Comparison of algorithms for road surface temperature prediction

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dc.citation.issue3
dc.citation.volume2
dc.contributor.authorLiu B
dc.contributor.authorShen L
dc.contributor.authorYou H
dc.contributor.authorDong Y
dc.contributor.authorLi J
dc.contributor.authorLi Y
dc.date.accessioned2023-11-16T02:02:34Z
dc.date.accessioned2023-11-20T01:38:14Z
dc.date.available2018-11-13
dc.date.available2023-11-16T02:02:34Z
dc.date.available2023-11-20T01:38:14Z
dc.date.issued2018-12-13
dc.description.abstractPurpose: 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.
dc.description.confidentialfalse
dc.format.pagination212-224
dc.identifier.citationLiu B, Shen L, You H, Dong Y, Li J, Li Y. (2018). Comparison of algorithms for road surface temperature prediction. International Journal of Crowd Science. 2. 3. (pp. 212-224).
dc.identifier.doi10.1108/IJCS-09-2018-0021
dc.identifier.eissn2398-7294
dc.identifier.elements-typejournal-article
dc.identifier.issn2398-7294
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/69167
dc.languageEnglish
dc.publisherEmerald Publishing Limited
dc.publisher.urihttps://www.emerald.com/insight/content/doi/10.1108/IJCS-09-2018-0021/full/html
dc.relation.isPartOfInternational Journal of Crowd Science
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectNeural network
dc.subjectGradient boosting regression tree
dc.subjectRandom Forest
dc.subjectRoad surface temperature
dc.titleComparison of algorithms for road surface temperature prediction
dc.typeJournal article
pubs.elements-id450794
pubs.organisational-groupOther
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