Fangce Guo
Fangce Guo
Research Fellow, Imperial College London
Verified email at imperial.ac.uk - Homepage
Title
Cited by
Cited by
Year
A computationally efficient two-stage method for short-term traffic prediction on urban roads
F Guo, R Krishnan, J Polak
Transportation planning and technology 36 (1), 62-75, 2013
472013
Short-term traffic prediction under normal and incident conditions using singular spectrum analysis and the k-nearest neighbour method
F Guo, R Krishnan, JW Polak
IET Digital Library, 2012
282012
Comparison of modelling approaches for short term traffic prediction under normal and abnormal conditions
F Guo, JW Polak, R Krishnan
13th International IEEE Conference on Intelligent Transportation Systems …, 2010
262010
Predictor fusion for short-term traffic forecasting
F Guo, JW Polak, R Krishnan
Transp. Res. C, Emerg. Technol. 92, 90-100, 2018
202018
Short-term traffic prediction under normal and abnormal traffic conditions on urban roads
F Guo, R Krishnan, JW Polak
Transportation Research Board 91st Annual Meeting, 2012
152012
A deep learning approach for traffic incident detection in urban networks
L Zhu, F Guo, R Krishnan, JW Polak
2018 21st International Conference on Intelligent Transportation Systems …, 2018
122018
The use of convolutional neural networks for traffic incident detection at a network level
L Zhu, F Guo, R Krishnan, JW Polak
Transportation Research Board 97th Annual MeetingTransportation Research Board, 2018
82018
A novel three-stage framework for short-term travel time prediction under normal and abnormal traffic conditions
F Guo, R Krishnan, JW Polak
Transportation Research Board Annual Meeting, 2014
72014
Urban link travel time estimation using traffic states-based data fusion
L Zhu, F Guo, JW Polak, R Krishnan
IET Intelligent Transport Systems 12 (7), 651-663, 2018
62018
Short-term traffic prediction under normal and abnormal conditions
F Guo
Imperial College London, 2013
52013
The influence of alternative data smoothing prediction techniques on the performance of a two-stage short-term urban travel time prediction framework
F Guo, R Krishnan, J Polak
Journal of Intelligent Transportation Systems 21 (3), 214-226, 2017
32017
Evaluating grid-interactive electric bus operation and demand response with load management tariff
Z Wu, F Guo, J Polak, G Strbac
Applied Energy 255, 113798, 2019
22019
A computationally efficient 2-stage method for short-term traffic prediction on urban roads
F Guo, R Krishnan, J Polak
44th Annual Universities Transport Studies Group (UTSG) Conference, 2012
22012
MULTI-SENSOR FUSION BASED ON THE DATA FROM BUS GPS, MOBILE PHONE AND LOOP DETECTORS IN TRAVEL TIME ESTIMATION
L Zhu, F Guo, JW Polak, R Krishnan
Transportation Research Board 96th Annual MeetingTransportation Research Board, 2017
12017
Right-of-way reallocation for mixed flow of autonomous vehicles and human driven vehicles
T Li, F Guo, R Krishnan, A Sivakumar, J Polak
Transportation Research Part C: Emerging Technologies 115, 102630, 2020
2020
Traffic Monitoring and Anomaly Detection based on Simulation of Luxembourg Road Network
L Zhu, R Krishnan, A Sivakumar, F Guo, JW Polak
2019 IEEE Intelligent Transportation Systems Conference (ITSC), 382-387, 2019
2019
Early Identification of Recurrent Congestion in Heterogeneous Urban Traffic
L Zhu, R Krishnan, F Guo, JW Polak, A Sivakumar
2019 IEEE Intelligent Transportation Systems Conference (ITSC), 4392-4397, 2019
2019
Disaggregate Short-Term Location Prediction Based on Recurrent Neural Network and an Agent-Based Platform
Y Dong, J Polak, A Sivakumar, F Guo
Transportation Research Record 2673 (8), 657-668, 2019
2019
Spatial-Temporal Hybrid Deep Neural Networks for Early Congestion Detection
L Zhu, F Guo, R Krishnan, JW Polak
The system can't perform the operation now. Try again later.
Articles 1–19