Study of Data-Driven Traffic Congestion Level—Taking Yangzhou as an Example

摘要

Traffic congestion levels change with traffic conditions of different cities. In this paper, we carry out data mining based on traffic flow data obtained by vehicle positioning and video detection. Three basic indicators are selected to measure traffic congestion level, including speed ratio, road link saturation, and intersection saturation. The index weight is determined by expert scoring and hierarchical analysis. A fuzzy evaluation method is developed to calculate the road link congestion level based on the principle of maximum membership degree. The secondary fuzzy evaluation method is applied to evaluate the regional congestion level considering the total driving time of each road link. According to the principle of congestion level division, the traffic congestion level surrounding scenic spots in Yangzhou is studied, and the disturbances of different influencing factors for typical regional traffic congestion are analyzed.