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Estimating heterogeneity of car travelers on mode shifting behavior based on discrete choice models

Author

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  • Huanmei Qin
  • Jianqiang Gao
  • Hongzhi Guan
  • Hongbo Chi

Abstract

In order to understand the mode shift behavior of car travelers and relieve traffic congestion, a Stated Preference survey has been conducted in the city of Ji'nan in China to analyze bus choice behavior and the heterogeneity of car travelers. Several discrete choice models, including multinomial logit, mixed logit and latent class model (LCM) are developed based on these survey data. A comparative analysis indicates that the LCM has the highest precision and is more suitable to analyze the heterogeneity of car travelers. The LCM divides car travelers into three classes. Different classes have different sets of influencing factors in the model. Policy recommendations are also proposed for those classes to promote bus shift from car travelers based on the model results. Finally, sensitivity analysis on parking fees and fuel cost is carried out on the LCMs under different bus service levels. Car travelers have different sensitivities to the influencing factors. The conclusions indicate that the LCM can reflect the heterogeneity and preferences of car travelers and can be used to understand how to shift the behavior of car travelers and make more effective traffic policy.

Suggested Citation

  • Huanmei Qin & Jianqiang Gao & Hongzhi Guan & Hongbo Chi, 2017. "Estimating heterogeneity of car travelers on mode shifting behavior based on discrete choice models," Transportation Planning and Technology, Taylor & Francis Journals, vol. 40(8), pages 914-927, November.
    • Handle: RePEc:taf:transp:v:40:y:2017:i:8:p:914-927
    DOI: 10.1080/03081060.2017.1355886
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    Cited by:

    1. Kim, Sung Hoo & Mokhtarian, Patricia L., 2023. "Finite mixture (or latent class) modeling in transportation: Trends, usage, potential, and future directions," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 134-173.
    2. Fowri, Hamid R. & Seyedabrishami, Seyedehsan, 2020. "Assessment of urban transportation pricing policies with incorporation of unobserved heterogeneity," Transport Policy, Elsevier, vol. 99(C), pages 12-19.
    3. Nabipour, Mohammad & Rosenberg, Mark W. & Nasseri, Seyed Hadi, 2022. "The built environment, networks design, and safety features: An analysis of pedestrian commuting behavior in intermediate-sized cities," Transport Policy, Elsevier, vol. 129(C), pages 14-23.
    4. Ye Ma & Biying Yu & Meimei Xue, 2018. "Spatial Heterogeneous Characteristics of Ridesharing in Beijing–Tianjin–Hebei Region of China," Energies, MDPI, vol. 11(11), pages 1-21, November.
    5. Nguyen Viet Long & Hoang Thuy Linh & Vu Anh Tuan, 2023. "Towards Smart Parking Management: Econometric Analysis and Modeling of Public-Parking-Choice Behavior in Three Cities of Binh Duong, Vietnam," Sustainability, MDPI, vol. 15(24), pages 1-22, December.
    6. Giancarlos Parady & Kay W. Axhausen, 2024. "Size matters: the use and misuse of statistical significance in discrete choice models in the transportation academic literature," Transportation, Springer, vol. 51(6), pages 2393-2425, December.
    7. Konstantakopoulos, Ioannis C. & Barkan, Andrew R. & He, Shiying & Veeravalli, Tanya & Liu, Huihan & Spanos, Costas, 2019. "A deep learning and gamification approach to improving human-building interaction and energy efficiency in smart infrastructure," Applied Energy, Elsevier, vol. 237(C), pages 810-821.

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