Accident-Focused Travel Time Prediction on Expressway Using Multivariate LSTM

Traffic congestion has become a serious social problem in many countries and cities. In recent years, with the popularization of car navigation systems and the advancement of traffic information provision technologies, there has been an increasing demand for more accurate congestion prediction. In particular, studies have shown that the use of machine learning for congestion prediction is effective, as it can take into account various factors affecting traffic volume, such as weather, events, and days of the week. In addition, existing research has confirmed that it is possible to predict congestion patterns in advance based on past traffic data. However, studies have pointed out that existing machine learning models struggle to ensure sufficient prediction accuracy for abrupt changes in traffic flow caused by sudden events such as traffic accidents. Since the occurrence of accidents is accompanied by discontinuous fluctuations, general time-series prediction models cannot appropriately reflect their impact, leading to a tendency for larger prediction errors. Therefore, there is an urgent need to develop prediction methods that can adapt to sudden changes in traffic conditions. To address this issue, this study aims to construct a machine learning model using multivariate LSTM (Long Short-Term Memory) to achieve more accurate congestion prediction. LSTM excels at capturing long-term dependencies in time-series data and is adept at making predictions based on past data. Thus, it is highly effective in learning the occurrence trends of congestion with certain patterns. Specifically, the purpose of this study is to analyze the trends of prediction errors when traffic accidents occur and to construct a congestion prediction framework that considers the impact of accidents. In this way, we strive to realize a congestion prediction model that can adapt to sudden changes in traffic conditions, contributing to the optimization of traffic flow and the alleviation of congestion.