Artificial Intelligence-Enabled Traffic Monitoring System

Vishal Mandal, Abdul Rashid Mussah, Peng Jin and Yaw Adu-Gyamfi
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Vishal Mandal: Department of Civil and Environmental Engineering, University of Missouri-Columbia, E2509 Lafferre Hall, Columbia, MO 65211, 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

Sustainability, 2020, vol. 12, issue 21, 1-21

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.

Keywords: traffic monitoring; intelligent transportation systems; traffic queues; vehicle counts; artificial intelligence; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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