Real-time predictive control strategy optimization

Journal Article (2020)
Author(s)

Samarth Gupta (Massachusetts Institute of Technology)

Ravi Seshadri (Singapore-MIT Alliance)

B. Atasoy (TU Delft - Transport Engineering and Logistics)

A. Arun Prakash (Massachusetts Institute of Technology)

Francisco Pereira (Technical University of Denmark (DTU))

Gary Tan (National University of Singapore)

Moshe Ben-Akiva (Massachusetts Institute of Technology)

Research Group
Transport Engineering and Logistics
Copyright
© 2020 Samarth Gupta, Ravi Seshadri, B. Atasoy, A. Arun Prakash, Francisco Pereira, Gary Tan, Moshe Ben-Akiva
DOI related publication
https://doi.org/10.1177/0361198120907903
More Info
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Publication Year
2020
Language
English
Copyright
© 2020 Samarth Gupta, Ravi Seshadri, B. Atasoy, A. Arun Prakash, Francisco Pereira, Gary Tan, Moshe Ben-Akiva
Research Group
Transport Engineering and Logistics
Issue number
3
Volume number
2674
Pages (from-to)
1-11
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Abstract

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.

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