ZHANG Tian-yi, YANG Han, TIAN Jun-shan, WANG Xin-yuan
To utilize the expressway ETC dataset more efficiently and to improve the data processing speed, the main characteristics of ETC users and the potential problems of expressway were analyzed in depth. Taking the ETC passage data of expressway entrance/exit in a province of China in June 2023 for an example, the data were cleaned by Python programming language. The time data were processed by using ring feature coding, and the K-means clustering algorithm was applied to process the data. The indicators (e.g., entrance time, exit time, and mileage of passage in the province) were mainly focused. The 3 core features (i.e., users’ toll mileage, speed, and driving time) were analyzed, and visualized with the assist of clustering centroids and radar charts. The result indicates that the passage efficiency is lower during evening time. The problems of fatigue driving in the evening and speeding in the midnight are more prominent. According to the mileage analysis, the daytime is mainly dominated by short-distance and medium-distance users, and the long-distance users tend to enter the expressway in the morning, while there is a large number of commuter vehicles on the expressway. In terms of speed analysis, the low-speed group is mostly short-distance vehicles. The application of K-means clustering algorithm makes the data processing process fast and reliable. Combining with more ETC data, it can provide further insights into the main groups and conditions of expressway access. The study result can provide a strong basis for the development of differentiated toll policies, e.g., analyzing the time of entering expressway through clustering, determining the peak time and trough time, increasing the fee in peak time and reducing in trough time. It can improve the access efficiency and balance the traffic flow of road network.