Mining Urban Congestion Evolution Characteristics Based on Taxi GPS Trajectories

Abstract

The taxi GPS trajectories involve sufficient temporal and spatial characteristics and make it easy for us to obtain potential knowledge for understanding human mobility pattern and urban traffic network dynamics. Sensing urban traffic conditions not only enables traffic management authority to improve urban traffic management. It can also provide decision-making for residents and taxi drivers. A spectral clustering method is proposed for sensing traffic congestion using taxi GPS trajectories. First, taxi GPS trajectories are pre-processed and matched with the urban road network established based on the primal graph representation. Second, the average speed of the road segments is obtained according to the taxi GPS trajectories and a dynamic weighted graph of urban road network is constructed to capture complicated urban traffic network. Then, a spectral clustering method is developed to detect the urban traffic congestion. Finally, the congestion evolution characteristics in Lanzhou, China are visualized and analyzed during different periods in the weekdays and weekends. Experimental results show that the proposed method can effectively detect traffic congestion, and the results are consistent with the usual actual experience. Compared with other traffic congestion methods, the proposed method can detect urban traffic congestion with wider coverage and lower cost. Therefore, the proposed method can be integrated into the classic intelligent traffic system, assisting urban traffic prediction, personal travel route plan, route planning and navigation application.