Enhancing Multi-level Urban Instant Delivery Management via Infomap-based Hierarchical Community Detection

TL;DR

This paper presents a hierarchical community detection framework using Infomap to analyze urban delivery patterns, improving resource allocation and logistics management in complex city environments.

Contribution

It introduces a novel hierarchical clustering method based on Infomap for analyzing urban delivery data, enhancing understanding of spatial dependencies and logistics optimization.

Findings

Hierarchical detection reveals diverse urban spatial clusters.

Clusters align with urban layouts and delivery demands.

Framework improves resource allocation strategies.

Abstract

Efficient management of on-demand delivery systems is essential for modern urban logistics, especially in densely populated cities with complex spatial layouts. This study introduces a novel, computer-supported cooperative framework that utilizes Infomap-based hierarchical community detection to analyze spatial multilevel clustering patterns. The experiment was conducted to large scale on-demand delivery datasets from Shenzhen and Beijing, revealing integrated spatial clusters that align with cohesive urban layout. Through hierarchical detection, finer and fragmented clusters are identified, reflecting its diverse urban structure and delivery demands. The findings demonstrate the effectiveness of hierarchical community detection in uncovering spatial dependencies and optimizing resource allocation and delivery strategies. This framework provides practical insights for urban logistics, enabling tailored approaches for business hub placement, route allocation, and adaptive resource management.