Interdisciplinary Integration of Spatial Syntax and Urban Big Data: Building a New Generation Logistics Route Optimization Decision Framework

Abstract

This paper addresses the limitations of traditional logistics route optimization models, which overly rely on historical average data and struggle to adapt to dynamic urban traffic environments. It proposes an interdisciplinary decision-making framework integrating spatial syntax with urban big data. The research aims to enhance route optimization accuracy and robustness by constructing a “spatial-temporal” dual-driven model. This model integrates static road network topology attributes (integration and connectivity values) generated by Depthmap with dynamic multi-source big data (historical traffic flow, time, and climate). Results demonstrate that compared to traditional shortest path models, this framework significantly reduces average travel time while improving network load balance and prediction accuracy. The conclusion asserts that this model effectively resolves the “shortest path not always optimal” challenge, providing a decision support tool for smart logistics that combines theoretical depth with practical value.