Journal article
Real-Time Predictive Control Strategy Optimization
Massachusetts Institute of Technology1
Singapore-MIT Alliance2
Delft University of Technology3
Transport, Department of Technology, Management and Economics, Technical University of Denmark4
Machine Learning, Transport, Department of Technology, Management and Economics, Technical University of Denmark5
Department of Technology, Management and Economics, Technical University of Denmark6
National University of Singapore7
Urban traffic congestion has led to an increasing emphasis on management measures for more efficient utilization of existing infrastructure. In this context, this paper proposes a novel framework that integrates real-time optimization of control strategies (tolls, ramp metering rates, etc.) with the generation of traffic guidance information using predicted network states for dynamic traffic assignment systems.
The efficacy of the framework is demonstrated through a fixed demand dynamic toll optimization problem, which is formulated as a non-linear program to minimize predicted network travel times. A scalable efficient genetic algorithm that exploits parallel computing is applied to solve this problem. Experiments using a closed-loop approach are conducted on a large-scale road network in Singapore to investigate the performance of the proposed methodology.
The results indicate significant improvements in network-wide travel time of up to 9% with real-time computational performance.
Language: | English |
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Publisher: | SAGE Publications |
Year: | 2020 |
Pages: | 1-11 |
ISSN: | 21694052 and 03611981 |
Types: | Journal article |
DOI: | 10.1177/0361198120907903 |
ORCIDs: | Pereira, Francisco Camara |