A Spatial Heterogeneity-Based Segmentation Model for Analyzing Road Deterioration Network Data in Multi-Scale Infrastructure Systems
Road network conditions and road quality are directly linked with the performance of an entire infrastructure system. As sensor monitoring of road deteriorations has rapidly increased, road infrastructure performance can now be assessed using multiple measures. However, more effective and accurate q...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
IEEE
2020
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| Online Access: | http://purl.org/au-research/grants/arc/DP180104026 http://hdl.handle.net/20.500.11937/82713 |
| _version_ | 1848764538078363648 |
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| author | Song, Yongze Wu, Peng Gilmore, Daniel Li, Qindong |
| author_facet | Song, Yongze Wu, Peng Gilmore, Daniel Li, Qindong |
| author_sort | Song, Yongze |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Road network conditions and road quality are directly linked with the performance of an entire infrastructure system. As sensor monitoring of road deteriorations has rapidly increased, road infrastructure performance can now be assessed using multiple measures. However, more effective and accurate quantitative analysis methods are increasingly required. This research explores road infrastructure performance using road deterioration network data in the Mid West Gascoyne region, Australia. A spatial heterogeneity-based segmentation (SHS) model is developed for redefining road segments across the network in terms of sensor monitoring data, and for both project-level and network-level infrastructure systems management. To evaluate the model effectiveness and accuracy, an evaluation system is proposed from four aspects: segment number, homogeneity within segments, heterogeneity among segments, and segment morphology. The SHS model is compared with two widely used road network segmentation methods. The results show that the SHS model can use fewer segments to ensure higher homogeneity within segments and heterogeneity among segments across the network. Meanwhile, the segment lengths are more uniformly distributed as compared with results from other methods. The developed model and findings from this research can significantly improve the utilization of sensor monitoring network data and support multi-scale infrastructure systems management. |
| first_indexed | 2025-11-14T11:20:56Z |
| format | Journal Article |
| id | curtin-20.500.11937-82713 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:20:56Z |
| publishDate | 2020 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-827132023-06-07T02:27:40Z A Spatial Heterogeneity-Based Segmentation Model for Analyzing Road Deterioration Network Data in Multi-Scale Infrastructure Systems Song, Yongze Wu, Peng Gilmore, Daniel Li, Qindong Road network conditions and road quality are directly linked with the performance of an entire infrastructure system. As sensor monitoring of road deteriorations has rapidly increased, road infrastructure performance can now be assessed using multiple measures. However, more effective and accurate quantitative analysis methods are increasingly required. This research explores road infrastructure performance using road deterioration network data in the Mid West Gascoyne region, Australia. A spatial heterogeneity-based segmentation (SHS) model is developed for redefining road segments across the network in terms of sensor monitoring data, and for both project-level and network-level infrastructure systems management. To evaluate the model effectiveness and accuracy, an evaluation system is proposed from four aspects: segment number, homogeneity within segments, heterogeneity among segments, and segment morphology. The SHS model is compared with two widely used road network segmentation methods. The results show that the SHS model can use fewer segments to ensure higher homogeneity within segments and heterogeneity among segments across the network. Meanwhile, the segment lengths are more uniformly distributed as compared with results from other methods. The developed model and findings from this research can significantly improve the utilization of sensor monitoring network data and support multi-scale infrastructure systems management. 2020 Journal Article http://hdl.handle.net/20.500.11937/82713 10.1109/TITS.2020.3001193 http://purl.org/au-research/grants/arc/DP180104026 IEEE fulltext |
| spellingShingle | Song, Yongze Wu, Peng Gilmore, Daniel Li, Qindong A Spatial Heterogeneity-Based Segmentation Model for Analyzing Road Deterioration Network Data in Multi-Scale Infrastructure Systems |
| title | A Spatial Heterogeneity-Based Segmentation Model for Analyzing Road Deterioration Network Data in Multi-Scale Infrastructure Systems |
| title_full | A Spatial Heterogeneity-Based Segmentation Model for Analyzing Road Deterioration Network Data in Multi-Scale Infrastructure Systems |
| title_fullStr | A Spatial Heterogeneity-Based Segmentation Model for Analyzing Road Deterioration Network Data in Multi-Scale Infrastructure Systems |
| title_full_unstemmed | A Spatial Heterogeneity-Based Segmentation Model for Analyzing Road Deterioration Network Data in Multi-Scale Infrastructure Systems |
| title_short | A Spatial Heterogeneity-Based Segmentation Model for Analyzing Road Deterioration Network Data in Multi-Scale Infrastructure Systems |
| title_sort | spatial heterogeneity-based segmentation model for analyzing road deterioration network data in multi-scale infrastructure systems |
| url | http://purl.org/au-research/grants/arc/DP180104026 http://hdl.handle.net/20.500.11937/82713 |