Research on seeds discovery model to detect traffic congestions

With rapid progress of urbanization, the increased number of vehicles has created traffic problems in many cities across the globe. Understanding the basic laws governing the mobility patterns of the vehicles attracts attention from different fields due to its importance for mobility modeling, urban computing and traffic engineering. In this paper, we use a data driven approach to find out the most influential causes, namely Optimal Seeds to provide fundamental clues for tackling the traffic anomalies with minimum efforts. First, a region-based model is proposed to measure the correlation among traffic anomalies. Second, we introduce the J-KEY-Region problem to find the top J most influential regions, which is unfortunately shown to be NP-hard. Third, we present two approximation approaches employing independent cascade (IC) model to address the J-KEY-Region is achieved. In addition, a pruning method is applied to improve the efficiency of our algorithm. At last, we conduct intensive experiments on large real-world GPS trajectories generated by more than 10,200 taxis in Shanghai to demonstrate the performance of our approaches.

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