Segment-Based Spatial Analysis for Assessing Road Infrastructure Performance Using Monitoring Observations and Remote Sensing Data

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 performan...

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Main Authors: Yongze Song, Graeme Wright, Peng Wu, Dominique Thatcher, Tom McHugh, Qindong Li, Shuk Jin Li, Xiangyu Wang
Format: Article
Language:English
Published: MDPI AG 2018-10-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/10/11/1696
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spelling doaj-db3e224d3d934005bcd54aa14d551bbf2020-11-24T21:45:45ZengMDPI AGRemote Sensing2072-42922018-10-011011169610.3390/rs10111696rs10111696Segment-Based Spatial Analysis for Assessing Road Infrastructure Performance Using Monitoring Observations and Remote Sensing DataYongze Song0Graeme Wright1Peng Wu2Dominique Thatcher3Tom McHugh4Qindong Li5Shuk Jin Li6Xiangyu Wang7Australasian Joint Research Centre for Building Information Modelling, School of Design and the Built Environment, Curtin University, Perth 6845, AustraliaOffice of Research and Development, Curtin University, Perth 6845, AustraliaSchool of Design and the Built Environment, Curtin University, Perth 6845, AustraliaHeavy Vehicle Services, Main Roads Western Australia, Perth 6986, AustraliaHead Office—Don Aitken Centre, Main Roads Western Australia, Perth 6004, AustraliaHead Office—Don Aitken Centre, Main Roads Western Australia, Perth 6004, AustraliaHead Office—Don Aitken Centre, Main Roads Western Australia, Perth 6004, AustraliaAustralasian Joint Research Centre for Building Information Modelling, School of Design and the Built Environment, Curtin University, Perth 6845, AustraliaRoad 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 heterogeneity method 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.https://www.mdpi.com/2072-4292/10/11/1696road infrastructuresegment-based spatial analysisremote sensing dataclimateheavy vehiclespavementgeographical detector
collection DOAJ
language English
format Article
sources DOAJ
author Yongze Song
Graeme Wright
Peng Wu
Dominique Thatcher
Tom McHugh
Qindong Li
Shuk Jin Li
Xiangyu Wang
spellingShingle Yongze Song
Graeme Wright
Peng Wu
Dominique Thatcher
Tom McHugh
Qindong Li
Shuk Jin Li
Xiangyu Wang
Segment-Based Spatial Analysis for Assessing Road Infrastructure Performance Using Monitoring Observations and Remote Sensing Data
Remote Sensing
road infrastructure
segment-based spatial analysis
remote sensing data
climate
heavy vehicles
pavement
geographical detector
author_facet Yongze Song
Graeme Wright
Peng Wu
Dominique Thatcher
Tom McHugh
Qindong Li
Shuk Jin Li
Xiangyu Wang
author_sort Yongze Song
title Segment-Based Spatial Analysis for Assessing Road Infrastructure Performance Using Monitoring Observations and Remote Sensing Data
title_short Segment-Based Spatial Analysis for Assessing Road Infrastructure Performance Using Monitoring Observations and Remote Sensing Data
title_full Segment-Based Spatial Analysis for Assessing Road Infrastructure Performance Using Monitoring Observations and Remote Sensing Data
title_fullStr Segment-Based Spatial Analysis for Assessing Road Infrastructure Performance Using Monitoring Observations and Remote Sensing Data
title_full_unstemmed Segment-Based Spatial Analysis for Assessing Road Infrastructure Performance Using Monitoring Observations and Remote Sensing Data
title_sort segment-based spatial analysis for assessing road infrastructure performance using monitoring observations and remote sensing data
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2018-10-01
description 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 heterogeneity method 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.
topic road infrastructure
segment-based spatial analysis
remote sensing data
climate
heavy vehicles
pavement
geographical detector
url https://www.mdpi.com/2072-4292/10/11/1696
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