IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i10p2791-d231565.html
   My bibliography  Save this article

A Travel Behavior-Based Skip-Stop Strategy Considering Train Choice Behaviors Based on Smartcard Data

Author

Listed:
  • Eun Hak Lee

    (Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Korea)

  • Inmook Lee

    (Future Transport Policy Research Division, Korea Railroad Research Institute, Uiwang 16105, Korea)

  • Shin-Hyung Cho

    (Institute of Engineering Research, Seoul National University, Seoul 08826, Korea)

  • Seung-Young Kho

    (Department of Civil and Environmental Engineering and Institute of Construction and Environmental Engineering, Seoul National University, Seoul 08826, Korea)

  • Dong-Kyu Kim

    (Department of Civil and Environmental Engineering and Institute of Construction and Environmental Engineering, Seoul National University, Seoul 08826, Korea)

Abstract

This study analyzes a skip-stop strategy considering four types of train choice behavior with smartcard data. The proposed model aims to minimize total travel time with realistic constraints such as facility condition, operational condition, and travel behavior. The travel time from smartcard data is decomposed by two distributions of the express trains and the local trains using a Gaussian mixture model. The utility parameters of the train choice model are estimated with the decomposed distribution using the multinomial logit model. The optimal solution is derived by a genetic algorithm to designate the express stations of the Bundang line in the Seoul metropolitan area. The results indicate the travel times of the transfer-based strategy and the high ridership-based strategy are estimated to be 21.2 and 19.7 min/person, respectively. Compared to the travel time of the current system, the transfer-based strategy has a 5.8% reduction and the high ridership-based strategy has a 12.2% reduction. For the travel behavior-based strategy, the travel time was estimated to be 18.7 minutes, the ratio of the saved travel time is 17.9%, and the energy consumption shows that the travel behavior-based strategy consumes 305,437 (kWh) of electricity, which is about 12.7% lower compared to the current system.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:10:p:2791-:d:231565
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/10/2791/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/10/2791/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kroon, Leo & Maróti, Gábor & Helmrich, Mathijn Retel & Vromans, Michiel & Dekker, Rommert, 2008. "Stochastic improvement of cyclic railway timetables," Transportation Research Part B: Methodological, Elsevier, vol. 42(6), pages 553-570, July.
    2. 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.
    3. Xueqiao Yu & Maoxiang Lang & Yang Gao & Kai Wang & Ching-Hsia Su & Sang-Bing Tsai & Mingkun Huo & Xiao Yu & Shiqi Li, 2018. "An Empirical Study on the Design of China High-Speed Rail Express Train Operation Plan—From a Sustainable Transport Perspective," Sustainability, MDPI, vol. 10(7), pages 1-19, July.
    4. Nachtigall, Karl & Voget, Stefan, 1997. "Minimizing waiting times in integrated fixed interval timetables by upgrading railway tracks," European Journal of Operational Research, Elsevier, vol. 103(3), pages 610-627, December.
    5. 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.
    6. Lu, Xiao-Shan & Liu, Tian-Liang & Huang, Hai-Jun, 2015. "Pricing and mode choice based on nested logit model with trip-chain costs," Transport Policy, Elsevier, vol. 44(C), pages 76-88.
    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. Mu Lin & Zhengdong Huang & Tianhong Zhao & Ying Zhang & Heyi Wei, 2022. "Spatiotemporal Evolution of Travel Pattern Using Smart Card Data," Sustainability, MDPI, vol. 14(15), pages 1-16, August.
    2. Maosheng Li & Hangcong Li, 2022. "Optimal Design of Subway Train Cross-Line Operation Scheme Based on Passenger Smart Card Data," Sustainability, MDPI, vol. 14(11), pages 1-17, May.
    3. Eun Hak Lee & Hosuk Shin & Shin-Hyung Cho & Seung-Young Kho & Dong-Kyu Kim, 2019. "Evaluating the Efficiency of Transit-Oriented Development Using Network Slacks-Based Data Envelopment Analysis," Energies, MDPI, vol. 12(19), pages 1-15, September.

