IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v153y2021ics136655452100185x.html
   My bibliography  Save this article

Optimizing driver menus under stochastic selection behavior for ridesharing and crowdsourced delivery

Author

Listed:
  • Horner, Hannah
  • Pazour, Jennifer
  • Mitchell, John E.

Abstract

Peer-to-peer logistics platforms coordinate independent drivers to fulfill requests for last mile delivery and ridesharing. To balance demand-side performance with driver autonomy, a new stochastic methodology provides drivers with a small but personalized menu of requests to choose from. This creates a Stackelberg game, in which the platform leads by deciding what menu of requests to send to drivers, and the drivers follow by selecting which request(s) they are willing to fulfill from their received menus. Determining optimal menus, menu size, and request overlaps in menus is complex as the platform has limited knowledge of drivers’ request preferences. Exploiting the problem structure when drivers signal willingness to participate, we reformulate our problem as an equivalent single-level Mixed Integer Linear Program (MILP) and apply the Sample Average Approximation (SAA) method. Computational tests recommend a training sample size for inputted SAA scenarios and a test sample size for completing performance analysis. Our stochastic optimization approach performs better than current approaches, as well as deterministic optimization alternatives. A simplified formulation ignoring ‘unhappy drivers’ who accept requests but are not matched is shown to produce similar objective values with a fraction of the runtime. A ridesharing case study of the Chicago Regional transportation network provides insights for a platform wanting to provide driver autonomy via menu creation. The proposed methods achieved high demand performance as long as the drivers are well compensated (e.g., even when drivers are allowed to reject requests, on average over 90% of requests are fulfilled when 80% of the fare goes to drivers; this drops to below 60% when only 40% of the fare goes to drivers). Thus, neither the platform nor the drivers benefit from low driver compensation due to its resulting low driver participation and thus low request fulfillment. Finally, for the cases tested, a maximum menu size of 5 is recommended as it produces good quality platform solutions without requiring much driver selection time.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:transe:v:153:y:2021:i:c:s136655452100185x
    DOI: 10.1016/j.tre.2021.102419
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S136655452100185X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2021.102419?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. Mourad, Abood & Puchinger, Jakob & Chu, Chengbin, 2019. "A survey of models and algorithms for optimizing shared mobility," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 323-346.
    2. Liran Einav & Chiara Farronato & Jonathan Levin, 2016. "Peer-to-Peer Markets," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 615-635, October.
    3. Punel, Aymeric & Stathopoulos, Amanda, 2017. "Modeling the acceptability of crowdsourced goods deliveries: Role of context and experience effects," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 105(C), pages 18-38.
    4. Long, Jiancheng & Tan, Weimin & Szeto, W.Y. & Li, Yao, 2018. "Ride-sharing with travel time uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 143-171.
    5. Mofidi, Seyed Shahab & Pazour, Jennifer A., 2019. "When is it beneficial to provide freelance suppliers with choice? A hierarchical approach for peer-to-peer logistics platforms," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 1-23.
    6. Kostas Bimpikis & Ozan Candogan & Daniela Saban, 2019. "Spatial Pricing in Ride-Sharing Networks," Operations Research, INFORMS, vol. 67(3), pages 744-769, May.
    7. Zha, Liteng & Yin, Yafeng & Du, Yuchuan, 2018. "Surge pricing and labor supply in the ride-sourcing market," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 708-722.
    8. Wang, Hai & Yang, Hai, 2019. "Ridesourcing systems: A framework and review," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 122-155.
    9. Wang, Yuan & Zhang, Dongxiang & Liu, Qing & Shen, Fumin & Lee, Loo Hay, 2016. "Towards enhancing the last-mile delivery: An effective crowd-tasking model with scalable solutions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 93(C), pages 279-293.
    10. Masoud, Neda & Jayakrishnan, R., 2017. "A decomposition algorithm to solve the multi-hop Peer-to-Peer ride-matching problem," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 1-29.
    11. Stiglic, Mitja & Agatz, Niels & Savelsbergh, Martin & Gradisar, Mirko, 2015. "The benefits of meeting points in ride-sharing systems," Transportation Research Part B: Methodological, Elsevier, vol. 82(C), pages 36-53.
    12. Cheng, Xi & Gou, Qinglong & Yue, Jinfeng & Zhang, Yan, 2019. "Equilibrium decisions for an innovation crowdsourcing platform," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 241-260.
    13. Furuhata, Masabumi & Dessouky, Maged & Ordóñez, Fernando & Brunet, Marc-Etienne & Wang, Xiaoqing & Koenig, Sven, 2013. "Ridesharing: The state-of-the-art and future directions," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 28-46.
