Dr Simon Hu is a Research Fellow at the Transport and Environmental Laboratory (TEL), within the Centre for Transport Studies (CTS) at Imperial College London. He has obtained BSc (2005) in Civil Engineering, holds a MSc (2007) and PhD (2011) degrees in Transport System Engineering from Imperial College London.
His research interests lie in smart urban transport systems, traffic micro-simulation modelling, vehicle emission monitoring as well as airport air quality management. His work to date has spanned across the areas of vehicle navigation, network reliability, traffic prediction and management, bike sharing, urban freight logistics emissions, aviation and railway engineering. In the past, he has obtained funding and undertook research projects through Department for Transport, UK Natural Environmental Research Council, Engineering and Physical Sciences Research Council, European Commission, INNOVATE UK and industry.
Current research interests
- Modelling and monitoring of urban air quality
- Vehicle emission modelling
- Traffic simulation and modelling
- Traffic prediction and data fusion
- Public transport (bike sharing, accessibility, personal rapid transit etc)
- Modelling and evaluating of autonomous vehicle
- Urban freight logistics
et al., 2021, Understanding City-Wide Ride-Sourcing Travel Flow: A Geographically Weighted Regression Approach, Journal of Advanced Transportation, Vol:2021, ISSN:0197-6729
et al., 2021, Dynamic wireless power transfer system for electric-powered connected and autonomous vehicle on urban road network, Iet Intelligent Transport Systems, Vol:15, ISSN:1751-956X, Pages:1153-1166
et al., 2021, Urban traffic route guidance method with high adaptive learning ability under diverse traffic scenarios, Ieee Transactions on Intelligent Transportation Systems, Vol:22, ISSN:1524-9050, Pages:2956-2968
et al., 2021, Freeway Traffic Control in Presence of Capacity Drop, Ieee Transactions on Intelligent Transportation Systems, Vol:22, ISSN:1524-9050, Pages:1497-1516
et al., 2020, Machine learning techniques to predict reactionary delays and other associated key performance indicators on British railway network, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, Vol:2020, ISSN:1547-2450, Pages:1-19