Real-Time Predictive Control Strategy Optimization

Samarth Gupta, Ravi Seshadri, Bilge Atasoy, A. Arun Prakash, Francisco Camara Pereira, Gary Tan, Moshe Ben-Akiva

Research output: Contribution to journalJournal articleResearchpeer-review

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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.
Original languageEnglish
Book seriesTransportation Research Record
Issue number3
Number of pages11
Publication statusPublished - 2020


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