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Joint route guidance and demand management using generalized MFDs⁎This work has been supported by the European Union's Horizon 2020 research and innovation programme under grant agreement No 739551 (KIOS CoE), 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). Issue 2 (2020)
Record Type:
Journal Article
Title:
Joint route guidance and demand management using generalized MFDs⁎This work has been supported by the European Union's Horizon 2020 research and innovation programme under grant agreement No 739551 (KIOS CoE), 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). Issue 2 (2020)
Main Title:
Joint route guidance and demand management using generalized MFDs⁎This work has been supported by the European Union's Horizon 2020 research and innovation programme under grant agreement No 739551 (KIOS CoE), 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).
Abstract: In this work we propose a Model Predictive Control (MPC) framework that combines multi-region route guidance with demand management at a macroscopic level. While route guidance is employed to control all vehicular routes, demand management is introduced to control the flows' departure times. In effect a portion of the demand may be instructed to wait at their origin before commercing their journey (i.e., delayed departure) and thus ensure that, when vehicles do enter the network, they will travel at free-flow conditions. We show that the resulting problem is a nonlinear optimization problem that is solved by a novel convex relaxation with tight lower bounds on the optimal solution. Extensive simulations are conducted to evaluate the performance of the proposed MPC convex optimization problem indicating the substantial performance improvements in the network utilization.