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

Takanori Sakai, Yusuke Hara, Ravi Seshadri, Andr\'e Alho, Md Sami Hasnine, Peiyu Jing, ZhiYuan Chua and Moshe Ben-Akiva

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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.

Date: 2020-10
New Economics Papers: this item is included in nep-cmp
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