A novel algorithm for urban traffic congestion detection based on GPS data compression

Traffic congestion exists in every big city of China. This paper designs a novel traffic congestion detection algorithm from two aspects. One is the offline traffic data processing and the other is congestion mode judgment by online monitoring. The offline data processing includes two pars: spatial information and temporal information in the trajectories. A trajectory is represented by a spatial path and a temporal sequence. This representation supports different compression approaches for spatial information and temporal information respectively, so that both spatial compression and temporal compression can achieve high compression effectiveness. The online monitoring is as following. Traffic congestion model is based on three parameters of traffic jams (average speed, density, traffic flow), then configured parameter values were calculated based on traffic data. Base on the rule of congestion threshold by city traffic management evaluation system, urban road design requirements and highways service level analysis of indicators and grading standards, we use standard function method to calculate the parameters of standardized integrated transport threshold, and then quantify the impact of each characteristic parameter congestion to achieve the goal. Finally, the road congestion is determined, and implement the traffic congestion judgment visualization.