Mining urban recurrent congestion evolution patterns from GPS-equipped vehicle mobility data

In this study, we developed a method for measuring urban Recurrent Congestion (RC) evolution patterns based on grid divisions and GPS-equipped vehicle mobility data. The method uses a three-step process: Detecting congestion in the grids, distinguishing RC from Non-Recurrent Congestion (NRC), and measuring the RC evolution pattern. A series of indicators were also established which reflect the RC evolution pattern. We conducted an experiment to evaluate the proposed method using GPS trajectory data collected from taxis in Harbin, China, and compared the results against real traffic information and field survey results. We hope that the findings discussed in this paper provide a better understanding of urban RC evolution patterns.

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