Comparison of modelling approaches for short term traffic prediction under normal and abnormal conditions

Abstract

Short-term prediction of traffic flows is an integral component of proactive traffic management systems. Prediction during abnormal conditions, such as incidents, is important for such systems. In this paper, three different models with increasing information in explanatory variables are presented. Time Delay and Recurrent Neural Networks and the k-Nearest Neighbour (kNN) algorithms are chosen as the machine learning tools in these models. The models are tested during both normal and incident conditions. The results indicate that historical patterns provide less predictive information during incidents.