Real-Time Predictive Control Strategy Optimization
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arXiv
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| Format: | Preprint |
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2019
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| author | Gupta, Samarth Seshadri, Ravi Atasoy, Bilge Prakash, A. Arun Pereira, Francisco Tan, Gary Ben-Akiva, Moshe |
| author_facet | Gupta, Samarth Seshadri, Ravi Atasoy, Bilge Prakash, A. Arun Pereira, Francisco Tan, Gary Ben-Akiva, Moshe |
| contents | Traffic congestion has lead to an increasing emphasis on management measures for a 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 guidance generation 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 is applied to solve this problem that exploits parallel computing. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1901_04571 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Real-Time Predictive Control Strategy Optimization Gupta, Samarth Seshadri, Ravi Atasoy, Bilge Prakash, A. Arun Pereira, Francisco Tan, Gary Ben-Akiva, Moshe Systems and Control Traffic congestion has lead to an increasing emphasis on management measures for a 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 guidance generation 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 is applied to solve this problem that exploits parallel computing. 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. |
| title | Real-Time Predictive Control Strategy Optimization |
| topic | Systems and Control |
| url | https://arxiv.org/abs/1901.04571 |