Imperial College London

Dr Fangce Guo

Faculty of EngineeringDepartment of Civil and Environmental Engineering

Research Fellow
 
 
 
//

Contact

 

fangce.guo

 
 
//

Location

 

422Skempton BuildingSouth Kensington Campus

//

Summary

 

Summary

Dr Fangce Guo is a 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 in the urban health project to reduce health inequalities in cities around the world funded by the Welcome 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.

Publications

Journals

Wu Z, Guo F, Polak J, et al., Evaluating grid-interactive electric bus operation and demand response with load management tariff, Applied Energy, ISSN:0306-2619

Dong Y, Polak J, Sivakumar A, et al., Disaggregate Short-Term Location Prediction Based on Recurrent Neural Network and an Agent-Based Platform, Transportation Research Record: Journal of the Transportation Research Board, ISSN:0361-1981, Pages:036119811984588-036119811984588

Zhu L, Guo F, Polak J, et al., 2018, Urban link travel time estimation using traffic states based data fusion, Iet Intelligent Transport Systems, Vol:12, ISSN:1751-956X, Pages:651-663

Guo F, Polak JW, Krishnamoorthy R, 2018, Predictor fusion for short-term traffic forecasting, Transportation Research Part C: Emerging Technologies, Vol:92, ISSN:0968-090X, Pages:90-100

Conference

Zhu L, Guo F, Krishnamoorthy R, et al., The Use of Convolutional Neural Networks for Traffic Incident Detection at a Network Level, Transportation Research Board 97th Annual Meeting

More Publications