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An Adaptive Traffic-Calming Measure and Effectiveness Evaluation in a Large Urban Complex of Shanghai, China

Author

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  • Jindong Wang

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China
    Frontiers Science Center for Intelligent Autonomous Systems, Tongji University, Shanghai 201210, China
    Shanghai Jinqiao (Group) Co., Ltd., Shanghai 201206, China)

  • Jianguo Ying

    (Shanghai Jinqiao (Group) Co., Ltd., Shanghai 201206, China)

  • Shengchuan Jiang

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China
    Frontiers Science Center for Intelligent Autonomous Systems, Tongji University, Shanghai 201210, China)

Abstract

The rapid development of the motor vehicle brings convenience to our life; however, it also increases the burden on traffic networks and the environment, especially when road space is limited. Traffic calming has proved to be an effective solution for the improvement of traffic safety and travel quality. However, most traffic-calming measures are investigated and carried out without any adaptive ability. Such measures cannot adapt to changing traffic requirements. There is a mismatch between static measures and dynamic traffic. In this study, we propose an adaptive traffic-calming measure using deep reinforcement learning. Traffic volume is controlled at intersections according to the state of dynamic traffic. Then, we take a large urban complex (the Jinding nine-rectangle-grid area) in Shanghai, China, as an example. Further, based on applied static traffic-calming measures, we consider the characteristics of the nine plots, along with traffic demand, to design traffic-calming measures. Finally, the effectiveness of the measures is evaluated in SUMO (Simulation of Urban Mobility). The experimental results show that the proposed measure can increase driving speed under the speed limit and reduce traffic volume in a peak period. The results indicate that the proposed measure is an effective and novel solution for traffic calming in the large urban complex.

Suggested Citation

  • Jindong Wang & Jianguo Ying & Shengchuan Jiang, 2022. "An Adaptive Traffic-Calming Measure and Effectiveness Evaluation in a Large Urban Complex of Shanghai, China," Sustainability, MDPI, vol. 14(20), pages 1-10, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13023-:d:939672
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    References listed on IDEAS

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    1. Heriberto Pérez-Acebo & Robert Ziolkowski & Hernán Gonzalo-Orden, 2021. "Evaluation of the Radar Speed Cameras and Panels Indicating the Vehicles’ Speed as Traffic Calming Measures (TCM) in Short Length Urban Areas Located along Rural Roads," Energies, MDPI, vol. 14(23), pages 1-17, December.
    2. Nadafianshahamabadi, Razieh & Tayarani, Mohammad & Rowangould, Gregory, 2021. "A closer look at urban development under the emergence of autonomous vehicles: Traffic, land use and air quality impacts," Journal of Transport Geography, Elsevier, vol. 94(C).
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    4. Monica Menendez & Lukas Ambühl, 2022. "Implementing Design and Operational Measures for Sustainable Mobility: Lessons from Zurich," Sustainability, MDPI, vol. 14(2), pages 1-21, January.
    5. Jan Paszkowski & Marcus Herrmann & Matthias Richter & Andrzej Szarata, 2021. "Modelling the Effects of Traffic-Calming Introduction to Volume–Delay Functions and Traffic Assignment," Energies, MDPI, vol. 14(13), pages 1-18, June.
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    Cited by:

    1. Maria Luisa Tumminello & Elżbieta Macioszek & Anna Granà & Tullio Giuffrè, 2023. "Evaluating Traffic-Calming-Based Urban Road Design Solutions Featuring Cooperative Driving Technologies in Energy Efficiency Transition for Smart Cities," Energies, MDPI, vol. 16(21), pages 1-28, October.

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