Detecting Hierarchical Congestion Intervals Based on the Fusion of Multi-source Highway Data

With the development of economy and urbanization in China, the issue of traffic congestion is increasingly prominent. Highways as the lifelines between cities are particularly vulnerable during the holidays. In order to manage highways more efficiently and regulate traffic more timely, it is necessary to detect congestion events on highways to realize the supervision of highway traffic. Using recognition data of highway path recognition system, transactions data of toll station, and GPS trajectory data of coach buses, touring buses and dangerous goods transport vehicles, this paper proposes a hierarchical congestion interval detection method which adopts fuzzy comprehensive evaluation algorithm to identify the traffic status of road sections. Taking the Changsha-Zhuzhou-Xiangtan urban agglomeration region of Hunan province as a case study, this paper uses the proposed method to detect and analyze traffic status of the experimental area in two weeks in February, 2018.