Prediction of Traffic Congestion Based on LSTM Through Correction of Missing Temporal and Spatial Data

Abstract

With the rapid increase in vehicle use during the fourth Industrial Revolution, road resources have reached their supply limit. Active studies have therefore been conducted on intelligent transportation systems (ITSs) to realize traffic management systems utilizing fewer resources. As part of an ITS, real-time traffic services are provided to improve user convenience. Such services are applied to prevent traffic congestion and disperse existing traffic. Therefore, these services focus on immediacy at the expense of accuracy. As these services typically rely on measured data, the accuracy of the models are contingent on the data collection. Therefore, this study proposes a long short-term memory (LSTM)-based traffic congestion prediction approach based on the correction of missing temporal and spatial values. Before making predictions, the proposed prediction method applies pre-processing that consists of outlier removal using the median absolute deviation of the traffic data and the correction of temporal and spatial values using temporal and spatial trends and pattern data. In previous studies, data with time-series features have not been appropriately learned. To address this problem, the proposed prediction method uses an LSTM model for time-series data learning. To evaluate the performance of the proposed method, the mean absolute percentage error (MAPE) was calculated for comparison with other models. The MAPE of the proposed method was found to be the best of the compared models, at approximately 5%.

Keywords

• Long short-term memory
• traffic
• intelligent transportation system
• deep learning
• missing data correction
• big data-based AI