IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i6p2574-d336541.html
   My bibliography  Save this article

A Method to Estimate URT Passenger Spatial-Temporal Trajectory with Smart Card Data and Train Schedules

Author

Listed:
  • Taoyuan Yang

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Peng Zhao

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Xiangming Yao

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

Abstract

Precise estimation of passenger spatial-temporal trajectory is the basis for urban rail transit (URT) passenger flow assignment and ticket fare clearing. Inspired by the correlation between passenger tap-in/out time and train schedules, we present a method to estimate URT passenger spatial-temporal trajectory. First, we classify passengers into four types according to the number of their routes and transfers. Subsequently, based on the characteristic that passengers tap-out in batches at each station, the K-means algorithm is used to assign passengers to trains. Then, we acquire passenger access, egress, and transfer time distribution, which are used to give a probability estimation of passenger trajectories. Finally, in a multi-route case of the Beijing Subway, this method presents an estimation result with 91.2% of the passengers choosing the same route in two consecutive days, and the difference of route choice ratio in these two days is 3.8%. Our method has high accuracy and provides a new method for passenger microcosmic behavior research.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:6:p:2574-:d:336541
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/6/2574/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/6/2574/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kim, Kyung Min & Hong, Sung-Pil & Ko, Suk-Joon & Kim, Dowon, 2015. "Does crowding affect the path choice of metro passengers?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 292-304.
    2. Schmöcker, Jan-Dirk & Bell, Michael G.H. & Kurauchi, Fumitaka, 2008. "A quasi-dynamic capacity constrained frequency-based transit assignment model," Transportation Research Part B: Methodological, Elsevier, vol. 42(10), pages 925-945, December.
    3. Spiess, Heinz & Florian, Michael, 1989. "Optimal strategies: A new assignment model for transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 23(2), pages 83-102, April.
    4. Nassir, Neema & Hickman, Mark & Ma, Zhen-Liang, 2019. "A strategy-based recursive path choice model for public transit smart card data," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 528-548.
    5. Sung-Pil Hong & Yun-Hong Min & Myoung-Ju Park & Kyung Min Kim & Suk Mun Oh, 2016. "Precise estimation of connections of metro passengers from Smart Card data," Transportation, Springer, vol. 43(5), pages 749-769, September.
    6. Zhu, Yiwen & Koutsopoulos, Haris N. & Wilson, Nigel H.M., 2017. "A probabilistic Passenger-to-Train Assignment Model based on automated data," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 522-542.
    7. Tian, Qiong & Huang, Hai-Jun & Yang, Hai, 2007. "Equilibrium properties of the morning peak-period commuting in a many-to-one mass transit system," Transportation Research Part B: Methodological, Elsevier, vol. 41(6), pages 616-631, July.
    8. Sun, Huijun & Wu, Jianjun & Wu, Lijuan & Yan, Xiaoyong & Gao, Ziyou, 2016. "Estimating the influence of common disruptions on urban rail transit networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 62-75.
    9. Mai, Tien & Fosgerau, Mogens & Frejinger, Emma, 2015. "A nested recursive logit model for route choice analysis," Transportation Research Part B: Methodological, Elsevier, vol. 75(C), pages 100-112.
    10. Poon, M. H. & Wong, S. C. & Tong, C. O., 2004. "A dynamic schedule-based model for congested transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 38(4), pages 343-368, May.
    11. Lam, W. H. K. & Gao, Z. Y. & Chan, K. S. & Yang, H., 1999. "A stochastic user equilibrium assignment model for congested transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 33(5), pages 351-368, June.
    12. Enhui Chen & Zhirui Ye & Hui Bi, 2019. "Incorporating Smart Card Data in Spatio-Temporal Analysis of Metro Travel Distances," Sustainability, MDPI, vol. 11(24), pages 1-22, December.
    13. Kjell Jansson & Bosse Ridderstolpe, 1992. "A Method for the Route-Choice Problem in Public Transport Systems," Transportation Science, INFORMS, vol. 26(3), pages 246-251, August.
    14. Takahiko Kusakabe & Takamasa Iryo & Yasuo Asakura, 2010. "Estimation method for railway passengers’ train choice behavior with smart card transaction data," Transportation, Springer, vol. 37(5), pages 731-749, September.
    15. Guido Gentile & Sang Nguyen & Stefano Pallottino, 2005. "Route Choice on Transit Networks with Online Information at Stops," Transportation Science, INFORMS, vol. 39(3), pages 289-297, August.
