Revealing Spatiotemporal Matching Patterns between Traffic Flow and Road Resources Using Multisource Big Geodata - a Case Study of Beijing

Mismatch between the road system and the spatiotemporal heterogeneity of traffic flow is a key reason for traffic congestion. Existing studies mainly focus on local regions or specific times (morning and evening peak) owing to the limited data source, while the spatiotemporal heterogeneity of match and its causes, especially at larger scales, are still insufficiently studied. Herein, we propose a framework for analyzing the match between traffic flow and road system, based on mobile phone data acquired of approximately 17 million users over one week in Beijing. Matches were measured through comparisons between the share of traffic flow and that of road resources both globally and locally. First, a global analysis with Gini coefficient revealed the match in Beijing is at a long-lasting low level. Then, the spatiotemporal disparity of match was examined via our proposed regional match index. Specifically, the overallocated areas (traffic flow exceeds its corresponding share of road resources) were mainly along arterial roads, while underallocated ones were mainly in suburbs and gated residential communities. To explore the mechanism, six spatiotemporal matching modes were identified through a time series clustering method, and the distribution for each mode was explained by urban function based on 'Point-of-Interest' (POI) data