[Edaily Reporter Kwon Oh Seok ] Next-generation artificial intelligence (AI) simulation technology has been unveiled to help identify the fastest and best detour routes and predict future traffic volumes when unexpected situations—such as accidents or construction—occur on the road. By calculating large-scale metropolitan road networks in near real time while also enabling precise analysis down to the individual vehicle level, the technology is expected to significantly improve decision-making efficiency for local governments and traffic control centers.

LaonPeople Inc.(300120) Raon Road, a subsidiary specializing in AI traffic management solutions, announced on the 10th that it participated in the “2026 Gangneung ITS World Congress” and unveiled for the first time its “AI Simulation-Based Next-Generation Urban Traffic Management Solution,” developed jointly with KAIST and others.
The core of this technology lies in recreating an actual urban traffic network in a virtual space (digital twin) and then simulating changes in traffic flow in advance based on various variables, such as accidents, disasters, road construction, or the introduction of trams and autonomous vehicles. Existing simulators had limitations where computational speed dropped sharply or accuracy declined when analyzing large areas.
The research team introduced distributed computing technology to distribute the computational load of large-scale road networks across multiple computers, thereby increasing the analysis speed by 4.9 times compared to previous methods. This enabled both rapid computations over a wide area and precise analysis of key locations, such as intersections where accidents occur.
Building on its track record of establishing 550 smart intersections nationwide and its AI analysis capabilities, Raon Road visualized this engine as a “traffic decision-support solution” that can be immediately utilized by actual traffic center operators.
Industry and academic circles anticipate that this technology will not only aid local governments in formulating traffic policies but also serve as a real-time emergency control tower to minimize traffic bottlenecks during disasters and unforeseen situations. Currently being carried out as a national project by the Ministry of Science and ICT and the Institute for Information & Communications Technology Planning & Evaluation (IITP), this initiative also involves the Korea Transportation Institute and Korea University. Following pilot tests in the Daejeon and Sejong regions, the solution is scheduled for commercialization in 2027.
Kang Byung-ki, Vice President of Raon Road, emphasized, “It is deeply meaningful to showcase this solution, developed in collaboration with leading domestic researchers including those at KAIST, on the global stage,” adding, “We will establish an AI-based traffic management system that combines traffic data and simulation to respond swiftly to any unexpected situations, such as disasters or construction projects.”