IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v141y2020icp356-372.html
   My bibliography  Save this article

Behavioral response to promotion-based public transport demand management: Longitudinal analysis and implications for optimal promotion design

Author

Listed:
  • Ma, Zhenliang
  • Koutsopoulos, Haris N.
  • Liu, Tianyou
  • Basu, Abhishek Arunasis

Abstract

Increasing ridership in the urban rail systems in major cities is outpacing their designed capacity. Promotion based demand management can facilitate better utilization of the available capacity of the existing system when the investment and opportunity to expand the system are limited. While several studies address short-term behavioral responses to such promotions using before and after analysis, how behavioral changes are sustained in the long run is also very important as well as differences in response among different user groups. Using an extensive smart card dataset over two years from Hong Kong’s urban heavy railway system, this paper explores the longitudinal behavior of passengers in response to a promotion aiming at changing passengers’ travel period from peak to the pre-peak. The approach uses customer segmentation to understand the heterogeneous response of different groups. Users who have high flexibility in schedule and familiarity with the system and travel long distances tend to be more likely to change their travel periods to take advantage of the discount. The longitudinal promotion analysis reveals that 35–40% of passengers who initially adopted the promotion will eventually revert to their previous travel time periods. The results suggest that the promotion designs should be adjusted/renewed over time to sustain the initial response given the attrition of early adopters. Based on the behavioral analysis, an ‘optimal’ promotion design approach is applied to examine the effectiveness of promotion strategies given different behavioral responses over time, heterogeneous group behavior, and constraints on the investment budgets and performance requirements. The promotion design using group-specific response can better target price-sensitive users, hence improves its effectiveness over time, while the design based on the average response shows a significant performance decrease. However, the optimal design schemes using different behavioral responses are relatively consistent in terms of the selected stations for promotion, though some differences exist in the discount levels and time periods for the areas where there can be more early morning travelers. From a design perspective, there is not much difference in the promotion effectiveness regardless of the behavioral response assumed for the design.

Suggested Citation

  • Ma, Zhenliang & Koutsopoulos, Haris N. & Liu, Tianyou & Basu, Abhishek Arunasis, 2020. "Behavioral response to promotion-based public transport demand management: Longitudinal analysis and implications for optimal promotion design," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 356-372.
  • Handle: RePEc:eee:transa:v:141:y:2020:i:c:p:356-372
    DOI: 10.1016/j.tra.2020.09.027
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856420307382
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tra.2020.09.027?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Anne Halvorsen & Haris N. Koutsopoulos & Zhenliang Ma & Jinhua Zhao, 0. "Demand management of congested public transport systems: a conceptual framework and application using smart card data," Transportation, Springer, vol. 0, pages 1-29.
    2. Luo, Qi & Saigal, Romesh & Chen, Zhibin & Yin, Yafeng, 2019. "Accelerating the adoption of automated vehicles by subsidies: A dynamic games approach," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 226-243.
    3. Tillema, Taede & Ben-Elia, Eran & Ettema, Dick & van Delden, Janet, 2013. "Charging versus rewarding: A comparison of road-pricing and rewarding peak avoidance in the Netherlands," Transport Policy, Elsevier, vol. 26(C), pages 4-14.
    4. Yizhou Zhang & Erik Jenelius & Karl Kottenhoff, 2017. "Impact of real-time crowding information: a Stockholm metro pilot study," Public Transport, Springer, vol. 9(3), pages 483-499, October.
    5. Wang, Hai & Yang, Hai, 2019. "Ridesourcing systems: A framework and review," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 122-155.
    6. Anne Halvorsen & Haris N. Koutsopoulos & Zhenliang Ma & Jinhua Zhao, 2020. "Demand management of congested public transport systems: a conceptual framework and application using smart card data," Transportation, Springer, vol. 47(5), pages 2337-2365, October.
    7. Ma, Xiaolei & Liu, Congcong & Wen, Huimin & Wang, Yunpeng & Wu, Yao-Jan, 2017. "Understanding commuting patterns using transit smart card data," Journal of Transport Geography, Elsevier, vol. 58(C), pages 135-145.
    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. Cong-Jian Liu & Fang-Kai Wang & Zhuang-Zhuang Wang & Tao Wang & Ze-Hao Jiang, 2022. "Autonomous Vehicles for Enhancing Expressway Capacity: A Dynamic Perspective," Sustainability, MDPI, vol. 14(9), pages 1-14, April.
    2. Lisa Dang & Widar von Arx, 2021. "How Can Rail Use for Leisure and Tourism Be Promoted? Using Leisure and Mobility Orientations to Segment Swiss Railway Customers," Sustainability, MDPI, vol. 13(11), pages 1-19, May.
    3. Lehua Bi & Shaorui Zhou & Jianjie Ke & Xiaoming Song, 2023. "Knowledge-Mapping Analysis of Urban Sustainable Transportation Using CiteSpace," Sustainability, MDPI, vol. 15(2), pages 1-29, January.
    4. Chen, Ruoyu & Zhou, Jiangping, 2022. "Fare adjustment’s impacts on travel patterns and farebox revenue: An empirical study based on longitudinal smartcard data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 111-133.
    5. Cardell-Oliver, Rachel & Olaru, Doina, 2022. "CIAM: A data-driven approach for classifying long-term engagement of public transport riders at multiple temporal scales," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 321-336.

