Improving Long-Term Traffic Prediction with Online Search Log Data

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

Abstract: Long-term traffic prediction is essential for both road managers and users to prepare for future congestion. However, most existing studies have only focused on short-term prediction. Moreover, few studies have effectively incorporated external data into long-term traffic prediction, even though traffic conditions are complexly influenced by various spatiotemporal factors. In this paper, we propose a novel method that utilizes online search log data for long-term traffic prediction on expressways. Online search logs reflect drivers’ travel intentions and external factors, such as weather conditions and events, which cannot be represented by historical traffic data. Based on a new analysis of the correlation between online search log data and real-world traffic, we use online search log data as potential future traffic volume in an LSTM-based encoder-decoder model. Experiments using a real-world dataset on an expressway known for frequent congestion show that the use of online search log data improves the metrics of MAE, RMSE, and MAPE in next-day traffic volume prediction by 8.1%, 12.5%, and 7.2% on average, respectively. Similarly, in speed prediction, the MAE, RMSE, and MAPE are reduced by 3.7%, 2.1%, and 11.8%, respectively. It is also shown that online search log data is particularly effective in predicting irregular congestion caused by sudden increases in traffic demand.