Traffic Accident Management System for Intelligent and Sustainable Vehicle Networking

被引:1
|
作者
Radi, Wafaa [1 ]
El Badawy, Hesham M. [2 ]
Mudassir, Ahmed [3 ]
Kamel, Hesham [4 ]
机构
[1] CIC, Canadian Int Coll, Dept Commun & Elect Engn, Cairo, Egypt
[2] Natl Telecommun Inst, Network Planning Dept, Cairo, Egypt
[3] Comsats Univ Islamabad, Dept Elect & Comp Engn, Lahore, Pakistan
[4] Canadian Int Coll, Dept Elect & Commun, Giza, Egypt
关键词
Traffic Accident Management; Machine Learning; Internet Of Vehicles (IoV); Intelligent Transportation Systems (ITS); Real-Time Predictions; Emergency Responders;
D O I
10.1109/AICCSA59173.2023.10479241
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Intelligent and sustainable vehicle networking (ISVN) is a new paradigm for transportation that makes use of developments in vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication to encourage collaboration between vehicles and the infrastructure in order to enhance traffic flow, safety, and environmental effect. Traffic accidents are a major public safety concern, resulting in significant casualties and economic losses each year, as well as a significant contributor to air pollution and greenhouse gas emissions. In order to increase traffic accident management's efficacy and efficiency and support a more environmentally friendly transportation system, this article suggests an integrated ISVN-ML traffic accident management system. The proposed system leverages ISVN sensors and cameras to collect data about traffic accidents in real time. This data is then transmitted to an ML-based traffic accident management system, which uses it to predict the number of killed and injured people involved in the accident and to immediately prioritize the dispatch of emergency responders. Additionally, the system provides real-time information to police stations and ambulance services to help them respond to accidents more quickly and efficiently. After the police station has completed its investigation of the accident, the details of the accident are sent back to the ML-based traffic accident management system to improve the accuracy of the system's predictions and make the system more efficient over time. Overall, the proposed system is a promising approach to improving traffic safety, reducing the economic costs associated with traffic accidents, and contributing to a more sustainable transportation system.
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页数:7
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