Prediction of traveller information and route choice based on real-time estimated traffic state

Accurate depiction of existing traffic states is essential to devise effective real-time traffic management strategies using intelligent transportation systems. Existing applications of dynamic traffic assignment (DTA) methods are mainly based on either the prediction from macroscopic traffic flow models or measurements from the sensors and do not take advantage of the traffic state estimation techniques, which produce an estimate of the traffic states which has less uncertainty than the prediction or measurement alone. On the other hand, research studies which highlight the estimation of real-time traffic state are focused only on traffic state estimation and have not utilised the estimated traffic state for DTA applications. In this paper we propose a framework which utilises real-time traffic state estimate to optimise network performance during an incident through the traveller information system. The estimate of real-time traffic states is obtained by combining the prediction of traffic density using the cell transmission model (CTM) and the measurements from the traffic sensors in extended Kalman filter (EKF) recursive algorithm. The estimated traffic state is used for predicting travel times on alternative routes in a small traffic network, and the predicted travel times are communicated to the commuters by a variable message sign (VMS). In numerical experiments on a two-route network, the proposed estimation and information method is seen to significantly improve travel times and network performance during a traffic incident.