Learning to Estimate Package Delivery Time in Mixed Imbalanced Delivery and Pickup Logistics Services
Jinhui Yi, Huan Yan, Haotian Wang, Jian Yuan, Yong Li

TL;DR
This paper introduces TransPDT, a Transformer-based model that accurately predicts package delivery times in complex, imbalanced logistics scenarios by modeling spatio-temporal dependencies and pickup behaviors.
Contribution
The paper presents a novel multi-task Transformer model that captures courier movement patterns and pickup effects, improving delivery time estimation in mixed logistics environments.
Findings
TransPDT outperforms existing methods on real datasets.
System deployment tracks over 2000 couriers daily.
Model effectively handles pickup-delivery imbalances.
Abstract
Accurately estimating package delivery time is essential to the logistics industry, which enables reasonable work allocation and on-time service guarantee. This becomes even more necessary in mixed logistics scenarios where couriers handle a high volume of delivery and a smaller number of pickup simultaneously. However, most of the related works treat the pickup and delivery patterns on couriers' decision behavior equally, neglecting that the pickup has a greater impact on couriers' decision-making compared to the delivery due to its tighter time constraints. In such context, we have three main challenges: 1) multiple spatiotemporal factors are intricately interconnected, significantly affecting couriers' delivery behavior; 2) pickups have stricter time requirements but are limited in number, making it challenging to model their effects on couriers' delivery process; 3) couriers'…
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