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Spatial dependence and spillover effects in customized bus demand: Empirical evidence using spatial dynamic panel models

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  • Wang, Jiangbo
  • Yamamoto, Toshiyuki
  • Liu, Kai

Abstract

This paper proposes customized bus (CB) demand models to investigate dynamic adjustments, spatial dependence, and spatial spillover effects using spatial dynamic panel data techniques and a balanced panel data set collected over two years. The endogeneity introduced by spatial dependence effects is resolved by extending the moment restrictions of the system GMM estimator to a spatial autoregressive dynamic panel. A wide range of variables representing factors related to 1) characteristics of service supply, 2) demographic characteristics, and 3) land use and accessibility were mined and fused to investigate their direct and spillover effects on CB demand. The results reveal the mechanism of spatial dependence such that the increments (or decrement) in demand rather than the amount of demand itself in the neighbourhood contribute to the spatial dependence effects. There is more than 43% matched demand generated in a given area for each-unit increment of demand from its neighbourhood. CB services are more attractive for passengers with long-distance trips, and therefore, increasing the focus on long-distance trips is one potential strategy for increasing ridership. Determined by the role of CB in the transportation system, areas with poor accessibility are found to have larger market niches for CB services.

Suggested Citation

  • Wang, Jiangbo & Yamamoto, Toshiyuki & Liu, Kai, 2021. "Spatial dependence and spillover effects in customized bus demand: Empirical evidence using spatial dynamic panel models," Transport Policy, Elsevier, vol. 105(C), pages 166-180.
    • Handle: RePEc:eee:trapol:v:105:y:2021:i:c:p:166-180
    DOI: 10.1016/j.tranpol.2021.03.004
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    4. Liu, Tao & You, Hailin & Gkiotsalitis, Konstantinos & Cats, Oded, 2024. "Human-Machine collaborative decision-making approach to scheduling customized buses with flexible departure times," Transportation Research Part A: Policy and Practice, Elsevier, vol. 187(C).
    5. Lei, Jiayou & He, Min & Shi, Zhuangbin & He, Mingwei & Liu, Yang & Qian, Qian & Qian, Huimin, 2024. "How does the built environment affect intermodal transit demand across different spatiotemporal contexts?," Journal of Transport Geography, Elsevier, vol. 121(C).
    6. Ahmed, Kashif & Kamihigashi, Takashi & Matsuo, Miwa, 2023. "Positive fuel price elasticities of expressway traffic flows: Insights for policymakers and management strategists," Transport Policy, Elsevier, vol. 142(C), pages 99-114.
    7. Ying Zhao & Jie Wei & Haijun Li & Yan Huang, 2024. "Predicting Station-Level Peak Hour Ridership of Metro Considering the Peak Deviation Coefficient," Sustainability, MDPI, vol. 16(3), pages 1-16, February.
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    9. Zuo, Ziyue & Xiao, Xinping & Gao, Mingyun & Rao, Congjun, 2025. "Mixed-frequency fusion grey panel model for spatiotemporal prediction of photovoltaic power generation," Renewable Energy, Elsevier, vol. 248(C).

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