Early perception of anomaly traffic patterns, both spatially and temporally, is of importance for emergency response in the smart cities. To capture the spatiotemporal correlations among traffic flows for city dynamics modeling in correspondence with normal states, we conduct sparse representation on taxi activity over spatially partitioned cells in a city. We can perceive the deviation from the normal evolution of traffic flows and find the traffic anomalies. This method roots in the ideal of global traffic flow network detection. Therefore, it is more informative than local statistics since traffic flows evolve in a mutually interacting manner to spread out all over the city. The experimental results confirm its predictive power in detecting spatiotemporal traffic anomalies.

在空间和时间上对异常交通模式的早期感知，对于智慧城市的应急响应至关重要。为了捕获与正常状态相对应的城市动力学建模的交通流之间的时空相关性，我们对城市中空间划分的单元格上的出租车活动进行了稀疏表示。我们可以感知到与正常交通流的偏离，并发现交通异常。该方法植根于全球交通流网络检测的理想状态。因此，它比本地统计信息更具信息性，因为交通流以相互影响的方式发展以遍及整个城市。实验结果证实了其在检测时空交通异常中的预测能力。