Tang Li is a Research Associate in the Urban Systems Laboratory (USL) and Centre for Transport Engineering and Modelling (CTEM) at Imperial College London (ICL). Tang has over ten years of academic and research experience in the transport systems and has contributed as a researcher, named researcher, co–principal investigator (Co-PI) or principal investigator (PI) on several cutting-edge research projects funded by Innovate UK, the Road Safety Trust, and the Royal Society. Tang has also received endorsement from Royal Academy of Engineering under the UK Global Talent framework in recognition of his research achievements and contributions to transport innovation.
Throughout his career, Tang has been dedicated to advancing intelligent and sustainable transport systems. His research focuses on transport decarbonisation, e-mobility adoption, and low-carbon urban mobility transitions, particularly in Low- and Middle-Income Countries (LMICs). He is involved in research activities under the Climate Compatible Growth (CCG) programme, where his work focuses on developing evidence-based modelling approaches and practical tools to support transport electrification, sustainable mobility transitions, and net-zero transport strategies. He has contributed to developing tools and approaches, including EV-APS, OseMobility, and CITED, which aim to enhance sustainable transport planning, EV transition analysis, accessibility assessment, and data-driven policymaking for climate-resilient, inclusive, and equitable urban mobility systems. Tang also integrates Gender Equality and Social Inclusion (GESI) considerations into his research, promoting equitable and accessible transport solutions that support inclusive and sustainable development.
His broader research interests include sustainability, Intelligent Transport Systems (ITS), Connected Autonomous Vehicles, traffic control and management, Stated Choice Experiments (SCE), Discrete Choice Models (DCM), Virtual Reality (VR)-based behavioural studies, urban railway systems, urban rail transit, and urban systems modelling. Through interdisciplinary research integrating advanced transport modelling, AI-driven analytics, and sustainable mobility planning, Tang aims to bridge cutting-edge methodologies with practical applications in future mobility systems, infrastructure planning, and equitable low-carbon transport transitions.