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Design of reinforcement learning for perimeter control using network transmission model based macroscopic traffic simulation

PloS one, 2020-07, Vol.15 (7), p.e0236655-e0236655 [Peer Reviewed Journal]

2020 Yoon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2020 Yoon et al 2020 Yoon et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0236655 ;PMID: 32730334

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Title:
Design of reinforcement learning for perimeter control using network transmission model based macroscopic traffic simulation
Author: Yoon, Jinwon ; Kim, Sunghoon ; Byon, Young-Ji ; Yeo, Hwasoo
Tang, Jinjun
Subjects: Central business districts ; Computer and Information Sciences ; Computer simulation ; Distance learning ; Earth Sciences ; Engineering ; Engineering and Technology ; Environmental engineering ; Learning ; Literature reviews ; Methods ; Optimization ; Physical Sciences ; Reinforcement ; Research and Analysis Methods ; Roads ; Shape effects ; Simulation ; Social Sciences ; Traffic ; Traffic congestion ; Traffic control ; Traffic engineering ; Traffic flow ; Traffic models ; Traffic signals ; Transportation networks ; Travel demand ; Urban areas
Is Part Of: PloS one, 2020-07, Vol.15 (7), p.e0236655-e0236655
Description: Perimeter control is an emerging alternative for traffic signal control, which regulates the traffic flows on the periphery of a road network. Some model-based approaches have been suggested earlier for the optimization of perimeter control based on macroscopic fundamental diagrams (MFDs). However, there are several limitations when considering their application to a large-scale urban area because the model-based approaches may not be scalable to multiple regions and inappropriate for handling various effects caused by the shape change of MFDs. Therefore, we propose a model-free and data-driven approach that combines reinforcement learning (RL) with the macroscopic traffic simulation based on the recently developed network transmission model. First, we design four perimeter control models with different macroscopic traffic variables and parametrizations. Then, we validate the proposed models by evaluating their performances with the test demand scenarios at different levels. The validation results show that the model containing travel demand information adapts to a new demand scenario better than the model containing only density-related factors.
Publisher: San Francisco: Public Library of Science
Language: English
Identifier: ISSN: 1932-6203
EISSN: 1932-6203
DOI: 10.1371/journal.pone.0236655
PMID: 32730334
Source: PubMed Central (Open access)
DOAJ Directory of Open Access Journals
Geneva Foundation Free Medical Journals at publisher websites
AUTh Library subscriptions: ProQuest Central
PLoS

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Design of reinforcement learning for perimeter control using network transmission model based macroscopic traffic simulation

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