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AuthorJabbar, Rateb
AuthorShinoy, Mohammed
AuthorKharbeche, Mohamed
AuthorAl-Khalifa, Khalifa
AuthorKrichen, Moez
AuthorBarkaoui, Kamel
Available date2023-10-16T10:48:50Z
Publication Date2019-12-01
Publication Name2019 International Conference on Internet of Things, Embedded Systems and Communications, IINTEC 2019 - Proceedings
Identifierhttp://dx.doi.org/10.1109/IINTEC48298.2019.9112118
CitationJabbar, R., Shinoy, M., Kharbeche, M., Al-Khalifa, K., Krichen, M., & Barkaoui, K. (2019, December). Urban traffic monitoring and modeling system: An iot solution for enhancing road safety. In 2019 international conference on internet of things, embedded systems and communications (iintec) (pp. 13-18). IEEE.‏
ISBN9781728151847
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85087514331&origin=inward
URIhttp://hdl.handle.net/10576/48545
AbstractQatar 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.
SponsorThis publication was funded by the NPRP award [NPRP8-910-2-387] from Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectAndroid
Deep Learning
Driver Behavior Analysis
Drowsiness Detection
Internet of Things
TitleUrban Traffic Monitoring and Modeling System: An IoT Solution for Enhancing Road Safety
TypeConference Paper
Pagination13-18
dc.accessType Open Access


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