A Data-driven Method for Identifying Congestion State and Selecting Guided Vehicles for Urban Expressways

The inefficient traffic guidance is that travelers are reluctant to accept a single guidance scheme due to heterogeneous travel characteristics. This work proposes an accurate selection method based on the travel characteristics to ensure guidance performance, thus alleviating the peak-hour congestion of the expressways. The congested sections are extracted from a traffic condition dataset of the Gaode map, and the original congested sections are identified according to the correlation of traffic conditions between the congested sections and its adjacent ones. Besides, the travel characteristics of vehicles passing on the expressways are extracted based on the license plate recognition data, including the travel intensity on the expressways, the travel intensity on the ground roads, the dispersion of expressway departure time, and the diversity of the expressway path selection. The travelers significantly affecting the traffic condition of the expressways are identified by the K-means++ clustering algorithm, and appropriate guidance(i.e. staggered shift and detour)is recommended to the identified travelers based on their traveling characteristics. Taking the Suzhou expressway as a case study, the traffic guidance for the original congested sections can improve the traffic condition of congested sections. Type-3 vehicles(high-intensity travel and easy to detour)are the key targets, accounting for only 14% of the total number of vehicles using expressways in the morning peak of working day in one month. However, they constitute 51% of the total traffic volume. There are 47% of vehicles that can be recommended with personalized traffic guidance after congestion-state identification and guided-object selection.