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dc.contributor.authorSong, Y.
dc.contributor.authorWright, G.
dc.contributor.authorWu, Peng
dc.contributor.authorThatcher, D.
dc.contributor.authorMcHugh, T.
dc.contributor.authorLi, Q.
dc.contributor.authorLi, S.
dc.contributor.authorWang, X.
dc.date.accessioned2019-02-19T04:18:04Z
dc.date.available2019-02-19T04:18:04Z
dc.date.created2019-02-19T03:58:16Z
dc.date.issued2018
dc.identifier.citationSong, Y. and Wright, G. and Wu, P. and Thatcher, D. and McHugh, T. and Li, Q. and Li, S. et al. 2018. Segment-based spatial analysis for assessing road infrastructure performance using monitoring observations and remote sensing data. Remote Sensing. 10 (11): Article ID 1696.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/74800
dc.identifier.doi10.3390/rs10111696
dc.description.abstract

Road infrastructure is important to the well-being and economic health of all nations. The performance of road pavement infrastructure is sophisticated and affected by numerous factors and varies greatly across different roads. Large scale spatial analysis for assessing road infrastructure performance is increasingly required for road management, therefore multi-source factors, including satellite remotely sensed climate and environmental data, and ground-monitored vehicles observations, are collected as explanatory variables. Different from the traditional point or area based geospatial attributes, the performance of pavement infrastructure is the line segment based spatial data. Thus, a segment-based spatial stratified heterogeneitymethod is utilized to explore the comprehensive impacts of vehicles, climate, properties of road and socioeconomic conditions on pavement infrastructure performance. Segment-based optimal discretization is applied on discretizing segment-based pavement data, and a segment-based geographical detector is utilized to assess the spatial impacts of variables and their interactions. Results show that the segment-based methods can more reasonably and accurately describe the characteristics of line segment based spatial data and assess the spatial associations. The two major categories of factors associated with pavement damage are the variables of traffic vehicles and heavy vehicles in particular, and climate and environmental conditions. Meanwhile, the interactions between the explanatory variables in these two categories have much more influence than the single explanatory variables, and the interactions can explain more than half of the pavement damage. This study highlights the great potential of remote sensing based large scale spatial analysis of road infrastructures. The approach in this study provides new ideas for spatial analysis for segmented geographical data. The findings indicate that the quantified comprehensive impacts of variables are practical for wise decision-making for road design, construction and maintenance.

dc.publisherMDPI AG
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleSegment-based spatial analysis for assessing road infrastructure performance using monitoring observations and remote sensing data
dc.typeJournal Article
dcterms.source.volume10
dcterms.source.number11
dcterms.source.issn2072-4292
dcterms.source.titleRemote Sensing
curtin.departmentSchool of Design and the Built Environment
curtin.accessStatusOpen access


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