    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. Sels, P. & Dewilde, T. & Cattrysse, D. & Vansteenwegen, P., 2016. "Reducing the passenger travel time in practice by the automated construction of a robust railway timetable," Transportation Research Part B: Methodological, Elsevier, vol. 84(C), pages 124-156.
    2. 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.
    3. Sparing, Daniel & Goverde, Rob M.P., 2017. "A cycle time optimization model for generating stable periodic railway timetables," Transportation Research Part B: Methodological, Elsevier, vol. 98(C), pages 198-223.
    4. Cordone, Roberto & Redaelli, Francesco, 2011. "Optimizing the demand captured by a railway system with a regular timetable," Transportation Research Part B: Methodological, Elsevier, vol. 45(2), pages 430-446, February.
    5. 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.
    6. Niu, Huimin & Zhou, Xuesong & Gao, Ruhu, 2015. "Train scheduling for minimizing passenger waiting time with time-dependent demand and skip-stop patterns: Nonlinear integer programming models with linear constraints," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 117-135.
    7. Kang, Liujiang & Wu, Jianjun & Sun, Huijun & Zhu, Xiaoning & Gao, Ziyou, 2015. "A case study on the coordination of last trains for the Beijing subway network," Transportation Research Part B: Methodological, Elsevier, vol. 72(C), pages 112-127.
    8. 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.
    9. Wang, Wei (Walker) & Wang, David Z.W. & Zhang, Fangni & Sun, Huijun & Zhang, Wenyi & Wu, Jianjun, 2017. "Overcoming the Downs-Thomson Paradox by transit subsidy policies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 95(C), pages 126-147.
    10. Xu, Shu-Xian & Liu, Tian-Liang & Huang, Hai-Jun & Liu, Ronghui, 2018. "Mode choice and railway subsidy in a congested monocentric city with endogenous population distribution," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 413-433.
    11. Ling-Ling Xiao & Tian-Liang Liu & Hai-Jun Huang, 2021. "Tradable permit schemes for managing morning commute with carpool under parking space constraint," Transportation, Springer, vol. 48(4), pages 1563-1586, August.
    12. Schön, Cornelia & König, Eva, 2018. "A stochastic dynamic programming approach for delay management of a single train line," European Journal of Operational Research, Elsevier, vol. 271(2), pages 501-518.
    13. Rachel C. W. Wong & Tony W. Y. Yuen & Kwok Wah Fung & Janny M. Y. Leung, 2008. "Optimizing Timetable Synchronization for Rail Mass Transit," Transportation Science, INFORMS, vol. 42(1), pages 57-69, February.
    14. Wan, Li & Tang, Junqing & Wang, Lihua & Schooling, Jennifer, 2021. "Understanding non-commuting travel demand of car commuters – Insights from ANPR trip chain data in Cambridge," Transport Policy, Elsevier, vol. 106(C), pages 76-87.
    15. Li, Jiajie & Bai, Yun & Chen, Yao & Yang, Lingling & Wang, Qian, 2022. "A two-stage stochastic optimization model for integrated tram timetable and speed control with uncertain dwell times," Energy, Elsevier, vol. 260(C).
    16. Jin Qin & Wenxuan Qu & Xuanke Wu & Yijia Zeng, 2019. "Differential Pricing Strategies of High Speed Railway Based on Prospect Theory: An Empirical Study from China," Sustainability, MDPI, vol. 11(14), pages 1-17, July.
    17. Huang, Yeran & Yang, Lixing & Tang, Tao & Gao, Ziyou & Cao, Fang, 2017. "Joint train scheduling optimization with service quality and energy efficiency in urban rail transit networks," Energy, Elsevier, vol. 138(C), pages 1124-1147.
    18. Jiang, Xiaodan & Fan, Houming & Luo, Meifeng & Xu, Zhenlin, 2020. "Strategic port competition in multimodal network development considering shippers’ choice," Transport Policy, Elsevier, vol. 90(C), pages 68-89.
    19. Harshad Khadilkar, 2017. "Data-Enabled Stochastic Modeling for Evaluating Schedule Robustness of Railway Networks," Transportation Science, INFORMS, vol. 51(4), pages 1161-1176, November.
    20. Di Wu & Juan Carlos Martín, 2022. "Research on Passengers’ Preference for High-Speed Railways (HSRs) and High-Speed Trains (HSTs)," Sustainability, MDPI, vol. 14(3), pages 1-20, January.

    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:11:y:2019:i:10:p:2791-:d:231565. 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.