IoT-Based System for Improving Vehicular Safety by Continuous Traffic Violation Monitoring

Yousef-Awwad Daraghmi (), Mamoun Abu Helou, Eman-Yasser Daraghmi and Waheeb Abu-ulbeh
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Yousef-Awwad Daraghmi: Computer Systems Engineering Department, Palestine Technical University-Kadoorie, Tulkarm p305, Palestine
Mamoun Abu Helou: Faculty of Information Technology, Al Istiqlal University, Jericho 4728, Palestine
Eman-Yasser Daraghmi: Applied Computing Department, Palestine Technical University-Kadoorie, Tulkarm p305, Palestine
Waheeb Abu-ulbeh: Faculty of Information Technology, Al Istiqlal University, Jericho 4728, Palestine

Future Internet, 2022, vol. 14, issue 11, 1-17

Abstract: The violation traffic laws by driving at high speeds, the overloading of passengers, and the unfastening of seatbelts are of high risk and can be fatal in the event of any accident. Several systems have been proposed to improve passenger safety, and the systems either use the sensor-based approach or the computer-vision-based approach. However, the accuracy of these systems still needs enhancement because the entire road network is not covered; the approaches utilize complex estimation techniques, and they are significantly influenced by the surrounding environment, such as the weather and physical obstacles. Therefore, this paper proposes a novel IoT-based traffic violation monitoring system that accurately estimates the vehicle speed, counts the number of passengers, and detects the seatbelt status on the entire road network. The system also utilizes edge computing, fog computing, and cloud computing technologies to achieve high accuracy. The system is evaluated using real-life experiments and compared with another system where the edge and cloud layers are used without the fog layer. The results show that adding a fog layer improves the monitoring accuracy as the accuracy of passenger counting rises from 94% to 97%, the accuracy of seatbelt detection rises from 95% to 99%, and the root mean square error of speed estimation is reduced from 2.64 to 1.87.

Keywords: IoT; vehicular safety; edge computing; fog computing; passengers counting; seatbelt detection; speed estimation (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
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