Ahmed, A, Ngoduy, D and Watling, D (2016) Prediction of traveller information and route choice based on real-time estimated traffic state. Transportmetrica B: Transport Dynamics, 4 (1). pp. 23-47. ISSN 2168-0566
Abstract
Accurate 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 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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015, Taylor & Francis. This is an Accepted Manuscript of an article published by Taylor & Francis in Transportmetrica B: Transport Dynamics on 10 Jun 2015 available online: http://www.tandfonline.com/10.1080/21680566.2015.1052110. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Cell transmission model; extended Kalman filter; traffic estimation; dynamic traffic assignment; route choice; traveller information; incident management |
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: | 19 May 2015 12:45 |
Last Modified: | 11 Jun 2016 21:37 |
Published Version: | http://dx.doi.org/10.1080/21680566.2015.1052110 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/21680566.2015.1052110 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:86183 |