How to make AI truly scalable and reliable for real-time traffic assignment?
Peer-Reviewed Publication
Updates every hour. Last Updated: 3-Apr-2026 12:16 ET (3-Apr-2026 16:16 GMT/UTC)
To answer this question: How to make AI truly scalable and reliable for real-time traffic assignment? A research team from KTH Royal Institute of Technology, Monash University, Technical University of Munich, Southeast University, and the University of Electro-Communications has developed a new framework—MARL-OD-DA—that offers a promising answer. The approach redesigns learning agents at the origin–destination (OD) level and utilizes Dirichlet-based continuous actions to achieve stable and high-quality solutions under dynamic travel demand.
A “standard reference thermoelectric module (SRTEM)*” for objectively measuring thermoelectric module performance has been developed in Korea for the first time. A research team led by Dr. Sang Hyun Park at the Korea Institute of Energy Research (KIER; President Yi, Chang-Keun) developed the world’s second standard reference thermoelectric module, following Japan, and improved its performance by more than 20% compared with existing modules, demonstrating the excellence of Korea’s homegrown technology.
To address the trade-off between accuracy and cross-city generalization in traffic flow estimation, a research team from The Hong Kong Polytechnic University and New York University proposes a novel framework based on global open multi-source (GOMS) data, including urban structures and population density. By developing an advanced graph neural network model that effectively fuses these static urban features with dynamic traffic data, the study achieves stable and accurate network-wide traffic estimation, as validated across 15 diverse cities in Europe and North America.
Researchers at National University of Singapore used multiple interpretable machine learning methods to predict traffic congestion in in Alameda County in the San Francisco Bay Area, USA, during the pre-lockdown, lockdown, and post-lockdown periods.
Permafrost degradation on the Qinghai-Tibet Plateau is accelerating under climate warming, causing roadbed settlement, waterlogging, thawing interlayers, and long-term instability in high-altitude highways. The study reviews global and Chinese permafrost engineering practices, identifies the mechanisms behind pervasive subgrade deformation, and highlights challenges in current “permafrost-protection” design approaches. It proposes a third design principle — proactively improving foundation conditions — advancing from passive protection to active treatment. The work further summarizes shallow and deep foundation treatment techniques, evaluates engineering applications, and outlines development trends such as improved hydrological investigation, long-term monitoring, and novel construction materials.