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dc.contributor.authorSong, Y.
dc.contributor.authorWang, Xiangyu
dc.contributor.authorWright, G.
dc.contributor.authorThatcher, D.
dc.contributor.authorWu, Peng
dc.contributor.authorFelix, P.
dc.date.accessioned2018-05-18T07:57:52Z
dc.date.available2018-05-18T07:57:52Z
dc.date.created2018-05-18T00:22:57Z
dc.date.issued2018
dc.identifier.citationSong, Y. and Wang, X. and Wright, G. and Thatcher, D. and Wu, P. and Felix, P. 2018. Traffic Volume Prediction With Segment-Based Regression Kriging and its Implementation in Assessing the Impact of Heavy Vehicles. IEEE Transactions on Intelligent Transportation Systems. 20 (1): pp. 232-243.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/67275
dc.identifier.doi10.1109/TITS.2018.2805817
dc.description.abstract

Geostatistical methods have been widely used for spatial prediction and the assessment of traffic issues. Most previous studies use point-based interpolation, but they ignore the critical information of the road segment itself. This can lead to inaccurate predictions, which will negatively affect decision making of road agencies. To address this problem, segment-based regression kriging (SRK) is proposed for traffic volume prediction with differentiation between heavy and light vehicles in the Wheatbelt region of Western Australia. Cross validations reveal that the prediction accuracy for heavy vehicles is significantly improved by SRK (R²=0.677). Specifically, 78% of spatial variance and 53% of estimated uncertainty are improved by SRK for heavy vehicles compared with regression kriging, a best performing point-based geostatistical model. This improvement shows that SRK can provide new insights into the spatial characteristics and spatial homogeneity of a road segment. Implementation results of SRK-based predictions show that the impact of heavy vehicles on road maintenance is much larger than that of light vehicles and it varies across space, and the total impacts of heavy vehicles account for more than 82% of the road maintenance burden even though its volume only accounts for 21% of traffic.

dc.publisherIEEE Intelligent Transportation Systems Society
dc.titleTraffic Volume Prediction With Segment-Based Regression Kriging and its Implementation in Assessing the Impact of Heavy Vehicles
dc.typeJournal Article
dcterms.source.issn1524-9050
dcterms.source.titleIEEE Transactions on Intelligent Transportation Systems
curtin.departmentSchool of Design and the Built Environment
curtin.accessStatusFulltext not available


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