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

Author

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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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