An Accident Detection and Classification System Using Internet of Things and Machine Learning towards Smart City

Mohammed Balfaqih, Soltan Abed Alharbi, Moutaz Alzain, Faisal Alqurashi and Saif Almilad
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Mohammed Balfaqih: Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia
Soltan Abed Alharbi: Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia
Moutaz Alzain: Department of Electrical and Electronic Engineering, College of Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia
Faisal Alqurashi: Department of Electrical and Electronic Engineering, College of Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia
Saif Almilad: Department of Electrical and Electronic Engineering, College of Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia

Sustainability, 2021, vol. 14, issue 1, 1-13

Abstract: Daily traffic accidents increase annually, causing a significant number of death and disability cases. Most of fatalities occur because of the late response to these emergency cases. The time after the traumatic injury is called the golden hour, where providing essential medical and surgical aid at that time increases the probability of saving human lives by one-third an average. Thus, the focus of this paper was to develop a system based on IoT for accident detection and classification. The system detects and classifies vehicle accidents based on severity level and reports the essential information about the accident to emergency services providers. The system consists of a microcontroller, GPS, and a group of sensors to determine different physical parameters related to vehicle motion. In addition, different types of machine learning classifiers were examined with the developed system to determine the most accurate classifier for the system. The classifiers are the Gaussian Mixture Model (GMM), Naive-Bayes Tree (NB), Decision Tree (DT), and Classification and Regression Trees (CART). The implementation of the system showed that GMM and CART models were better in terms of precision and recall. It was also shown that the severity of accidents depends mainly on the g-force value and fire occurrence.

Keywords: accident detection and classification; internet of things (IoT); sensor fusion; machine learning classifiers (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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