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

Chengbo Zhang; Zuopeng Xiao

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arXiv · 2507.11584

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

Chengbo Zhang·Zuopeng Xiao

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Abstract

The rapid expansion of the on-demand economy has profoundly reshaped urban mobility and logistics, yet open data linking multi-stop delivery task sequences with route geometry remain scarce. Here, we present a city-scale, path-reconstructed route dataset for instant-delivery tasks in Beijing, built from desensitized platform records from Ele.me. The dataset contains 79,648 reconstructed delivery-wave records associated with 986 anonymized courier identifiers and covering 267,529 orders during February 2020. For each wave, Assign, Pickup, and Delivery action points are ordered by source-record timestamps, and Amap cycling routes are queried between consecutive action points to create continuous route geometries. The resulting dataset records navigation-based riding paths and the associated action sequence for each delivery wave, revealing insightful spatiotemporal regularities. Source-record validation shows strong agreement for distance and moderate agreement for duration. This urban data resource enables researchers in urban analytics and transportation management to investigate delivery activities, model urban logistics systems, and develop sustainable policies.

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Chengbo Zhang, Zuopeng Xiao. 2025-07-15. Deciphering Delivery Mobility: A City-Scale, Path-Reconstructed Trajectory Dataset of Instant Delivery Riders. https://arxiv.org/abs/2507.11584

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