    14. Agatz, Niels & Erera, Alan & Savelsbergh, Martin & Wang, Xing, 2012. "Optimization for dynamic ride-sharing: A review," European Journal of Operational Research, Elsevier, vol. 223(2), pages 295-303.
    15. Stiglic, M. & Agatz, N.A.H. & Savelsbergh, M.W.P. & Gradisar, M., 2015. "The Benefits of Meeting Points in Ride-sharing Systems," ERIM Report Series Research in Management ERS-2015-003-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    16. Hou, Liwen & Li, Dong & Zhang, Dali, 2018. "Ride-matching and routing optimisation: Models and a large neighbourhood search heuristic," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 143-162.
    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. Da, Yuwen & Gou, Qinglong & Liang, Chao, 2023. "Will self-gifting of streamers hurt unions? Analyzing the union’s compensation mechanism for a live streaming supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    2. Mancini, Simona & Gansterer, Margaretha, 2022. "Bundle generation for last-mile delivery with occasional drivers," Omega, Elsevier, vol. 108(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. Peng, Zixuan & Shan, Wenxuan & Zhu, Xiaoning & Yu, Bin, 2022. "Many-to-one stable matching for taxi-sharing service with selfish players," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 255-279.
    2. Meng Li & Guowei Hua & Haijun Huang, 2018. "A Multi-Modal Route Choice Model with Ridesharing and Public Transit," Sustainability, MDPI, vol. 10(11), pages 1-14, November.
    3. Wang, Jing-Peng & Ban, Xuegang (Jeff) & Huang, Hai-Jun, 2019. "Dynamic ridesharing with variable-ratio charging-compensation scheme for morning commute," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 390-415.
    4. Alnaggar, Aliaa & Gzara, Fatma & Bookbinder, James H., 2021. "Crowdsourced delivery: A review of platforms and academic literature," Omega, Elsevier, vol. 98(C).
    5. Amirmahdi Tafreshian & Neda Masoud & Yafeng Yin, 2020. "Frontiers in Service Science: Ride Matching for Peer-to-Peer Ride Sharing: A Review and Future Directions," Service Science, INFORMS, vol. 12(2-3), pages 44-60, June.
    6. Ke, Jintao & Yang, Hai & Zheng, Zhengfei, 2020. "On ride-pooling and traffic congestion," Transportation Research Part B: Methodological, Elsevier, vol. 142(C), pages 213-231.
    7. Behrend, Moritz & Meisel, Frank & Fagerholt, Kjetil & Andersson, Henrik, 2019. "An exact solution method for the capacitated item-sharing and crowdshipping problem," European Journal of Operational Research, Elsevier, vol. 279(2), pages 589-604.
    8. Sun, Yanshuo & Chen, Zhi-Long & Zhang, Lei, 2020. "Nonprofit peer-to-peer ridesharing optimization," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    9. Ke, Jintao & Yang, Hai & Li, Xinwei & Wang, Hai & Ye, Jieping, 2020. "Pricing and equilibrium in on-demand ride-pooling markets," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 411-431.
    10. Zhang, Ruolin & Masoud, Neda, 2021. "A distributed algorithm for operating large-scale ridesourcing systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 156(C).
    11. Hua, Shijia & Zeng, Wenjia & Liu, Xinglu & Qi, Mingyao, 2022. "Optimality-guaranteed algorithms on the dynamic shared-taxi problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    12. Zhong, Lin & Zhang, Kenan & (Marco) Nie, Yu & Xu, Jiuping, 2020. "Dynamic carpool in morning commute: Role of high-occupancy-vehicle (HOV) and high-occupancy-toll (HOT) lanes," Transportation Research Part B: Methodological, Elsevier, vol. 135(C), pages 98-119.
    13. Yan, Pengyu & Lee, Chung-Yee & Chu, Chengbin & Chen, Cynthia & Luo, Zhiqin, 2021. "Matching and pricing in ride-sharing: Optimality, stability, and financial sustainability," Omega, Elsevier, vol. 102(C).
    14. Guo, Jiaqi & Long, Jiancheng & Xu, Xiaoming & Yu, Miao & Yuan, Kai, 2022. "The vehicle routing problem of intercity ride-sharing between two cities," Transportation Research Part B: Methodological, Elsevier, vol. 158(C), pages 113-139.
    15. Mohammad Asghari & Seyed Mohammad Javad Mirzapour Al-E-Hashem & Yacine Rekik, 2022. "Environmental and social implications of incorporating carpooling service on a customized bus system," Post-Print hal-03598768, HAL.
    16. Mourad, Abood & Puchinger, Jakob & Chu, Chengbin, 2019. "A survey of models and algorithms for optimizing shared mobility," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 323-346.
    17. Ruijie Li & Yu (Marco) Nie & Xiaobo Liu, 2020. "Pricing Carpool Rides Based on Schedule Displacement," Transportation Science, INFORMS, vol. 54(4), pages 1134-1152, July.
    18. Huang, Zhihui & Long, Jiancheng & Szeto, W.Y. & Liu, Haoxiang, 2021. "Modeling and managing the morning commute problem with park-and-ride-sharing," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 190-226.
    19. Qian, Xinwu & Zhang, Wenbo & Ukkusuri, Satish V. & Yang, Chao, 2017. "Optimal assignment and incentive design in the taxi group ride problem," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 208-226.
    20. Omer Faruk Aydin & Ilgin Gokasar & Onur Kalan, 2020. "Matching algorithm for improving ride-sharing by incorporating route splits and social factors," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-23, March.

    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:transe:v:153:y:2021:i:c:s136655452100185x. 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/600244/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.