Prediction of traveller information and route choice based on real-time estimated traffic stateAccurate depiction of existing traffic states is essential to devise effective real-time traffic management strategies using Intelligent Transportation Systems (ITS). 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 estimate of the traffic states which has less uncertainty than the prediction or measurement alone. On the other hand, research studies which highlight estimation of real-time traffic state are focused only on traffic state estimation and have not utilized the estimated traffic state for DTA applications. In this paper we propose a framework which utilizes real-time traffic state estimate to optimize network performance during an incident through traveller information system. The estimate of real-time traffic states is obtained by combining the prediction of traffic density using Cell Transmission
Model (CTM) and the measurements from the traffic sensors in Extended KalmanFilter (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.
31 32 33 Word count: 7434 words text + 0 table x 250 words (each) = 7,434 words ABSTRACT 1 The accurate depiction of the existing traffic state on a road network is essential in reducing 2 congestion and delays at signalized intersections. The existing literature in the optimization of 3 signal timings either utilizes prediction of traffic state from traffic flow models or limited real-time 4 measurements available from sensors. Prediction of traffic state based on historic data cannot 5 represent the dynamics of change in traffic demand or network capacity. Similarly, data obtained 6 from limited point sensors in a network provides estimates which contain errors. A reliable 7 estimate of existing traffic state is, therefore, necessary to obtain signal timings which are based on 8 the existing condition of traffic on the network. 9 This research proposes a framework which utilizes estimates of traffic flows and travel times based 10 on real-time estimated traffic state for obtaining optimal signal timings. The prediction of traffic 11 state from the Cell Transmission Model (CTM) and measurements from traffic sensors are 12 combined in the recursive algorithm of Extended Kalman Filter (EKF) to obtain a reliable estimate 13 of existing traffic state. The estimate of traffic state obtained from the CTM-EKF model is utilized 14 in the optimization of signal timings using Genetic Algorithm (GA) in the proposed 15 CTM-EKF-GA framework. 16The proposed framework is applied to a synthetic signalized intersection and the results are 17 compared with a model-based optimal solution and simulated reality. The optimal delay estimated 18 by CTM-EKF-GA framework is only 0.6% higher than the perfect solution, whereas the delay 19 estimated by CTM-GA model is 12.9% higher than the perfect solution. 20 21 22 23
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