j.jtrangeo.2022.103414
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    as
    1. John Parkin & Mark Wardman & Matthew Page, 2008. "Estimation of the determinants of bicycle mode share for the journey to work using census data," Transportation, Springer, vol. 35(1), pages 93-109, January.
    2. Mark R. Stevens, 2017. "Does Compact Development Make People Drive Less?," Journal of the American Planning Association, Taylor & Francis Journals, vol. 83(1), pages 7-18, January.
    3. Reid Ewing & Robert Cervero, 2010. "Travel and the Built Environment," Journal of the American Planning Association, Taylor & Francis Journals, vol. 76(3), pages 265-294.
    4. Halás, Marián & Klapka, Pavel & Kladivo, Petr, 2014. "Distance-decay functions for daily travel-to-work flows," Journal of Transport Geography, Elsevier, vol. 35(C), pages 107-119.
    5. Lu, Wei & Scott, Darren M. & Dalumpines, Ron, 2018. "Understanding bike share cyclist route choice using GPS data: Comparing dominant routes and shortest paths," Journal of Transport Geography, Elsevier, vol. 71(C), pages 172-181.
    6. Kevin Krizek & Ahmed El-Geneidy & Kristin Thompson, 2007. "A detailed analysis of how an urban trail system affects cyclists’ travel," Transportation, Springer, vol. 34(5), pages 611-624, September.
    7. Pucher, John & Buehler, Ralph, 2006. "Why Canadians cycle more than Americans: A comparative analysis of bicycling trends and policies," Transport Policy, Elsevier, vol. 13(3), pages 265-279, May.
    8. Rahul, T.M. & Verma, Ashish, 2014. "A study of acceptable trip distances using walking and cycling in Bangalore," Journal of Transport Geography, Elsevier, vol. 38(C), pages 106-113.
    9. Sun, Guibo & Wallace, Dugald & Webster, Chris, 2020. "Unravelling the impact of street network structure and gated community layout in development-oriented transit design," Land Use Policy, Elsevier, vol. 90(C).
    10. Cervero, R. & Duncan, M., 2003. "Walking, Bicycling, and Urban Landscapes: Evidence from the San Francisco Bay Area," American Journal of Public Health, American Public Health Association, vol. 93(9), pages 1478-1483.
    11. Zhang, Yuanyuan & Bigham, John & Ragland, David & Chen, Xiaohong, 2015. "Investigating the associations between road network structure and non-motorist accidents," Journal of Transport Geography, Elsevier, vol. 42(C), pages 34-47.
    12. Scott, Darren M. & Lu, Wei & Brown, Matthew J., 2021. "Route choice of bike share users: Leveraging GPS data to derive choice sets," Journal of Transport Geography, Elsevier, vol. 90(C).
    13. Broach, Joseph & Dill, Jennifer & Gliebe, John, 2012. "Where do cyclists ride? A route choice model developed with revealed preference GPS data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(10), pages 1730-1740.
    14. Crane, Randall & Crepeau, Richard, 1998. "Does Neighborhood Design Influence Travel?: Behavioral Analysis of Travel Diary and GIS Data," University of California Transportation Center, Working Papers qt4pj4s7t8, University of California Transportation Center.
    15. Liu, Jixiang & Wang, Bo & Xiao, Longzhu, 2021. "Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach," Journal of Transport Geography, Elsevier, vol. 92(C).
    16. Porta, Sergio & Crucitti, Paolo & Latora, Vito, 2006. "The network analysis of urban streets: A dual approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 369(2), pages 853-866.
    17. Ding, Chuan & Cao, Xinyu (Jason) & Næss, Petter, 2018. "Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 107-117.
    18. Hansen, Karsten Bruun & Nielsen, Thomas Alexander Sick, 2014. "Exploring characteristics and motives of long distance commuter cyclists," Transport Policy, Elsevier, vol. 35(C), pages 57-63.
    19. Weizhang Liang & Suizhi Luo & Guoyan Zhao & Hao Wu, 2020. "Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms," Mathematics, MDPI, vol. 8(5), pages 1-17, May.
    20. Guzman, Luis A. & Peña, Javier & Carrasco, Juan Antonio, 2020. "Assessing the role of the built environment and sociodemographic characteristics on walking travel distances in Bogotá," Journal of Transport Geography, Elsevier, vol. 88(C).
    21. Tao, Tao & Wang, Jueyu & Cao, Xinyu, 2020. "Exploring the non-linear associations between spatial attributes and walking distance to transit," Journal of Transport Geography, Elsevier, vol. 82(C).
    22. Crane, Randall, 1998. "Travel By Design?," University of California Transportation Center, Working Papers qt3pc4v6jj, University of California Transportation Center.
    23. Cervero, Robert, 1996. "Mixed land-uses and commuting: Evidence from the American Housing Survey," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(5), pages 361-377, September.
    24. Song, Jie & Zhang, Liye & Qin, Zheng & Ramli, Muhamad Azfar, 2021. "Where are public bikes? The decline of dockless bike-sharing supply in Singapore and its resulting impact on ridership activities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 146(C), pages 72-90.
    25. Rietveld, Piet & Daniel, Vanessa, 2004. "Determinants of bicycle use: do municipal policies matter?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(7), pages 531-550, August.
    26. Cervero, Robert & Duncan, Michael, 2003. "Walking, Bicycling, and Urban Landscapes: Evidence from the San Francisco Bay Area," University of California Transportation Center, Working Papers qt6zr1x95m, University of California Transportation Center.
    27. Yang, Lin & Zhang, Fayong & Kwan, Mei-Po & Wang, Ke & Zuo, Zejun & Xia, Shaotian & Zhang, Zhiyong & Zhao, Xinpei, 2020. "Space-time demand cube for spatial-temporal coverage optimization model of shared bicycle system: A study using big bike GPS data," Journal of Transport Geography, Elsevier, vol. 88(C).
    28. Márquez, Luis & Soto, Jose J., 2021. "Integrating perceptions of safety and bicycle theft risk in the analysis of cycling infrastructure preferences," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 285-301.
    29. De Vos, Jonas & Cheng, Long & Kamruzzaman, Md. & Witlox, Frank, 2021. "The indirect effect of the built environment on travel mode choice: A focus on recent movers," Journal of Transport Geography, Elsevier, vol. 91(C).
    30. Xueming Chen, 2012. "Statistical and activity-based modeling of university student travel behavior," Transportation Planning and Technology, Taylor & Francis Journals, vol. 35(5), pages 591-610, March.
    31. Faghih-Imani, Ahmadreza & Eluru, Naveen & El-Geneidy, Ahmed M. & Rabbat, Michael & Haq, Usama, 2014. "How land-use and urban form impact bicycle flows: evidence from the bicycle-sharing system (BIXI) in Montreal," Journal of Transport Geography, Elsevier, vol. 41(C), pages 306-314.
    32. Cervero, Robert & Denman, Steve & Jin, Ying, 2019. "Network design, built and natural environments, and bicycle commuting: Evidence from British cities and towns," Transport Policy, Elsevier, vol. 74(C), pages 153-164.
    33. Motoaki, Yutaka & Daziano, Ricardo A., 2015. "A hybrid-choice latent-class model for the analysis of the effects of weather on cycling demand," Transportation Research Part A: Policy and Practice, Elsevier, vol. 75(C), pages 217-230.
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