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An incentive-based delivery scheme and its effect evaluated via explainable machine learning

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  • Wang, Yang
  • Sun, Yu
  • Lai, Jianhui
  • Chen, Yanyan
  • Holguín-Veras, José

Abstract

The current delivery mode acquiesces E-shopping consumers to provide only a single address for delivery, despite their potential to have multiple addresses available. Inspired by this, we propose a new delivery mode with an economic incentive scheme to encourage consumers to provide more addresses and empower the delivery operator to determine the final delivery address following a certain optimization criteria. To examine the incentive's effect, we conducted a survey. The survey reveals a substantial, near-linear impact on promoting multiple address provision through the incentive, resulting in a 32% increase in consumers providing additional addresses. We develop an eXtreme Gradient Boosting model, which outperformed Logistic Regression and Support Vector Machine, to explore the relationship between address provision decision and E-shopping behavior. Augmented by Shapley Additive Explanations, the model can interpret how both the incentive and E-shopping behavior influence address provision. In addition to the incentive, factors such as the number of available addresses and the average price of the parcel also significantly influence the decision-making process for providing delivery addresses. The insights extracted from this study can provide a foundation for policymakers to establish more practical delivery management policies.

Suggested Citation

  • Wang, Yang & Sun, Yu & Lai, Jianhui & Chen, Yanyan & Holguín-Veras, José, 2025. "An incentive-based delivery scheme and its effect evaluated via explainable machine learning," Transport Policy, Elsevier, vol. 162(C), pages 559-574.
  • Handle: RePEc:eee:trapol:v:162:y:2025:i:c:p:559-574
    DOI: 10.1016/j.tranpol.2025.01.004
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    References listed on IDEAS

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    1. Boggio-Marzet, Alessandra & Villa-Martínez, Rafael & Monzón, Andrés, 2023. "Selection of policy actions for e-commerce last-mile delivery in cities: An online multi-actor multi-criteria evaluation," Transport Policy, Elsevier, vol. 142(C), pages 15-27.
    2. Fessler, Andreas & Thorhauge, Mikkel & Mabit, Stefan & Haustein, Sonja, 2022. "A public transport-based crowdshipping concept as a sustainable last-mile solution: Assessing user preferences with a stated choice experiment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 210-223.
    3. Mohri, Seyed Sina & Nassir, Neema & Thompson, Russell G. & Lavieri, Patricia Sauri, 2024. "Public transportation-based crowd-shipping initiatives: Are users willing to participate? Why not?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 182(C).
    4. Wang, Yi-Jia & Wang, Yue & Huang, George Q. & Lin, Ciyun, 2024. "Public acceptance of crowdsourced delivery from a customer perspective," European Journal of Operational Research, Elsevier, vol. 317(3), pages 793-805.
    5. 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.
    6. Tsai, Yao-Te & Tiwasing, Praewwanit, 2021. "Customers’ intention to adopt smart lockers in last-mile delivery service: A multi-theory perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).
    7. Wang, Yang & Bi, Mengyu & Lai, Jianhui & Wang, Chenxi & Chen, Yanyan & Holguín-Veras, José, 2024. "Recourse strategy for the routing problem of mobile parcel lockers with time windows under uncertain demands," European Journal of Operational Research, Elsevier, vol. 316(3), pages 942-957.
    8. Rafael Villa & Andrés Monzón, 2021. "Mobility Restrictions and E-Commerce: Holistic Balance in Madrid Centre during COVID-19 Lockdown," Economies, MDPI, vol. 9(2), pages 1-19, April.
    9. Bi, Hui & Li, Aoyong & Hua, Mingzhuang & Zhu, He & Ye, Zhirui, 2022. "Examining the varying influences of built environment on bike-sharing commuting: Empirical evidence from Shanghai," Transport Policy, Elsevier, vol. 129(C), pages 51-65.
    10. Holguín-Veras, José & Wang, Xiaokun (Cara) & Sánchez-Díaz, Iván & Campbell, Shama & Hodge, Stacey D. & Jaller, Miguel & Wojtowicz, Jeffrey, 2017. "Fostering unassisted off-hour deliveries: The role of incentives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 102(C), pages 172-187.
    11. Comi, Antonio, 2020. "A modelling framework to forecast urban goods flows," Research in Transportation Economics, Elsevier, vol. 80(C).
    12. Cebeci, Merve Seher & Tapia, Rodrigo Javier & Kroesen, Maarten & de Bok, Michiel & Tavasszy, Lóránt, 2023. "The effect of trust on the choice for crowdshipping services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 170(C).
    13. Zhou, Min & Zhao, Lindu & Kong, Nan & Campy, Kathryn S. & Xu, Ge & Zhu, Guiju & Cao, Xianye & Wang, Song, 2020. "Understanding consumers’ behavior to adopt self-service parcel services for last-mile delivery," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    14. Li, Xiaowei & Shi, Lanxin & Tang, Junqing & Yang, Chenyu & Zhao, Ting & Wang, Yuting & Wang, Wei, 2023. "Determinants of passengers' ticketing channel choice in rail transit systems: New evidence of e-payment behaviors from Xi'an, China," Transport Policy, Elsevier, vol. 140(C), pages 30-41.
    15. Yuen, Kum Fai & Wang, Xueqin & Ng, Li Ting Wendy & Wong, Yiik Diew, 2018. "An investigation of customers’ intention to use self-collection services for last-mile delivery," Transport Policy, Elsevier, vol. 66(C), pages 1-8.
    16. Zhu, Siying & Zhu, Feng, 2019. "Cycling comfort evaluation with instrumented probe bicycle," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 217-231.
    17. dell’Olio, Luigi & Moura, Jose Luis & Ibeas, Angel & Cordera, Ruben & Holguin-Veras, Jose, 2017. "Receivers’ willingness-to-adopt novel urban goods distribution practices," Transportation Research Part A: Policy and Practice, Elsevier, vol. 102(C), pages 130-141.
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