Traffic AI predicts accidents
Traffic cameras today are not only used to monitor violations but also become a valuable source of data for AI systems. And thanks to artificial intelligence, unusual vehicle behavior can be identified and analyzed, creating a risk map before an accident occurs.
This is a new direction to help improve traffic safety and support smart urban planning.
Traffic AI 'scrutinizes' every movement on the road
Current traffic AI systems collect data from hundreds of cameras and sensors placed on roads, including sensors that measure speed, acceleration, vehicle distance, and traffic volume in real time.
Using machine learning and deep learning algorithms, AI identifies behaviors that are precursors to accidents , such as sudden lane changes, sudden acceleration, or tailgating. Real-time analysis allows the system to assess risk as soon as anomalous behavior occurs, rather than relying on accident data that has already occurred.
According to Tuoi Tre Online's research, deep learning models, especially convolutional neural networks (CNN), are applied to analyze images from cameras, combining data from sensors to calculate relative speed, minimum distance and reaction time between vehicles.
The system assigns risk scores to each intersection or road section, creating a map of potential “black spots” for the city. Edge computing is used to process data near cameras and sensors, reducing latency, protecting privacy, and ensuring rapid response as soon as dangerous behavior appears .
International research from MIT Senseable City Lab and smart city projects in Singapore and Toronto shows that this method helps identify high-risk areas two to three times better than traditional accident statistics.
The system not only recognizes unusual behavior but also tracks complex traffic flow patterns, from rush hour to bad weather conditions, to better predict risks. The AI also learns from historical data, improving its predictions over time and adapting to changes in traffic flow.
From black spot mapping to optimizing urban safety
For the blackspot map to be effective, the system must process a huge amount of data from cameras and sensors and analyze it in real time. Current AI models use edge computing, which processes data near the camera instead of sending it to a central server, reducing latency and protecting privacy.
The aggregated data not only helps identify risk areas, but also supports traffic authorities in making appropriate decisions regarding traffic signals and infrastructure.
However, the accuracy of AI also depends on environmental conditions , from day or night, rain or shine, to heavy or light traffic, as well as pedestrian and motorbike behavior. Therefore, AI models need to be fine-tuned according to the traffic characteristics of each urban area to reduce false warnings and increase forecasting efficiency.
AI predicts traffic accidents from cameras & sensors
The accuracy of AI depends on the synchronization of sensor and camera data, handling of traffic fluctuations, and the ability to recognize behavior in different lighting and weather conditions. When deployed effectively, AI not only predicts accidents but also forms the basis for systems that optimize traffic signals, coordinate traffic flows, and reduce congestion.
The technology also opens up the prospect of self-driving cars and intelligent transport systems, which can identify risks before accidents occur and improve safety across urban networks.
Overall, AI traffic accident prediction from urban cameras and sensors represents a major step forward in the application of artificial intelligence to traffic management . This technology combines behavioral analysis, real-time data and deep learning models, turning surveillance data into specific risk maps, helping to improve safety, optimize traffic flows and build smarter cities in the future.
Source: https://tuoitre.vn/ai-du-doan-tai-nan-giao-thong-tu-camera-va-sensor-do-thi-20251128174419006.htm
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