TY - GEN
T1 - Urban Traffic Monitoring and Modeling System
T2 - 2019 International Conference on Internet of Things, Embedded Systems and Communications, IINTEC 2019
AU - Jabbar, Rateb
AU - Shinoy, Mohammed
AU - Kharbeche, Mohamed
AU - Al-Khalifa, Khalifa
AU - Krichen, Moez
AU - Barkaoui, Kamel
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - 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.
AB - 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.
KW - Android
KW - Deep Learning
KW - Driver Behavior Analysis
KW - Drowsiness Detection
KW - Internet of Things
UR - http://www.scopus.com/inward/record.url?scp=85087514331&partnerID=8YFLogxK
U2 - 10.1109/IINTEC48298.2019.9112118
DO - 10.1109/IINTEC48298.2019.9112118
M3 - Conference contribution
AN - SCOPUS:85087514331
T3 - 2019 International Conference on Internet of Things, Embedded Systems and Communications, IINTEC 2019 - Proceedings
SP - 13
EP - 18
BT - 2019 International Conference on Internet of Things, Embedded Systems and Communications, IINTEC 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 December 2019 through 22 December 2019
ER -