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E-Commerce Delivery Demand Modeling Framework for An Agent-Based Simulation Platform

Author

Listed:
  • Takanori Sakai
  • Yusuke Hara
  • Ravi Seshadri
  • Andr'e Alho
  • Md Sami Hasnine
  • Peiyu Jing
  • ZhiYuan Chua
  • Moshe Ben-Akiva

Abstract

The e-commerce delivery demand has grown rapidly in the past two decades and such trend has accelerated tremendously due to the ongoing coronavirus pandemic. Given the situation, the need for predicting e-commerce delivery demand and evaluating relevant logistics solutions is increasing. However, the existing simulation models for e-commerce delivery demand are still limited and do not consider the delivery options and their attributes that shoppers face on e-commerce order placements. We propose a novel modeling framework which jointly predicts the average total value of e-commerce purchase, the purchase amount per transaction, and delivery option choices. The proposed framework can simulate the changes in e-commerce delivery demand attributable to the changes in delivery options. We assume the model parameters based on various sources of relevant information and conduct a demonstrative sensitivity analysis. Furthermore, we have applied the model to the simulation for the Auto-Innovative Prototype city. While the calibration of the model using real-world survey data is required, the result of the analysis highlights the applicability of the proposed framework.

Suggested Citation

  • Takanori Sakai & Yusuke Hara & Ravi Seshadri & Andr'e Alho & Md Sami Hasnine & Peiyu Jing & ZhiYuan Chua & Moshe Ben-Akiva, 2020. "E-Commerce Delivery Demand Modeling Framework for An Agent-Based Simulation Platform," Papers 2010.14375, arXiv.org.
    • Handle: RePEc:arx:papers:2010.14375
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    References listed on IDEAS

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