    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. Li, Xiaonan & Li, Xiangyong & Wang, Hai & Shi, Junxin & Aneja, Y.P., 2022. "Supply regulation under the exclusion policy in a ride-sourcing market," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 69-94.
    2. Shi, Ziyi & Xu, Meng & Song, Yancun & Zhu, Zheng, 2024. "Multi-Platform dynamic game and operation of hybrid Bike-Sharing systems based on reinforcement learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(C).
    3. Zhong, Yuanguang & Zillmann, Stefan & Zhang, Ruijie & Zhou, Yong-Wu & Xie, Wei, 2023. "Vehicle repositioning for a ride-sourcing network system providing differentiated services," Transportation Research Part B: Methodological, Elsevier, vol. 170(C), pages 221-243.
    4. Busscher, Tim & Tillema, Taede & Arts, Jos, 2015. "In search of sustainable road infrastructure planning: How can we build on historical policy shifts?," Transport Policy, Elsevier, vol. 42(C), pages 42-51.
    5. Sapan Tiwari & Neema Nassir & Patricia Sauri Lavieri, 2024. "Smart Insertion Strategies for Sustainable Operation of Shared Autonomous Vehicles," Sustainability, MDPI, vol. 16(12), pages 1-28, June.
    6. Jihui Ma & Cuiying Song & Avishai (Avi) Ceder & Tao Liu & Wei Guan, 2017. "Fairness in optimizing bus-crew scheduling process," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-19, November.
    7. Yong, Juan & Zheng, Linjiang & Mao, Xiaowen & Tang, Xi & Gao, Ang & Liu, Weining, 2021. "Mining metro commuting mobility patterns using massive smart card data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    8. Maya Abou-Zeid & Satoshi Fujii, 2016. "Travel satisfaction effects of changes in public transport usage," Transportation, Springer, vol. 43(2), pages 301-314, March.
    9. Hao, Wu & Martin, Layla, 2022. "Prohibiting cherry-picking: Regulating vehicle sharing services who determine fleet and service structure," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    10. Pieroni, Caio & Giannotti, Mariana & Alves, Bianca B. & Arbex, Renato, 2021. "Big data for big issues: Revealing travel patterns of low-income population based on smart card data mining in a global south unequal city," Journal of Transport Geography, Elsevier, vol. 96(C).
    11. Horner, Hannah & Pazour, Jennifer & Mitchell, John E., 2021. "Optimizing driver menus under stochastic selection behavior for ridesharing and crowdsourced delivery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    12. Tong, Zhaomin & Zhang, Ziyi & An, Rui & Liu, Yaolin & Chen, Huiting & Xu, Jiwei & Fu, Shihang, 2024. "Detecting anomalous commuting patterns: Mismatch between urban land attractiveness and commuting activities," Journal of Transport Geography, Elsevier, vol. 116(C).
    13. Ke, Jintao & Wang, Ce & Li, Xinwei & Tian, Qiong & Huang, Hai-Jun, 2024. "Equilibrium analysis for on-demand food delivery markets," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).
    14. Liu, Yang & Li, Sen, 2023. "An economic analysis of on-demand food delivery platforms: Impacts of regulations and integration with ride-sourcing platforms," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 171(C).
    15. Zhu, Zheng & Xu, Ailing & He, Qiao-Chu & Yang, Hai, 2021. "Competition between the transportation network company and the government with subsidies to public transit riders," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    16. Zhong, Yuanguang & Lan, Yibo & Chen, Zhi & Yang, Jiazi, 2023. "On-demand ride-hailing platforms with heterogeneous quality-sensitive customers: Dedicated system or pooling system?," Transportation Research Part B: Methodological, Elsevier, vol. 173(C), pages 247-266.
    17. Daganzo, Carlos F. & Ouyang, Yanfeng & Yang, Haolin, 2020. "Analysis of ride-sharing with service time and detour guarantees," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 130-150.
    18. Wang, Yihong & Correia, Gonçalo Homem de Almeida & de Romph, Erik & Timmermans, H.J.P., 2017. "Using metro smart card data to model location choice of after-work activities: An application to Shanghai," Journal of Transport Geography, Elsevier, vol. 63(C), pages 40-47.
    19. Di, Yining & Xu, Meng & Zhu, Zheng & Yang, Hai & Chen, Xiqun, 2022. "Analysis of ride-sourcing drivers' working Pattern(s) via spatiotemporal work slices: A case study in Hangzhou," Transport Policy, Elsevier, vol. 125(C), pages 336-351.
    20. Chen, Wendong & Cheng, Long & Chen, Xuewu & Chen, Jingxu & Cao, Mengqiu, 2021. "Measuring accessibility to health care services for older bus passengers: A finer spatial resolution," Journal of Transport Geography, Elsevier, vol. 93(C).

    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:eee:transa:v:141:y:2020:i:c:p:356-372. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    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.