# Learning to Estimate Package Delivery Time in Mixed Imbalanced Delivery and Pickup Logistics Services

arXiv: https://arxiv.org/abs/2505.00375 · HTML: https://mlsanity.com/paper/2505.00375/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 48 references that are in the map.
- First posted on arXiv 1 May 2025.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values](https://mlsanity.com/paper/1911.10273/) (2019)
- [A Simple Baseline for Travel Time Estimation using Large-Scale Trip Data](https://mlsanity.com/paper/1512.08580/) (2015)
- [Event-Aware Multimodal Mobility Nowcasting](https://mlsanity.com/paper/2112.08443/) (2021)
- [DRL4Route: A Deep Reinforcement Learning Framework for Pick-up and Delivery Route Prediction](https://mlsanity.com/paper/2307.16246/) (2023)
- [End-to-End Prediction of Parcel Delivery Time with Deep Learning for Smart-City Applications](https://mlsanity.com/paper/2009.12197/) (2020)
- [Inductive Graph Transformer for Delivery Time Estimation](https://mlsanity.com/paper/2211.02863/) (2022)
- [Dual Graph Multitask Framework for Imbalanced Delivery Time Estimation](https://mlsanity.com/paper/2302.07429/) (2023)

## Built on by (most cited first, 5 of 5)

- [MRGRP: Empowering Courier Route Prediction in Food Delivery Service with Multi-Relational Graph](https://mlsanity.com/paper/2505.11999/) (2025)
- [Conformal Predictive Distributions for Order Fulfillment Time Forecasting](https://mlsanity.com/paper/2505.17340/) (2025)
- [Uncertainty-Aware Delivery Delay Duration Prediction via Multi-Task Deep Learning](https://mlsanity.com/paper/2602.20271/) (2026)
- [UME: A Unified Meta-Generalization Framework for Cross-Domain ETA](https://mlsanity.com/paper/2606.00979/) (2026)
- [UniMod: Enhancing Multi-Modal Medical Diagnosis through Cross-Modality and Within-Modality Alignment](https://mlsanity.com/paper/2608.10316/) (2026)

## Abstract (arXiv)

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.