Imperial College London
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Dr Fangce Guo

Faculty of Engineering, Department of Civil and Environmental Engineering

Advanced Research Fellow
 
 
 
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Contact

 

fangce.guo

 
 
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Location

 

308ASkempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Li:2021:10.1016/j.trc.2021.102977,
author = {Li, J and Guo, F and Sivakumar, A and Dong, Y and Krishnan, R},
doi = {10.1016/j.trc.2021.102977},
journal = {Transportation Research Part C: Emerging Technologies},
title = {Transferability Improvement in Short-term Traffic Prediction using Stacked LSTM Network},
url = {http://dx.doi.org/10.1016/j.trc.2021.102977},
volume = {124},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Short-term traffic flow forecasting is a key element in Intelligent Transport Systems (ITS) to provide proactive traffic state information to road network operators. A variety of methods to predict traffic variables in the short-term can be found in the literature, ranging from time-series algorithms, machine learning tools and deep learning methods to a selective hybrid of these approaches. Despite the advances in prediction techniques, a challenging problem that affects the application of such methods in the real world is the prevalence of insufficient data across an entire network. It is rare that extensive historical training data required for model training are available for all the links in a city. In order to address this data insufficiency problem, this paper applies transfer learning techniques to machine learning methods in short-term traffic prediction. All the traffic data used in this paper were collected from Highways England road networks in the UK. The results show that through improving the transferability of machine learning-based models, the computational burden due to the model training process can be significantly reduced and the prediction accuracy under data deficient scenarios can be improved for one-step ahead prediction. However, the prediction accuracy gradually decreases in multi-step ahead prediction. It is also found that the accuracy of the proposed hybrid method is highly dependent upon consistency between datasets but less dependent on geographical attributes of links.
AU  - Li,J
AU  - Guo,F
AU  - Sivakumar,A
AU  - Dong,Y
AU  - Krishnan,R
DO  - 10.1016/j.trc.2021.102977
PY  - 2021///
SN  - 0968-090X
TI  - Transferability Improvement in Short-term Traffic Prediction using Stacked LSTM Network
T2  - Transportation Research Part C: Emerging Technologies
UR  - http://dx.doi.org/10.1016/j.trc.2021.102977
UR  - http://hdl.handle.net/10044/1/86572
VL  - 124
ER  -
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