NEC technology predicts sudden traffic congestion in real time using optical fiber cables

• 26 Aug 2025
• TM Team
• 0 Comments

NEC Corporation has announced the development of an optical fiber sensing technology to monitor road conditions and accurately predict sudden traffic congestion in real-time. By collecting data from existing optical fiber communications cables and analyzing real-time traffic flow data using a proprietary AI model, NEC has reduced prediction errors by 80% compared to conventional methods.

This technology enables effective traffic control, including the arrangement of detour routes, thereby contributing to the resolution of societal challenges such as logistics issues and CO2 emissions.

Traffic congestion is a significant social issue, threatening safety and causing substantial economic losses. In particular, congestion on expressways—the backbone of many logistics networks—has a significant economic impact. Addressing this issue requires real-time understanding of road conditions for both immediate congestion mitigation and proactive avoidance, as well as highly accurate congestion prediction based on up-to-date data.

Current monitoring approaches primarily rely on point-based instruments, including cameras and loop detectors, as well as probe vehicle data. However, both have limitations: the former is costly to install and maintain over entire road networks, and the latter only provides data when equipped vehicles pass by specific receiver points, limiting real-time and continuous coverage. Moreover, existing congestion prediction models heavily rely on long-term historical data, making it difficult to capture the emergence and propagation of sudden, unexpected congestion patterns.

To address these challenges, NEC has developed a technology that utilizes optical fiber cables—originally installed for communications purposes alongside roads—as extensive, distributed sensors for collecting real-time, continuous traffic flow data across entire routes. NEC’s unique AI-powered model then provides real-time, high-precision predictions regarding the development and clearance of sudden congestion.