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Using a Neural Network to Analyze the Impact of Passenger Activity on Bus Dwell Time and Travel Time

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  • Chen, Mei
  • Liu, Xiaobo

Abstract

This paper applies neural network modeling approach to analyze the impact of passenger activities on bus dwell time and station-to-station travel time. Data used to develop the model was collected by onboard AVL/APC devices. Sensitivity analyses based on a trained neural network were performed to evaluate the relative significance of each passenger activity variable to variation of dwell time and/or station-to-station travel time. Transit providers can use these methods to identify the causes of schedule deviation and to develop improvement measures that are most effective to transit service.

Suggested Citation

  • Chen, Mei & Liu, Xiaobo, 2005. "Using a Neural Network to Analyze the Impact of Passenger Activity on Bus Dwell Time and Travel Time," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 44(3).
    • Handle: RePEc:ags:ndjtrf:206774
    DOI: 10.22004/ag.econ.206774
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    File URL: https://ageconsearch.umn.edu/record/206774/files/581-701-1-PB.pdf
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    References listed on IDEAS

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    1. Strathman, James G. & Hopper, Janet R., 1993. "Empirical analysis of bus transit on-time performance," Transportation Research Part A: Policy and Practice, Elsevier, vol. 27(2), pages 93-100, April.
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    Cited by:

    1. Chen, Mei & Xia, Jingxin & Liu, Rongfang, 2006. "Developing a Strategy for Imputing Missing Traffic Volume Data," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 45(3).

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