Revision as of 01:01, 2 February 2021

Abstract

Traffic congestion occurs as demand surpasses the available capacity of a road network, resulting to lower speeds and longer journey times; with route guidance constituting the primary control strategy to alleviate the problem. However, the effectiveness of route guidance is limited in high-demand conditions. In this work, we proposed a Model Predictive Control (MPC) framework that combines multi-regional route guidance with a novel demand management method. Route guidance is used to minimize the network's density imbalance while demand management is utilized to reduce the conditions that cause congestion. This can be achieved by manipulating vehicle routes (i.e., using route guidance) and/or by instructing a portion of the vehicles to wait at their origin before commencing their journey (demand management). Simulations are conducted to evaluate the performance of the proposed MPC optimization indicating the substantial improvements that can be achieved in traffic flow performance. Cyprus Research Promotion Foundation the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development and through the Research Promotion Foundation (Project: CULTURE/BR-NE/0517/14) © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, in-cluding reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to serv-ers or lists, or reuse of any copyrighted component of this work in other works. Menelaou, C., Timotheou, S., Kolios, P. and Panayiotou, C.G., 2019, June. "Joint route guidance and demand management for multi-region traffic networks," 2019 18th European Control Conference (ECC), Naples, pp. 2183-2188, IEEE. doi:10.23919/ECC.2019.8795819

Document type: Conference object

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The different versions of the original document can be found in:

• https://zenodo.org/record/3522450 under the license https://creativecommons.org/licenses/by

• https://zenodo.org/record/3522450/files/mpcECC_ST3.pdf under the license http://creativecommons.org/licenses/by/4.0/legalcode

• http://xplorestaging.ieee.org/ielx7/8778424/8795605/08795819.pdf?arnumber=8795819,

http://dx.doi.org/10.23919/ecc.2019.8795819 under the license cc-by

• https://dblp.uni-trier.de/db/conf/eucc/eucc2019.html#MenelaouTKP19,

https://academic.microsoft.com/#/detail/2967109672