Traffic Congestion Prediction Using Toll and Route Search Log Data

Yuto Kosugi, Itsuki Matsunaga, Hangli Ge, Takashi Michikata, Noboru Koshizuka

Abstract: Predicting future people’s behavior can significantly impact various industries. Intelligent transportation system (ITS) advancement, in particular, depends on the ability to predict traffic congestion. If we can do so, we can encourage people to alter their behavior, which reduces traffic congestion, traffic accidents, travel times, and CO 2 emissions while also promoting the development of applications like dynamic pricing. However, predicting traffic congestion a few days ahead is challenging owing to its spatial and temporal dependence and its nature of being susceptible to external factors, such as weather, local events, and the pandemic of infectious diseases. For these reasons, previous studies have been limited to predicting the next few minutes to a few hours. To address this limitation, we propose using search log data of the toll route search service owned by East Nippon Expressway Co., Ltd. (NEXCO East), which operates expressway services in Japan, as these data are available several days before the prediction and comprehensively explain multiple external factors. We show that search log data can contribute to predicting people’s behavior by verifying the improvement in the accuracy of traffic congestion prediction.