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A Robust Adaptive Traffic Signal Control Algorithm Using Q-Learning under Mixed Traffic Flow

Author

Listed:
  • Zibin Wei

    (College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
    These authors contributed equally to this work.)

  • Tao Peng

    (College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
    These authors contributed equally to this work.)

  • Sijia Wei

    (School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, China)

Abstract

The operational and safety performance of intersections is the key to ensuring the efficient operation of urban traffic. With the development of automated driving technologies, the ability of adaptive traffic signal control has been improved according to data detected by connected and automated vehicles (CAVs). In this paper, an adaptive traffic signal control was proposed to optimize the operational and safety performance of the intersection. The proposed algorithm based on Q-learning considers the data detected by loop detectors and CAVs. Furthermore, a comprehensive analysis was conducted to verify the performance of the proposed algorithm. The results show that the average delay and conflict rate have been significantly optimized compared with fixed timing and traffic actuated control. In addition, the performance of the proposed algorithm is good in the test of the irregular intersection. The algorithm provides a new idea for the intelligent management of isolated intersections under the condition of mixed traffic flow. It provides a research basis for the collaborative control of multiple intersections.

Suggested Citation

  • Zibin Wei & Tao Peng & Sijia Wei, 2022. "A Robust Adaptive Traffic Signal Control Algorithm Using Q-Learning under Mixed Traffic Flow," Sustainability, MDPI, vol. 14(10), pages 1-16, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:5751-:d:812160
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    References listed on IDEAS

    as
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    2. Xinqiang Chen & Jinquan Lu & Jiansen Zhao & Zhijian Qu & Yongsheng Yang & Jiangfeng Xian, 2020. "Traffic Flow Prediction at Varied Time Scales via Ensemble Empirical Mode Decomposition and Artificial Neural Network," Sustainability, MDPI, vol. 12(9), pages 1-17, May.
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