arXiv subjects

Zuopeng Xiao

Publications and source records attributed to Zuopeng Xiao.

3 recordsLinked to original sources

arXiv2026

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

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.

physics.soc-ph↗

arXiv2026

Large Language Models as Delivery Rider: Generating Instant Food Delivery Riders' Routing Decision with LLM Agent Framework

The utilization of Large Language Models (LLMs) to power human-like agents has shown remarkable potential in simulating individual mobility pattern. However, a significant gap remains in modeling cohorts of agents in dynamic and interactive systems where they must take strategic routing decisions to response mobility-specific task. To bridge this gap, we introduce LLM-DR, a novel agent framework designed to simulate the heterogeneous decision-making of riders in the on-demand instant delivery task scenario. Our framework is founded on two principles: 1) Empirically-grounded personas, where we use unsupervised clustering on a large-scale, real-world trajectory dataset to identify four distinct rider work strategies; and 2) Reasoning-based routing process, where each persona is instantiated as an LLM agent that employs a structured Chain-of-Thought (CoT) process to make human-like routing choices. This framework enables the construction of high-fidelity simulations to investigate how the strategic composition of a rider workforce influences system-level outcomes regarding their mobility pattern. We validate our framework on an real-world instant deliver order datasets, demonstrating its capacity to model complex rider behavior in an interactive market scenario. This work provides pioneering findings in agentic mobility system empowered by LLM.

physics.soc-ph↗

arXiv2024

Uncover the Dynamic Community Structure of Instant Delivery Network

The rise of instant delivery services has reshaped urban spatial structures through the interaction between suppliers and consumers. However, limited research has explored the spatiotemporal dynamics of delivery network structures. This study constructs a time-dependent, multi-layer instant delivery network in the case city of Beijing using a large-scale dataset from Eleme, organized into 500m grid units. A dynamic community detection method identifies evolving community structures over time. The results reveal 309 dynamic communities, with an average size of 13.78 square kilometers. Communities form in the morning, expand, stabilize, then contract, and disappear by night. Key factors influencing stability include building area and residential population, while online retail and service facilities contribute to instability. These findings offer insights into the spatial structure of instant delivery networks and the factors driving their dynamics, with practical implications for optimizing platform strategies, resource allocation, and urban transportation planning.

physics.soc-ph↗