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Urban Traffic Monitoring and Modeling System: An IoT Solution for Enhancing Road Safety

Mohammed Shinoy
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Mohamed Kharbeche
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Khalifa Al-Khalifa
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Kamel Barkaoui

Abstract

Qatar expects more than a million visitors during the 2022 World Cup, which will pose significant challenges. The high number of people will likely cause a rise in road traffic congestion, vehicle crashes, injuries and deaths. To tackle this problem, Naturalistic Driver Behavior can be utilised which will collect and analyze data to estimate the current Qatar traffic system, including traffic data infrastructure, safety planning, and engineering practices and standards. In this paper, an IoT-based solution to facilitate such a study in Qatar is proposed. Different data points from a driver are collected and recorded in an unobtrusive manner, such as trip data, GPS coordinates, compass heading, minimum, average, and maximum speed and his driving behavior, including driver's drowsiness level. Analysis of these data points will help in prediction of crashes and road infrastructure improvements to reduce such events. It will also be used for drivers' risk assessment and to detect extreme road user behaviors. A framework that will help to visualize and manage this data is also proposed, along with a Deep Learning-based application that detects drowsy driving behavior that netted an 82% accuracy.
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Dates and versions

hal-02504586 , version 1 (10-03-2020)

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Rateb Jabbar, Mohammed Shinoy, Mohamed Kharbeche, Khalifa Al-Khalifa, Moez Krichen, et al.. Urban Traffic Monitoring and Modeling System: An IoT Solution for Enhancing Road Safety. IINTEC 2019, Dec 2019, Hammamet, Tunisia. pp. 13-18, ⟨10.1109/IINTEC48298.2019.9112118⟩. ⟨hal-02504586⟩
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