Dr Fangce Guo is an advanced research fellow in the Urban Systems Laboratory (USL) and Centre for Transport Studies (CTS) at Imperial College London. Her current research interests include short-term traffic forecasting, sensor data analysis, traffic state estimation, traffic data fusion and car park management in Intelligent Transport Systems (ITS).
Fangce holds a BS in Electronic/Information Engineering and a BA from Dalian University of Technology China, an MSc in Communications and Signal Processing and a PhD in Intelligent Transport Systems from Imperial College London.
Fangce is working on an urban health project to reduce health inequalities in cities worldwide funded by the Wellcome Trust. She recently completed working on the oneTRANSPORT project (onetransport.uk.net), funded by Innovate UK, which developed an IoT platform for predictive analytics in the transport field.
et al., 2023, Few-Shot traffic prediction based on transferring prior knowledge from local network, Transportmetrica B: Transport Dynamics, Vol:11, ISSN:2168-0566, Pages:1664-1686
et al., 2023, When does it pay off to use electricity demand data with rich information about households and their activities? A comparative machine learning approach to demand modelling, Energy and Buildings, Vol:295, ISSN:0378-7788, Pages:1-15
et al., 2023, Modular Bus Unit Scheduling for an Autonomous Transit System under Range and Charging Constraints, Applied Sciences (switzerland), Vol:13
Zhai X, Guo F, Krishnan R, 2023, An Online Optimal Bus Signal Priority Strategy to Equalise Headway in Real-Time, Information, Vol:14
et al., 2023, Car-following model calibration with load effect as additional optimisation objective, Structure and Infrastructure Engineering, ISSN:1573-2479