SafeTrax: Smart Collision Prediction and Alert System Using IoT for Sustainable Traffic Safety

With the rise in traffic accidents worldwide, it has become very important to have a more effective way to ensure road safety. To address this issue, we have made a system called SafeTrax. SafeTrax uses deep learning to predict traffic accidents and car crashes. Additionally, it has been implemented...

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Bibliographic Details
Published in:IEEE access 2025, Vol.13, p.6667-6684
Main Authors: Amin, Md. Fahim Bin, Khan, Israt Jahan, Akter, Faria, Ahmed, Adib, Islam, Md. Motaharul
Format: Article
Language:English
Subjects:
Accidents
Accuracy
AWS IoT Greengrass
Cloud computing
collision
Crashes
Data models
Deep learning
Hidden Markov models
Internet of Things
IoT
Learning programs
pedestrian
Predictive models
Real time
Real-time systems
Road safety
Roads
SafeTrax
Safety
Sensors
Traffic
Traffic accidents
Traffic accidents & safety
Traffic safety
Vehicles
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Summary:With the rise in traffic accidents worldwide, it has become very important to have a more effective way to ensure road safety. To address this issue, we have made a system called SafeTrax. SafeTrax uses deep learning to predict traffic accidents and car crashes. Additionally, it has been implemented with the Internet of Things (IoT) to gather real-time data from sensors, and AWS Greengrass to process that data quickly and give drivers timely warnings. Unlike old safety systems, SafeTrax can predict if an accident might happen. It warns drivers beforehand to make roads safer for cars, people, and other animals. SafeTrax uses video frames from camera sensors in cars to look for dangers. It then uses a special deep-learning program to figure out if a crash might happen. One important part of SafeTrax is the integration of AWS IoT Greengrass. It helps the system work faster by using cloud services on devices like the Raspberry Pi. This means the system can quickly check the data and send warnings in time. SafeTrax can ensure safer roads, and it can help save lives.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3524676