NDLI: Feature selection-based approach for urban short-term travel speed prediction

Content Provider	IET Digital Library
Author	Zheng, Liang Zhu, Chuang Zhu, Ning He, Tian Dong, Ni Huang, Helai
Abstract	This study proposes a feature selection-based approach to identify reasonable spatial–temporal traffic patterns related to the target link, in order to improve the online-prediction performance. The prediction task is composed of two steps: one hybrid intelligent algorithm-based feature selector (FS) is proposed to optimise original state vectors, which are designed empirically during the offline process and optimised state vectors are employed to carry out the online prediction. Numerical experiments by three non-parametric algorithms are conducted with taxis’ global positioning system data in an urban road network of Changsha, China. It is concluded that: (i) under optimised state vectors, the prediction accuracies improve or almost maintain the same; (ii) K-nearest neighbour (KNN) with the simplest state vectors obtains the greatest improvement of prediction performance; (iii) although the performance improvement of ɛ-support vector regression is limited with optimised state vectors, it always outperforms backward-propagation neural network and KNN; and (iv) three non-parametric approaches with optimised state vectors outperform auto-regressive integrated moving average in relatively longer prediction horizons. In conclusion, such FS-based approach is able to improve or guarantee the prediction performance under the remarkably reduced model complexity, and is a promising methodology for short-term traffic prediction.
Starting Page	474
Ending Page	484
Page Count	11
ISSN	1751956X
Volume Number	12
e-ISSN	17519578
Issue Number	Issue 6, Aug (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/12/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2017.0059
Journal	IET Intelligent Transport Systems
Publisher Date	2018-01-31
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Changsha China Feature Selection Global Positioning System Data Hybrid Intelligent Algorithm K-nearest Neighbour KNN Online Prediction Performance Road Traffic Spatial Temporal Traffic Pattern System Theory Application in Transportation Target Link Urban Road Network Urban Short-term Travel Speed Prediction
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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