{"id":648191,"date":"2025-01-07T06:15:33","date_gmt":"2025-01-07T03:15:33","guid":{"rendered":"https:\/\/en.buradabiliyorum.com\/researchers-propose-ai-driven-solution-for-smarter-traffic-management\/"},"modified":"2025-01-07T06:15:33","modified_gmt":"2025-01-07T03:15:33","slug":"researchers-propose-ai-driven-solution-for-smarter-traffic-management","status":"publish","type":"post","link":"https:\/\/buradabiliyorum.com\/en\/researchers-propose-ai-driven-solution-for-smarter-traffic-management\/","title":{"rendered":"#Researchers propose AI-driven solution for smarter traffic management"},"content":{"rendered":"
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A new method for managing urban traffic at multi-intersection networks is discussed<\/a> in the International Journal of Information and Communication Technology<\/a><\/i>. The research promises improvements in efficiency and adaptability, and by combining technologies could address the long-standing challenges of congestion and unpredictable traffic patterns in dense urban areas.<\/p>\n

Renyong Zhang, Shibiao He, and Peng Lu of the Chongqing Institute of Engineering in Chongqing, China, suggest the use of vehicle-to-everything (V2X) technology could allow vehicles and infrastructure to exchange real-time data about road conditions and traffic. This continuous sharing of data would improve the way in which traffic management systems control traffic lights and speed and lane restrictions to smooth the flow of vehicles safely.<\/p>\n

The system suggested by the team uses an improved long short-term memory (LSTM) model, a type of artificial intelligence designed for recognizing patterns and making predictions. By using a “sliding time window” update mechanism, the model can learn from real-time data while maintaining historical context. By balancing the two, faster adjustments to traffic flow can be made while reducing the overall computational load on the system and cutting prediction times in half.<\/p>\n

The team has carried out simulations and demonstrated that such an app<\/a>roach might reduce average vehicle delays by just under a third and increase road “throughput” by almost 15%. The result would be shorter travel<\/a> times and smoother traffic flow. This should also improve fuel consumption and reduce overall vehicle emissions.<\/p>\n

Conventional traffic management systems use historical data or limited real-time inputs, and so cannot respond to actual road conditions at a given time without manual input. Such systems are useful in less complex traffic scenarios, but struggle to handle rapid and unpredictable changes in traffic, particularly in larger, interconnected networks. The newly proposed system addresses these limitations by offering more responsive and precise adjustments.<\/p>\n

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More information:<\/strong>
\n\t\t\t\t\t\t\t\t\t\t\t\tRenyong Zhang et al, Multi-intersection traffic flow prediction control based on vehicle-road collaboration V2X and improved LSTM, International Journal of Information and Communication Technology<\/i> (2024). DOI: 10.1504\/IJICT.2024.143411<\/a><\/p>\n<\/div>\n

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\n Citation<\/strong>:
\n Researchers propose AI-driven solution for smarter traffic management (2025, January 6)
\n retrieved 6 January 2025
\n from https:\/\/techxplore.com\/news<\/a>\/2025-01-ai-driven-solution-smarter-traffic.html\n <\/p>\n

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