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AI for Reducing Urban Traffic Emissions
The current rise in urbanisation, combined with socio-economic drivers for increased mobility, is placing significant strain on transport infrastructure, leading to notable repercussions for emissions and air quality in urban areas. In this talk, we will focus on the application of model-based AI to urban traffic control. First, we will explore how AI can be used to optimise traffic signal control and the associated environmental benefits. Then, we will present how connected and autonomous vehicles (CAVs) can enable more direct traffic control and unlock further potential for reducing emissions.
University of Huddersfield, UK
Prof. Mauro Vallati is a Professor of Artificial Intelligence at the University of Huddersfield (UK), UKRI Future Leaders Fellow, ACM Senior Member, and ACM Distinguished Speaker on AI. He directs the Research Centre on Autonomous and Intelligent Systems at University of Huddersfield and has an extensive track record in AI Planning. His research focuses on developing intelligent planning systems for urban traffic management and sustainable mobility, advancing techniques that enable adaptive, explainable, and data-driven control of complex urban environments. Through his work on automated planning, connected autonomous vehicles, and LLM-assisted decision support, Prof. Vallati contributes to the mission of leveraging AI to build sustainable, resilient, and environmentally conscious systems.
At ECAI-2025, Prof. Vallati is organising the Workshop on Reasoning and Learning for Intelligent Transport (ReLiT), which will be of particular interest to the AISE community and to scholars exploring how AI can support and impact sustainable mobility.
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