Identification and Analysis of Urban Traffic Congestion Based on Multi-Source Data

With the increasing number of motor vehicles in China, road congestion is becoming more and more serious. Quickly identifying bottlenecks of urban road congestion and carry out causation mechanism analysis have become effective ways of alleviating traffic congestion. With the continuous development of intelligent transportation systems, abundant traffic data sources provide us with a lot of traffic information. Therefore, this paper uses floating car data to identify road congestion bottleneck sections. Then, the workplace and residence distribution and residents' OD travel characteristics of the area near the road are obtained through mobile phone signaling data. Finally, traffic conditions and road facilities are analyzed by using the field survey data. The analysis results can be combined to carry out causation mechanism analysis on congestion identification results. This study can identify frequent bottleneck locations effectively and determine causes of traffic congestion from a deep level.