Deciphering Delivery Mobility: A City-Scale, Path-Reconstructed Trajectory Dataset of Instant Delivery Riders

TL;DR

This paper introduces a high-resolution, city-scale trajectory dataset of delivery riders in Beijing, reconstructed through a novel methodology, enabling detailed analysis of urban delivery mobility for research and optimization.

Contribution

It presents a new, high-fidelity trajectory dataset of delivery riders, reconstructed from open data and validated against ground-truth metrics, facilitating urban mobility research.

Findings

High correlation with actual travel metrics (r=0.92 for distance, r=0.79 for duration)

Provides a detailed, realistic dataset for urban logistics analysis

Enables diverse research in urban planning and transportation

Abstract

The rapid expansion of the on-demand economy has profoundly reshaped urban mobility and logistics, yet high-resolution trajectory data on delivery riders' consistent movements remains scarce. Here, we present a city-scale, high-resolution spatiotemporal trajectory dataset of on-demand instant delivery riders in Beijing. This dataset was produced through a path-reconstruction methodology applied to an open dataset containing delivery order information. Subsequently, detailed and continuous trajectories were reconstructed by simulating cycling routes via a major online map service to ensure they were realistically aligned. For validation, the reconstructed paths were compared against ground-truth travel metrics, revealing a strong correlation with actual travel patterns. The analysis yielded Pearson correlation coefficients of 0.92 for route distance and 0.79 for route duration. This high fidelity ensures the dataset's utility for describing delivery riders' mobility. This publicly available resource offers unprecedented opportunities for researchers in urban planning, transportation studies, logistics optimization, and computational social science to investigate rider behavior, model urban freight systems, and develop more efficient and sustainable city-wide logistics solutions.