An Automated Smartphone-Capable Road Traffic Accident Notification System

• Published in: Journal of advanced computational intelligence and intelligent informatics Vol. 28; no. 4; pp. 939 - 952
• Main Authors: Langa, Relebogile Makhulu, Moeti, Michael Nthabiseng, Kgoete, Senota Frans
• Format: Journal Article
• Language: English
• Published: Tokyo Fuji Technology Press Co. Ltd 01.07.2024
• Subjects: Fatalities; In vehicle; Machine learning; Microelectromechanical systems; Mobile ad hoc networks; Prototypes; Roads & highways; Smart sensors; Smartphones; Task complexity; Traffic accidents; Traffic accidents & safety; Traffic safety
• ISSN: 1343-0130; 1883-8014
• DOI: 10.20965/jaciii.2024.p0939

The widespread use of automobiles has revolutionized transportation and attracted a large population owing to their convenience and effectiveness. However, this widespread adoption has resulted in a significant increase in road traffic accidents. The alarming road fatalities suggest that medical responders are overwhelmed by the need to save lives in a timely manner. This is due to a lack of affordable autonomous detection and notification mechanisms. Prior work in this domain includes the use of vehicular ad hoc networks, Arduinos, and Raspberry Pis; machine-learning approaches for predictions; and smart devices using integrated sensors. These methods are either expensive to acquire, human-reliant, or require vehicular modifications. Therefore, the aim of this study is to suggest a cheap prototype that can work with smartphones. The prototype should have embedded micro-electromechanical system (MEMS) sensors that measure g-force to find car accidents and global system for mobile communications-long term evolution (GSM-LTE) to call the closest medical responders, which would be found using GPS. A prototype was developed using the .NET Multi-Platform App UI (MAUI) framework. This study applied the design science research methodology (DSRM) to produce a socially acceptable, low-cost artifact similar to existing in-vehicle systems to save lives on the road during a road traffic accident. The FEDS evaluation of the results indicated that smartphones can perform such complex tasks with reasonable accuracy compared with expensive in-vehicle systems. The prototype can be adopted by lower- to middle-class individuals as it is a cheaper alternative. This study makes a practical contribution to the society by utilizing artifacts to ensure road safety.