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Improving traffic management efficiency through reinforcement learning-based traffic signal control and citywide transit simulation

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Title: Improving traffic management efficiency through reinforcement learning-based traffic signal control and citywide transit simulation
Authors: Tran, Viet Toan
Committee Members: Sartipi, Mina; Liang, Yu; Wu, Dalei; College of Engineering and Computer Science
Summary: Traffic congestion reduces productivity and harms the environment. Enhancing traffic signal control and public transportation are effective solutions. However, prior research has limitations stemming from the absence of real-time reliable data. Recent computer vision systems have made collecting traffic data easier. This thesis explores leveraging these data sources to enhance existing traffic signal controls (TSCs) and citywide transit simulations. For TSC, a comprehensive framework that facilitates rapid prototyping of reinforcement learning (RL) and an automatic feature engineering method are proposed. Additionally, RL techniques are implemented to a digital twin of Chattanooga smart corridor. Regarding transit simulations, a toolkit for calibrating large-scale simulations and an efficient solution for simulating changes in transit system settings are developed. Finally, we delve into a fundamental question of optimization for training neural networks and demonstrate that a novel approach using Neuroevolution outperforms Gradient Descent methods.
URL: https://scholar.utc.edu/theses/823
Database: OpenDissertations
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DbLabel: OpenDissertations
An: ddu.oai.scholar.utc.edu.theses.2003
AccessLevel: 6
PubType: Dissertation/ Thesis
PubTypeId: dissertation
PreciseRelevancyScore: 0
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  Data: Improving traffic management efficiency through reinforcement learning-based traffic signal control and citywide transit simulation
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  Label: Summary
  Group: Ab
  Data: Traffic congestion reduces productivity and harms the environment. Enhancing traffic signal control and public transportation are effective solutions. However, prior research has limitations stemming from the absence of real-time reliable data. Recent computer vision systems have made collecting traffic data easier. This thesis explores leveraging these data sources to enhance existing traffic signal controls (TSCs) and citywide transit simulations. For TSC, a comprehensive framework that facilitates rapid prototyping of reinforcement learning (RL) and an automatic feature engineering method are proposed. Additionally, RL techniques are implemented to a digital twin of Chattanooga smart corridor. Regarding transit simulations, a toolkit for calibrating large-scale simulations and an efficient solution for simulating changes in transit system settings are developed. Finally, we delve into a fundamental question of optimization for training neural networks and demonstrate that a novel approach using Neuroevolution outperforms Gradient Descent methods.
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RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Electronic traffic controls
        Type: general
      – SubjectFull: Reinforcement learning
        Type: general
      – SubjectFull: Urban transportation--Computer simulation
        Type: general
    Titles:
      – TitleFull: Improving traffic management efficiency through reinforcement learning-based traffic signal control and citywide transit simulation
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Tran, Viet Toan
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 08
              Type: published
              Y: 2023
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