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Artificial Intelligence-Enabled Traffic Monitoring System

Author

Listed:
  • Vishal Mandal

    (Department of Civil and Environmental Engineering, University of Missouri-Columbia, E2509 Lafferre Hall, Columbia, MO 65211, USA
    WSP USA, 211 N Broadway Suite 2800, St. Louis, MO 63102, USA)

  • Abdul Rashid Mussah

    (Department of Civil and Environmental Engineering, University of Missouri-Columbia, E2509 Lafferre Hall, Columbia, MO 65211, USA)

  • Peng Jin

    (Department of Civil and Environmental Engineering, University of Missouri-Columbia, E2509 Lafferre Hall, Columbia, MO 65211, USA)

  • Yaw Adu-Gyamfi

    (Department of Civil and Environmental Engineering, University of Missouri-Columbia, E2509 Lafferre Hall, Columbia, MO 65211, USA)

Abstract

Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which would reduce the impact of incidents and recurring congestion on roadways. This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks and a stand-alone graphical user interface. The authors describe the results of research received in the process of developing models that serve as an integrated framework for an artificial intelligence enabled traffic monitoring system. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. A pixel-level segmentation approach is applied to detect traffic queues and predict severity. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts. At each stage of development, interesting experimental results are presented to demonstrate the effectiveness of the proposed system. Overall, the results demonstrate that the proposed framework performs satisfactorily under varied conditions without being immensely impacted by environmental hazards such as blurry camera views, low illumination, rain, or snow.

Suggested Citation

  • Vishal Mandal & Abdul Rashid Mussah & Peng Jin & Yaw Adu-Gyamfi, 2020. "Artificial Intelligence-Enabled Traffic Monitoring System," Sustainability, MDPI, vol. 12(21), pages 1-21, November.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:21:p:9177-:d:440031
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    References listed on IDEAS

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    1. Xiaolei Ma & Haiyang Yu & Yunpeng Wang & Yinhai Wang, 2015. "Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-17, March.
    2. Baykal-Gürsoy, M. & Xiao, W. & Ozbay, K., 2009. "Modeling traffic flow interrupted by incidents," European Journal of Operational Research, Elsevier, vol. 195(1), pages 127-138, May.
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    Citations

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    Cited by:

    1. Abdul Rashid Mussah & Yaw Adu-Gyamfi, 2022. "Machine Learning Framework for Real-Time Assessment of Traffic Safety Utilizing Connected Vehicle Data," Sustainability, MDPI, vol. 14(22), pages 1-16, November.
    2. Andrzej Paszkiewicz & Bartosz Pawłowicz & Bartosz Trybus & Mateusz Salach, 2021. "Traffic Intersection Lane Control Using Radio Frequency Identification and 5G Communication," Energies, MDPI, vol. 14(23), pages 1-17, December.
    3. Minjung Kim & Max Schrader & Hwan-Sik Yoon & Joshua A. Bittle, 2023. "Optimal Traffic Signal Control Using Priority Metric Based on Real-Time Measured Traffic Information," Sustainability, MDPI, vol. 15(9), pages 1-18, May.

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