Russian researchers at South Ural State University (SUSU) have developed and patented an advanced artificial intelligence (AI) system designed to detect traffic anomalies using neural network technology. The program, as reported by TV BRICS, processes real-time CCTV footage, accurately identifying vehicles and tracking their speed and trajectory with precision up to 30 centimeters.
This enables authorities to generate real-time visual maps of traffic congestion and disruptions, enhancing urban traffic management. Olga Ivanova, an associate professor in SUSU’s Department of System Programming, highlighted that the system’s key capability lies in detecting even minor deviations in traffic flow, such as slight reductions in lane width.
The AI is programmed to identify obstacles, including accidents and roadwork, providing a timely alert system for potential disruptions. The visualization tool updates every two seconds, using a color-coded scheme where increased congestion is represented by progressively redder shades.
The system’s future development aims to not only detect anomalies but also classify them and predict their impact on traffic conditions within a 10-to-20-minute window. This predictive capability would allow transport authorities to implement early interventions, mitigating potential traffic jams and improving overall road efficiency. According to Ivanova, a major advantage of the technology is its seamless integration into existing city infrastructure.
New neural network technology tracks urban traffic patterns
Unlike conventional traffic monitoring systems that often require costly GPS sensors installed on individual vehicles, this AI-driven approach leverages existing surveillance networks, making it a cost-effective and scalable solution for urban centers. The AI’s precision in recognizing traffic conditions and its ability to deliver real-time insights make it a valuable tool for city planners and emergency response teams.
By enabling quicker reactions to developing road conditions, the system could significantly enhance public safety and reduce traffic congestion in busy metropolitan areas. With ongoing advancements, the research team at SUSU envisions further refinements that would enhance the system’s predictive accuracy and adaptability to varying urban traffic conditions.
The project underscores Russia’s commitment to integrating AI solutions into public infrastructure, positioning the technology as a key asset for smart city initiatives. As the system undergoes further testing and potential deployment in Russian cities, its success could pave the way for adoption in other regions looking to modernize their traffic management capabilities. – By Eurasian Newswire News Desk.
