Imperial College London
Alternate

Dr Simon Hu

Faculty of Engineering, Department of Civil and Environmental Engineering

Honorary Research Fellow
 
 
 
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Contact

 

+44 (0)20 7594 6024j.s.hu05

 
 
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Location

 

422Skempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Zhang:2022:10.1061/9780784484265.066,
author = {Zhang, K and Li, J and Zhou, Q and Hu, S},
doi = {10.1061/9780784484265.066},
pages = {699--711},
title = {Short-Term Traffic Prediction with Balanced Domain Adaptation},
url = {http://dx.doi.org/10.1061/9780784484265.066},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Short-term traffic forecasting has been a hot topic in the intelligent transportation systems field. The traditional traffic forecasting methods mostly fix traffic sensors. However, most sensors are subject to bad conditions, leading to noisy and insufficient raw data. Recent advances have provided new traffic prediction opportunities. For example, the transfer learning method takes advantage of data trained on one good dataset and transfers the knowledge to others with bad data. Existing applications do not consider the underlying data distributions sufficiently, limiting the prediction performance. We propose a transfer learning-based traffic flow prediction framework using the Balanced Domain Adaptation (BDA) method. Various regression models are fed into the framework to evaluate a good data source and predict bad target datasets. A case study using data from the Highways England is conducted. The results show that the proposed BDA-based framework can match the distributions between traffic flow datasets and significantly improve prediction accuracy.
AU  - Zhang,K
AU  - Li,J
AU  - Zhou,Q
AU  - Hu,S
DO  - 10.1061/9780784484265.066
EP  - 711
PY  - 2022///
SP  - 699
TI  - Short-Term Traffic Prediction with Balanced Domain Adaptation
UR  - http://dx.doi.org/10.1061/9780784484265.066
ER  -
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