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Inferring the route-use patterns of metro passengers based only on travel-time data within a Bayesian framework using a reversible-jump Markov chain Monte Carlo (MCMC) simulation

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  • Lee, Minseo
  • Sohn, Keemin

Abstract

The passenger share and the average travel time for multiple routes connecting an origin–destination pair on a metro network has been examined based on a known number of used routes. Determining how many routes were used based only on travel times from smart-card data is a difficult task, even though the automatic fare collection system can provide a massive amount of travel data. The present study proposes a robust approach to incorporate the number of used routes as an unknown parameter into a Bayesian framework based on a reversible-jump Markov chain Monte Carlo (MCMC) algorithm. Other route-use patterns such as the passenger share and the mean and variance of route travel times were also estimated. The performance of the present approach was compared with the existing method, which depends on the Bayesian information criterion (BIC). The present approach showed better performance in reproducing the observed number of routes used, and also provided greater flexibility in recognizing route-use patterns through the marginal posterior distribution of other unknown parameters.

Suggested Citation

  • Lee, Minseo & Sohn, Keemin, 2015. "Inferring the route-use patterns of metro passengers based only on travel-time data within a Bayesian framework using a reversible-jump Markov chain Monte Carlo (MCMC) simulation," Transportation Research Part B: Methodological, Elsevier, vol. 81(P1), pages 1-17.
    • Handle: RePEc:eee:transb:v:81:y:2015:i:p1:p:1-17
    DOI: 10.1016/j.trb.2015.08.008
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    References listed on IDEAS

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    Cited by:

    1. Yu, Chao & Li, Haiying & Xu, Xinyue & Liu, Jun, 2020. "Data-driven approach for solving the route choice problem with traveling backward behavior in congested metro systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    2. Hong En Tan & De Wen Soh & Yong Sheng Soh & Muhamad Azfar Ramli, 2021. "Derivation of train arrival timings through correlations from individual passenger farecard data," Transportation, Springer, vol. 48(6), pages 3181-3205, December.
    3. Han, Gain & Sohn, Keemin, 2016. "Activity imputation for trip-chains elicited from smart-card data using a continuous hidden Markov model," Transportation Research Part B: Methodological, Elsevier, vol. 83(C), pages 121-135.
    4. Taoyuan Yang & Peng Zhao & Xiangming Yao, 2020. "A Method to Estimate URT Passenger Spatial-Temporal Trajectory with Smart Card Data and Train Schedules," Sustainability, MDPI, vol. 12(6), pages 1-13, March.
    5. Huang, Wencheng & Zhang, Yue & Kou, Xingyi & Yin, Dezhi & Mi, Rongwei & Li, Linqing, 2020. "Railway dangerous goods transportation system risk analysis: An Interpretive Structural Modeling and Bayesian Network combining approach," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    6. Eun Hak Lee & Inmook Lee & Shin-Hyung Cho & Seung-Young Kho & Dong-Kyu Kim, 2019. "A Travel Behavior-Based Skip-Stop Strategy Considering Train Choice Behaviors Based on Smartcard Data," Sustainability, MDPI, vol. 11(10), pages 1-18, May.
    7. Wu, Jianjun & Qu, Yunchao & Sun, Huijun & Yin, Haodong & Yan, Xiaoyong & Zhao, Jiandong, 2019. "Data-driven model for passenger route choice in urban metro network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 787-798.

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