Early identification of recurrent congestion in heterogeneous urban traffic

Urban traffic congestion has become a criticalissue that not only affects the quality of daily lives but alsoharms the environment and economy. Traffic patterns arerecurrent in nature, so is congestion. However, little attentionhas been paid to the development of methods that wouldenable early warning of the formation of congestion and itspropagation. This paper proposes a method for automatedearly congestion detection operating over time horizons rangingfrom half an hour to three hours. The method uses a deeplearning technique, Convolutional Neural Networks (CNN), andadapts it to the specific context of urban roads. Empiricalresults are reported from a busy traffic corridor in the city ofBath. Comprehensive evaluation metrics, including DetectionRate, False Positive Rate and Mean Time to Detection, areused to evaluate the performance of the proposed methodcompared to more conventional machine learning methodsincluding Feed-forward Neural Network and Random Forest.The results indicate that recurrent congestion can indeed bepredicted before it occurs and demonstrates that CNN basedmethod offers superior detection accuracy compared to theconventional machine learning methods in this context