Revealing spatiotemporal matching patterns between traffic flux and road resources using big geodata - A case study of Beijing

Mismatch between road system and 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), while the spatiotemporal heterogeneity of match and its causes, especially at larger scales, are still insufficiently studied. Herein, we proposed a framework for analyzing the match between traffic flux, i.e. the number of individuals driving into or out of a region per unit time, and road resources, using mobile phone data covering approximately 17 million users over one week in Beijing. Matches were measured through comparisons between the share of traffic flux 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, overallocated areas (traffic flux exceeds its corresponding shares of road resources) were mainly along arterial roads, while underallocated ones were in suburbs and gated residential communities. To explore the mechanism, four spatiotemporal matching modes were identified through a time series clustering method, and their distributions were explained by urban function based on ‘point-of-interest’ data.