Early Identification of Recurrent Congestion in Heterogeneous Urban Traffic

Urban traffic congestion has become a critical issue that not only affects the quality of daily lives but also harms the environment and economy. Traffic patterns are recurrent in nature, so is congestion. However, little attention has been paid to the development of methods that would enable early warning of the formation of congestion and its propagation. This paper proposes a method for automated early congestion detection operating over time horizons ranging from half an hour to three hours. The method uses a deep learning technique, Convolutional Neural Networks (CNN), and adapts it to the specific context of urban roads. Empirical results are reported from a busy traffic corridor in the city of Bath. Comprehensive evaluation metrics, including Detection Rate, False Positive Rate and Mean Time to Detection, are used to evaluate the performance of the proposed method compared to more conventional machine learning methods including Feed-forward Neural Network and Random Forest. The results indicate that recurrent congestion can indeed be predicted before it occurs and demonstrates that CNN based method offers superior detection accuracy compared to the conventional machine learning methods in this context.