Ahmed, A, Naqvi, SAA, Watling, D orcid.org/0000-0002-6193-9121 et al. (1 more author) (2019) Real-Time Dynamic Traffic Control Based on Traffic-State Estimation. Transportation Research Record, 2673 (5). pp. 584-595. ISSN 0361-1981
Abstract
The accurate depiction of the existing traffic state on a road network is essential in reducing congestion and delays at signalized intersections. The existing literature in the optimization of signal timings either utilizes prediction of traffic state from traffic flow models or limited real-time measurements available from sensors. Prediction of traffic state based on historic data cannot represent the dynamics of change in traffic demand or network capacity. Similarly, data obtained from limited point sensors in a network provides estimates which contain errors. A reliable estimate of existing traffic state is, therefore, necessary to obtain signal timings which are based on the existing condition of traffic on the network. This research proposes a framework which utilizes estimates of traffic flows and travel times based on real-time estimated traffic state for obtaining optimal signal timings. The prediction of traffic state from the Cell Transmission Model (CTM) and measurements from traffic sensors are combined in the recursive algorithm of Extended Kalman Filter (EKF) to obtain a reliable estimate of existing traffic state. The estimate of traffic state obtained from the CTM-EKF model is utilized in the optimization of signal timings using Genetic Algorithm (GA) in the proposed CTM-EKF-GA framework. The proposed framework is applied to a synthetic signalized intersection and the results are compared with a model-based optimal solution and simulated reality. The optimal delay estimated by CTM-EKF-GA framework is only 0.6% higher than the perfect solution, whereas the delay estimated by CTM-GA model is 12.9% higher than the perfect solution.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019, National Academy of Sciences: Transportation Research Board. This is an author produced version of a paper published in Transportation Research Record. Reprinted by permission of SAGE Publications. |
Keywords: | Cell Transmission Model; Extended Kalman Filter; Real-time Traffic State Estimation; Adaptive Signal Control; Signal Optimization; Genetic Algorithm |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Spatial Modelling and Dynamics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 13 Feb 2019 15:35 |
Last Modified: | 15 Jul 2019 03:56 |
Status: | Published |
Publisher: | SAGE Publications |
Identification Number: | 10.1177/0361198119838842 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:142488 |