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Bike-sharing or taxi? Modeling the choices of travel mode in Chicago using machine learning

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  • Zhou, Xiaolu
  • Wang, Mingshu
  • Li, Dongying

Abstract

In many big cities, the bike-sharing system (BSS) and taxi play critical roles in transportation services. They both offer on-demand transportation options and allow flexible riding scheduling and routing. Previous literature has compared BSS and taxi to other transport modes, such as public transit and private automobile, but little is known about the spatiotemporal factors that influence travel choices between these two alternatives. Understanding travel patterns of BSS and taxi is critical in traffic demand analysis and sustainable transportation planning. Also, an in-depth examination of the patterns of travel behaviors, especially when one would choose BSS over a taxi, will provide valuable insights on human mobility and active living research. In this study, we investigated the spatiotemporal patterns of BSS and taxi trips in Chicago from 2014 to 2016. To model travel choices between BSS and taxi, we applied machine learning techniques to simulate the means of transport based on environmental and temporal factors. Results show seasonal trip variations of the BSS and a declining trend of taxi trips. BSS speed is relatively stable while taxi speed varies primarily because of time and locations. Based on the random forest model, which has demonstrated the best fit with high processing speed, travel distance and the number of parks and recreational facilities seem to be critical spatial predicting factors of the travel choice. Given any time and location, the model can recommend the travel choices between BSS and taxis for users. This study shows the significance of machine learning techniques in urban mobility research. Results of the study may potentially support people's transportation decision-making and facilitate sustainable transportation planning.

Suggested Citation

  • Zhou, Xiaolu & Wang, Mingshu & Li, Dongying, 2019. "Bike-sharing or taxi? Modeling the choices of travel mode in Chicago using machine learning," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.
    • Handle: RePEc:eee:jotrge:v:79:y:2019:i:c:22
    DOI: 10.1016/j.jtrangeo.2019.102479
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    References listed on IDEAS

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    3. Rocio de la Torre & Canan G. Corlu & Javier Faulin & Bhakti S. Onggo & Angel A. Juan, 2021. "Simulation, Optimization, and Machine Learning in Sustainable Transportation Systems: Models and Applications," Sustainability, MDPI, vol. 13(3), pages 1-21, February.
    4. Dehdari Ebrahimi, Zhila & Momenitabar, Mohsen & Nasri, Arefeh A. & Mattson, Jeremy, 2022. "Using a GIS-based spatial approach to determine the optimal locations of bikeshare stations: The case of Washington D.C," Transport Policy, Elsevier, vol. 127(C), pages 48-60.
    5. Zhang, Ning & Wu, Yiping & Rong, Jian & Shao, Juan & Chen, Jiayuan & Zhou, Chenjing, 2023. "Analysis of truckers’ intentions in choosing freeways or parallel national and provincial roads," Research in Transportation Economics, Elsevier, vol. 101(C).
    6. Guangnian Xiao & Zihao Wang, 2020. "Empirical Study on Bikesharing Brand Selection in China in the Post-Sharing Era," Sustainability, MDPI, vol. 12(8), pages 1-16, April.
    7. Liu, Jixiang & Xiao, Longzhu, 2023. "Non-linear relationships between built environment and commuting duration of migrants and locals," Journal of Transport Geography, Elsevier, vol. 106(C).
    8. 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).
    9. Li, Xiaowei & Tang, Junqing & Hu, Xiaojiao & Wang, Wei, 2020. "Assessing intercity multimodal choice behavior in a Touristy City: A factor analysis," Journal of Transport Geography, Elsevier, vol. 86(C).
    10. Yang, Hongtai & Luo, Peng & Li, Chaojing & Zhai, Guocong & Yeh, Anthony G.O., 2023. "Nonlinear effects of fare discounts and built environment on ridesplitting adoption rates," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    11. Sifringer, Brian & Lurkin, Virginie & Alahi, Alexandre, 2020. "Enhancing discrete choice models with representation learning," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 236-261.
    12. Hamed Naseri & Edward Owen Douglas Waygood & Bobin Wang & Zachary Patterson, 2022. "Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters," IJERPH, MDPI, vol. 19(24), pages 1-19, December.
    13. Shenhao Wang & Baichuan Mo & Stephane Hess & Jinhua Zhao, 2021. "Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark," Papers 2102.01130, arXiv.org.
    14. Ding, Chuan & Cao, Xinyu & Yu, Bin & Ju, Yang, 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, Elsevier, vol. 148(C), pages 22-35.
    15. Tang, Jinjun & Gao, Fan & Han, Chunyang & Cen, Xuekai & Li, Zhitao, 2021. "Uncovering the spatially heterogeneous effects of shared mobility on public transit and taxi," Journal of Transport Geography, Elsevier, vol. 95(C).
    16. Wenya Cui & Guangnian Xiao, 2021. "Tripartite Dynamic Game among Government, Bike-Sharing Enterprises, and Consumers under the Influence of Seasons and Quota," Sustainability, MDPI, vol. 13(20), pages 1-24, October.

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