Real-Time Predictive Control Strategy Optimization
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...
Main Authors: | , , , , , , |
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Format: | Article |
Language: | English |
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SAGE Publications
2021
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Online Access: | https://hdl.handle.net/1721.1/132692 |
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author | Gupta, Samarth Seshadri, Ravi Atasoy, Bilge Prakash, A Arun Pereira, Francisco Tan, Gary Ben-Akiva, Moshe |
author2 | Massachusetts Institute of Technology. Center for Computational Science and Engineering |
author_facet | Massachusetts Institute of Technology. Center for Computational Science and Engineering Gupta, Samarth Seshadri, Ravi Atasoy, Bilge Prakash, A Arun Pereira, Francisco Tan, Gary Ben-Akiva, Moshe |
author_sort | Gupta, Samarth |
collection | MIT |
description | 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. |
first_indexed | 2024-09-23T10:00:39Z |
format | Article |
id | mit-1721.1/132692 |
institution | Massachusetts Institute of Technology |
language | English |
last_indexed | 2024-09-23T10:00:39Z |
publishDate | 2021 |
publisher | SAGE Publications |
record_format | dspace |
spelling | mit-1721.1/1326922024-05-21T17:54:56Z Real-Time Predictive Control Strategy Optimization Gupta, Samarth Seshadri, Ravi Atasoy, Bilge Prakash, A Arun Pereira, Francisco Tan, Gary Ben-Akiva, Moshe Massachusetts Institute of Technology. Center for Computational Science and Engineering Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Singapore-MIT Alliance in Research and Technology (SMART) 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. 2021-10-04T13:42:10Z 2021-10-04T13:42:10Z 2020-02-24 2021-10-01T17:04:44Z Article http://purl.org/eprint/type/JournalArticle 0361-1981 2169-4052 https://hdl.handle.net/1721.1/132692 Gupta S, Seshadri R, Atasoy B, et al. Real-Time Predictive Control Strategy Optimization. Transportation Research Record. 2020;2674(3):1-11 en 10.1177/0361198120907903 Transportation Research Record Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf SAGE Publications arXiv |
spellingShingle | Gupta, Samarth Seshadri, Ravi Atasoy, Bilge Prakash, A Arun Pereira, Francisco Tan, Gary Ben-Akiva, Moshe Real-Time Predictive Control Strategy Optimization |
title | Real-Time Predictive Control Strategy Optimization |
title_full | Real-Time Predictive Control Strategy Optimization |
title_fullStr | Real-Time Predictive Control Strategy Optimization |
title_full_unstemmed | Real-Time Predictive Control Strategy Optimization |
title_short | Real-Time Predictive Control Strategy Optimization |
title_sort | real time predictive control strategy optimization |
url | https://hdl.handle.net/1721.1/132692 |
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