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Research paper | Open Access

Comparison of algorithms for road surface temperature prediction

Bo Liu1( ), Libin Shen2, Huanling You3, Yan Dong4, Jianqiang Li2, Yong Li2
Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China and School of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing, China
School of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing, China
China Meteorological Administration, Beijing, China
Beijing Meteorological Service Center, Beijing, China
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Abstract

Purpose

The influence of road surface temperature (RST) on vehicles is becoming more and more obvious. Accurate predication of RST is distinctly meaningful. At present, however, the prediction accuracy of RST is not satisfied with physical methods or statistical learning methods. To find an effective prediction method, this paper selects five representative algorithms to predict the road surface temperature separately.

Design/methodology/approach

Multiple linear regressions, least absolute shrinkage and selection operator, random forest and gradient boosting regression tree (GBRT) and neural network are chosen to be representative predictors.

Findings

The experimental results show that for temperature data set of this experiment, the prediction effect of GBRT in the ensemble algorithm is the best compared with the other four algorithms.

Originality/value

This paper compares different kinds of machine learning algorithms, observes the road surface temperature data from different angles, and finds the most suitable prediction method.

Keywords

Random ForestNeural networkGradient boosting regression treeRoad surface temperature

References

 

Breiman, L. (1996), “Bagging predictors”, Machine Learning, Vol. 24 No. 2, pp. 123-140.
Crossref Google Scholar
 

Breiman, L. (2001), “Random forests”, Machine Learning, Vol. 45 No. 1, pp. 5-32.
Google Scholar
 

Dai, Y., Zeng, X., Dickinson, R., Baker, I., Bonan, G.B., Bosilovich, M.G. and Oleson, K., W. (2003), “The common land model”, Bulletin of the American Meteorological Society, Vol. 84 No. 8, pp. 1013-1023.
Crossref Google Scholar
 

Edward, B. (1957), “Calculation of maximum pavement temperatures from weather reports”, Highway Research Board Bulletin, Vol. 168, pp. 1-8.
Google Scholar
 

Feng, T. and Feng, S. (2012), “A numerical model for predicting road surface temperature in the highway”, Procedia Engineering, Vol. 37, pp. 137-142.
Crossref Google Scholar
 

Friedman, J.H. (2001), “Greedy function approximation”, The Annals of Statistics, Vol. 29 No. 5, pp. 1189-1232.
Google Scholar
 

Hansen, L.K. and Salamon, P. (1990), “Neural network ensembles”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12 No. 10, pp. 993-1001.
Crossref Google Scholar
 
Hastie, T. (2009), The Elements of Statistical Learning, Springer Science, USA.https://doi.org/10.1007/978-0-387-84858-7
Crossref
 

LeCun, Y., Bengio, Y. and Hinton, G. (2015), “Deep learning”, Nature, Vol. 521 No. 7553, pp. 436-521.
Crossref Google Scholar
 
Li, H. (2012), Statistical Learning Methods, Tsinghua University Press, Beijing.
 

Liu, Y. (2005), “A mathematical model of multiple linear regression”, Journal of Shenyang Institute of Engineering, Vol. 1 No. 2, pp. 128-129.
Google Scholar
 
Liu, B., Yan, S., You, H., Dong, Y., Li, J., Li, Y. and Gu, R. (2017), “An ensembled RBF extreme learning machine to forecast road surface temperature”, 16th ICMLA, IEEE International Conference, IEEE, pp. 977-980.https://doi.org/10.1109/ICMLA.2017.00-26
Crossref
 

Meng, C. and Liu, C. (2009), “Summer road temperature disaster forecast of expressway in Beijing”, J.of Institute of Disaster-Prevention Science and Technology, Vol. 03 No. 7, pp. 26-29.
Google Scholar
 

Robert, T. (1996), “Regression shrinkage and selection via the LASSO”, Journal of the Royal Statistical Society, Vol. 58 No. 1, pp. 267-288.
Crossref Google Scholar
 

Sass, B. (1997), “A numerical forecasting system for the prediction of slippery roads”, Journal of Applied Meteorology, Vol. 36 No. 6, pp. 801-817.
Crossref
 

Tibshirani, R. (2011), “Regression shrinkage and selection via the lasso”, Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 73 No. 3, pp. 273-282.
Crossref Google Scholar
 

Wei, D., You, F., Fan, S., Yang, B. and Chen, M. (2010), “Evaluation and analysis of sounding quality of Beijing rapid renewal cycle prediction system (BJ-RUC) model”, Journal of Meteorological, Vol. 36 No. 8, pp. 72-80.
Google Scholar
 
Zhou, Z.H. (2009), “Ensemble learning”, pp. 13-14.https://doi.org/10.7748/mhp.13.1.14.s12
Crossref
 
Zhou, Z.H. (2016), Machine Learning, Tsinghua University Press, Beijing.
International Journal of Crowd Science
Volume 2 Issue 3,
December 2018
Pages 212-224
DOI: 10.1108/IJCS-09-2018-0021
Cite this article:
Liu B, Shen L, You H, et al. Comparison of algorithms for road surface temperature prediction. International Journal of Crowd Science, 2018, 2(3): 212-224. https://doi.org/10.1108/IJCS-09-2018-0021

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Received: 09 September 2018
Revised: 11 October 2018
Accepted: 13 October 2018
Published: 13 November 2018
© The author(s)

Bo Liu, Libin Shen, Huanling You, Yan Dong, Jianqiang Li and Yong Li. Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
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