Adaptive Mobility, Land Use and Infrastructure [AMI] - Qiming | Video Series

I'm Qiming Ye and I'm currently a postdoc researcher at the Future Cities Laboratory Global where our group work focuses on the adaptive planning for urban mobility infrastructure and land use in general. My research lies at the intersection of transportation systems, artificial intelligence and energy networks with a particular emphasis on how cities can effectively support the electrified and shared mobility services for the purpose of decarbonized, healthy and sustainable urbanism in the long term. Today, cities face a chicken and egg problem where inadequate charging infrastructure slows EV EV adoption while uncertain demand makes infrastructure investment risky. So one of our recent research on using reinforcement learning based adaptive charging infrastructure planning demonstrates how multi agent AI models can optimize charging station deployment and effectively optimize the spatial temporal sequences of charging infrastructure deployment that could Effectively reduce approximately 30% of chargers in need for Singapore compared to the baseline planning scenario. Meanwhile, the charging efficiency index improved by around 10%. By integrating multi period decision making, our model outperforms conventional deterministic planning methods ensuring both short term efficiency and long term resiliency are targeted. We are moving towards a future where autonomous shared electric vehicles, bidirectional energy flows like VTG and real time AI driven infrastructure planning will shape the way cities function. The ability to integrate real time traffic, charging demand and grid data into AI driven decision making framework will ensure truly smart and adaptive urban mobility ecosystems. The models we develop today will will have a direct impact on shaping sustainable and livable cities for the future.