    16. Wei Yu & Hua Bai & Jun Chen & Xingchen Yan, 2019. "Analysis of Space-Time Variation of Passenger Flow and Commuting Characteristics of Residents Using Smart Card Data of Nanjing Metro," Sustainability, MDPI, vol. 11(18), pages 1-19, September.
    17. 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.
    18. Hörcher, Daniel & Graham, Daniel J. & Anderson, Richard J., 2017. "Crowding cost estimation with large scale smart card and vehicle location data," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 105-125.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yu, Liping & Liu, Huiran & Fang, Zhiming & Ye, Rui & Huang, Zhongyi & You, Yayun, 2023. "A new approach on passenger flow assignment with multi-connected agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cortés, Cristián E. & Donoso, Pedro & Gutiérrez, Leonel & Herl, Daniel & Muñoz, Diego, 2023. "A recursive stochastic transit equilibrium model estimated using passive data from Santiago, Chile," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    2. Hamdouch, Younes & Ho, H.W. & Sumalee, Agachai & Wang, Guodong, 2011. "Schedule-based transit assignment model with vehicle capacity and seat availability," Transportation Research Part B: Methodological, Elsevier, vol. 45(10), pages 1805-1830.
    3. Cats, Oded & West, Jens & Eliasson, Jonas, 2016. "A dynamic stochastic model for evaluating congestion and crowding effects in transit systems," Transportation Research Part B: Methodological, Elsevier, vol. 89(C), pages 43-57.
    4. Zhu, Yiwen & Koutsopoulos, Haris N. & Wilson, Nigel H.M., 2017. "A probabilistic Passenger-to-Train Assignment Model based on automated data," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 522-542.
    5. Xing Chen & Leishan Zhou & Yixiang Yue & Yu Zhou & Liwen Liu, 2018. "Data-Driven Method to Estimate the Maximum Likelihood Space–Time Trajectory in an Urban Rail Transit System," Sustainability, MDPI, vol. 10(6), pages 1-21, May.
    6. 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.
    7. Canca, David & Andrade-Pineda, José Luis & De los Santos, Alicia & Calle, Marcos, 2018. "The Railway Rapid Transit frequency setting problem with speed-dependent operation costs," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 494-519.
    8. 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.
    9. Khani, Alireza, 2019. "An online shortest path algorithm for reliable routing in schedule-based transit networks considering transfer failure probability," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 549-564.
    10. Liu, Jiangtao & Zhou, Xuesong, 2016. "Capacitated transit service network design with boundedly rational agents," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 225-250.
    11. Hänseler, Flurin S. & van den Heuvel, Jeroen P.A. & Cats, Oded & Daamen, Winnie & Hoogendoorn, Serge P., 2020. "A passenger-pedestrian model to assess platform and train usage from automated data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 948-968.
    12. Chen, Kang & Yang, Zhongzhen & Notteboom, Theo, 2014. "The design of coastal shipping services subject to carbon emission reduction targets and state subsidy levels," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 61(C), pages 192-211.
    13. Xu, Zhandong & Xie, Jun & Liu, Xiaobo & Nie, Yu (Marco), 2020. "Hyperpath-based algorithms for the transit equilibrium assignment problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
    14. Hamdouch, Younes & Szeto, W.Y. & Jiang, Y., 2014. "A new schedule-based transit assignment model with travel strategies and supply uncertainties," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 35-67.
    15. 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).
    16. Shang, Pan & Li, Ruimin & Guo, Jifu & Xian, Kai & Zhou, Xuesong, 2019. "Integrating Lagrangian and Eulerian observations for passenger flow state estimation in an urban rail transit network: A space-time-state hyper network-based assignment approach," Transportation Research Part B: Methodological, Elsevier, vol. 121(C), pages 135-167.
    17. Nassir, Neema & Hickman, Mark & Ma, Zhen-Liang, 2019. "A strategy-based recursive path choice model for public transit smart card data," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 528-548.
    18. Yap, Menno & Cats, Oded, 2021. "Taking the path less travelled: Valuation of denied boarding in crowded public transport systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 1-13.
    19. Oded Cats & Zafeira Gkioulou, 2017. "Modeling the impacts of public transport reliability and travel information on passengers’ waiting-time uncertainty," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 6(3), pages 247-270, September.
    20. Svanberg , Lisa & Pyddoke, Roger, 2020. "Policies for on-board crowding in public transportation : a literature review," Working Papers 2020:6, Swedish National Road & Transport Research Institute (VTI).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:6:p:2574-:d:336541. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.