The IM Group presents at the TRISTAN Conference in Okinawa, Japan

TRISTAN is a prestigious symposium held every three years, serving as a platform for advancing methodological research in transportation science and operations research.

Their study was selected through a competitive process, ranking among the top 151 papers from 496 submissions representing 50 countries and regions. Dr Qiming Ye presented the work, which proposes a reinforcement learning-based framework for long-term, city-scale deployment of electric vehicle charging infrastructure under deep uncertainty in travel and charging demand. The framework was tested using a case study of Singapore. This presentation builds on a series of studies, including the published one “external page Multi-period Charging Infrastructure Planning under Uncertainty: Challenges and Opportunities” in Sustainable Cities and Society, with the latest work currently under review. This line of research is part of the Adaptive Mobility, Infrastructure, and Land-Use (AMIL) module at the Future Cities Laboratory Global (FCL-G), Singapore-ETH Centre (SEC), supported by ETH Zurich and the National Research Foundation of Singapore.

Assistant external page Professor Prateek Bansal chaired the "Reinforcement Learning" session, which also featured contributions from Assistant Professor Dr Manxi Wu (external page UC Berkeley) and Dr Yang Deng (external page Tsinghua University & City University of Hong Kong), who presented their latest research.

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