Understanding the Regular Travel Behavior of Private Vehicles: An Empirical Evaluation and a Semi-Supervised Model

The mining of private vehicle trajectories can be used to establish the regular travel behavior of private vehicles and provide novel solutions to address problems related to urban traffic. With the help of automotive sensors in private vehicles, we can obtain a considerable amount of trajectory data on connected private vehicles. We herein assessed the regular travel behavior from connected private vehicle trajectory data. First, we explored the travel features of private vehicles through an empirical evaluation of move-and-stay frequency, time, duration, and distance. Second, we acquired the fitting distribution of private vehicle move-and-stay features based on the sum of the squared errors. We then proposed a move-and-stay feature based detection (MSFD) method, i.e., a semi-supervised model with incomplete data labels, to identify regular travelers from a massive private vehicle trajectory dataset. Extensive experiments were conducted on the private vehicle trajectory dataset collected from more than 68,069 private vehicles in Shenzhen, China, from June 1, 2016 to August 31, 2016. We compared the proposed MSFD with the existing baselines. The quantitative experimental results demonstrated that the MSFD outperformed these baselines.