Researchers investigated reinforcement learning (RL) for adaptive traffic signal control at an urban intersection in Kuwait, aiming to mitigate urban traffic congestion. They developed a Proximal Policy Optimization (PPO)-based controller that dynamically adjusts green-phase durations using local traffic states in a realistic simulation environment informed by real-world Kuwaiti traffic data. The controller reduced average vehicle delay by 46% relative to fixed-time control and 34% relative to actuated control, while also lowering per-vehicle CO2 emissions by approximately 23%. Why it matters: This demonstrates a practical, learning-based edge traffic signal control solution for IoT-enabled smart city transportation systems, offering significant improvements in traffic flow and environmental impact for car-dependent cities in the Middle East.
Yango Drive, a mobility technology company, has released its 2025 Mobility Report focusing on the impact of artificial intelligence on the UAE's travel ecosystem. The report analyzes current trends and future predictions, detailing how AI is reshaping urban transportation, logistics, and consumer travel experiences in the Emirates. It likely highlights areas such as traffic management optimization, autonomous vehicle integration, and personalized mobility solutions driven by AI technologies. Why it matters: This report provides insights into the strategic role of AI in developing smart city infrastructure and sustainable urban mobility in the UAE, influencing future policy and investment.
Saudi Air Navigation Services (SANS) and KAUST have signed an MoU to collaborate on research, innovation, and advanced technology deployment in aviation. The partnership aims to leverage KAUST's AI and data analytics expertise to support SANS' digital transformation and the National Aviation Strategy. The collaboration will focus on developing data-driven solutions for air traffic management, operational efficiency, and national capability development. Why it matters: This MoU signifies a strategic alignment between academia and a key aviation sector player to advance technological capabilities and support Saudi Arabia's national transport and logistics strategy.
This article discusses the application of uncertain time series (UTS) approach to manage and analyze big traffic data for high-resolution vehicular transportation services. The study addresses challenges such as data sparseness, decision-making among multiple UTSs, and future forecasting with spatio-temporal correlations. Jilin Hui, previously a Research Associate at the Inception Institute of Artificial Intelligence (UAE), is applying this approach to solve problems related to increased congestion, greenhouse gas emissions, and reduced air quality in urban environments. Why it matters: The application of AI techniques to traffic management could significantly improve urban mobility and environmental sustainability in the GCC region and beyond.