Human-Centric Road Network Evaluation: A Multi-Dimensional Trajectory Big Data Approach

Traditional road network evaluation approaches frequently fail to capture the inherent complexities of human mobility patterns, leading to suboptimal urban transportation planning outcomes. This study introduces a novel human-centric evaluation framework that leverages multi-source trajectory big data, including mobile signaling, GPS tracking, and internet map query data. Through advanced physical and semantic data integration techniques, our framework provides comprehensive assessment of three critical dimensions: commuting behavior patterns, daily activity range coverage, and transportation hub accessibility. Applied to a metropolitan case study, the framework demonstrates superior capability in identifying spatial disparities and peak-hour congestion patterns compared to conventional static indicators. Furthermore, we employ Gini coefficient analysis to quantify spatial equity, enabling evidence-based urban planning and policy formulation.