Learning to Estimate Package Delivery Time in Mixed Imbalanced Delivery and Pickup Logistics Services
This work addresses a critical operational challenge for logistics companies by enhancing delivery time estimation, though it is incremental as it builds on existing methods with specific adaptations for mixed scenarios.
The paper tackles the problem of accurately estimating package delivery times in mixed logistics scenarios with imbalanced delivery and pickup tasks, proposing TransPDT, a Transformer-based multi-task model that improves prediction by addressing spatiotemporal dependencies and pickup constraints, and it is deployed in JD Logistics to track thousands of couriers handling hundreds of thousands of packages daily.
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' spatial mobility patterns are critical determinants of their delivery behavior, but have been insufficiently explored. To deal with these, we propose TransPDT, a Transformer-based multi-task package delivery time prediction model. We first employ the Transformer encoder architecture to capture the spatio-temporal dependencies of couriers' historical travel routes and pending package sets. Then we design the pattern memory to learn the patterns of pickup in the imbalanced dataset via attention mechanism. We also set the route prediction as an auxiliary task of delivery time prediction, and incorporate the prior courier spatial movement regularities in prediction. Extensive experiments on real industry-scale datasets demonstrate the superiority of our method. A system based on TransPDT is deployed internally in JD Logistics to track more than 2000 couriers handling hundreds of thousands of packages per day in Beijing.