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Willingness to board: A novel concept for modeling queuing up passengersAuthor-Name: Liu, Zhiyuan

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  • Wang, Shuaian
  • Chen, Weijie
  • Zheng, Yuan

Abstract

This paper addresses an innovative concept, termed as queuing passengers’ willingness to board (WTB) the transit vehicles. In the peak hours, some queuing passengers cannot board a crowded bus/train, but when the same vehicle arrives at the next stop, some other passengers could still get on. This phenomenon reflects that passengers at different queuing locations have heterogeneous level of ambitions to board. A methodological framework is proposed for the quantitative investigation of WTB. First, a general model is proposed, together with a new least square method (LSM) for the calibration. Then, a parametric model is developed, which is also calibrated by the LSM. To refine the calibration method and deal with the biasness of survey data, a weighted least square method is further developed. Based on real survey data, the calibration results clearly support the existence of WTB, which can be used to estimate the capacity of transit vehicles. This paper also sheds some lights on the practical applications of the quantitative WTB.

Suggested Citation

  • Wang, Shuaian & Chen, Weijie & Zheng, Yuan, 2016. "Willingness to board: A novel concept for modeling queuing up passengersAuthor-Name: Liu, Zhiyuan," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 70-82.
    • Handle: RePEc:eee:transb:v:90:y:2016:i:c:p:70-82
    DOI: 10.1016/j.trb.2016.04.005
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    References listed on IDEAS

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    1. Gupta, Monika, 2016. "Willingness to pay for carbon tax: A study of Indian road passenger transport," Transport Policy, Elsevier, vol. 45(C), pages 46-54.
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    Cited by:

    1. Chen Wang & Lin Liu & Chengcheng Xu & Weitao Lv, 2019. "Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework," IJERPH, MDPI, vol. 16(3), pages 1-18, January.
    2. Huang, Di & Liu, Zhiyuan & Liu, Pan & Chen, Jun, 2016. "Optimal transit fare and service frequency of a nonlinear origin-destination based fare structure," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 96(C), pages 1-19.
    3. Wang, Shuaian & Wang, Xinchang, 2016. "A polynomial-time algorithm for sailing speed optimization with containership resource sharing," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 394-405.
    4. Shucheng Yu & Shuaian Wang & Lu Zhen, 2017. "Quay crane scheduling problem with considering tidal impact and fuel consumption," Flexible Services and Manufacturing Journal, Springer, vol. 29(3), pages 345-368, December.
    5. An, Qinhe & Fu, Xiao & Huang, Di & Cheng, Qixiu & Liu, Zhiyuan, 2020. "Analysis of adding-runs strategy for peak-hour regular bus services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
    6. Wang, Shuaian & Qu, Xiaobo, 2017. "Station choice for Australian commuter rail lines: Equilibrium and optimal fare design," European Journal of Operational Research, Elsevier, vol. 258(1), pages 144-154.
    7. Lu Zhen & Shucheng Yu & Shuaian Wang & Zhuo Sun, 2019. "Scheduling quay cranes and yard trucks for unloading operations in container ports," Annals of Operations Research, Springer, vol. 273(1), pages 455-478, February.

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