Traffic Volume Prediction With Segment-Based Regression Kriging and its Implementation in Assessing the Impact of Heavy Vehicles
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 de...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Published: |
IEEE Intelligent Transportation Systems Society
2018
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| Online Access: | http://purl.org/au-research/grants/arc/DE170101502 http://hdl.handle.net/20.500.11937/67275 |
| _version_ | 1848761523890028544 |
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| author | Song, Y. Wang, Xiangyu Wright, G. Thatcher, D. Wu, Peng Felix, P. |
| author_facet | Song, Y. Wang, Xiangyu Wright, G. Thatcher, D. Wu, Peng Felix, P. |
| author_sort | Song, Y. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T10:33:02Z |
| format | Journal Article |
| id | curtin-20.500.11937-67275 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:33:02Z |
| publishDate | 2018 |
| publisher | IEEE Intelligent Transportation Systems Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-672752022-10-06T04:33:19Z Traffic Volume Prediction With Segment-Based Regression Kriging and its Implementation in Assessing the Impact of Heavy Vehicles Song, Y. Wang, Xiangyu Wright, G. Thatcher, D. Wu, Peng Felix, P. 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. 2018 Journal Article http://hdl.handle.net/20.500.11937/67275 10.1109/TITS.2018.2805817 http://purl.org/au-research/grants/arc/DE170101502 http://purl.org/au-research/grants/arc/LP140100873 IEEE Intelligent Transportation Systems Society restricted |
| spellingShingle | Song, Y. Wang, Xiangyu Wright, G. Thatcher, D. Wu, Peng Felix, P. Traffic Volume Prediction With Segment-Based Regression Kriging and its Implementation in Assessing the Impact of Heavy Vehicles |
| title | Traffic Volume Prediction With Segment-Based Regression Kriging and its Implementation in Assessing the Impact of Heavy Vehicles |
| title_full | Traffic Volume Prediction With Segment-Based Regression Kriging and its Implementation in Assessing the Impact of Heavy Vehicles |
| title_fullStr | Traffic Volume Prediction With Segment-Based Regression Kriging and its Implementation in Assessing the Impact of Heavy Vehicles |
| title_full_unstemmed | Traffic Volume Prediction With Segment-Based Regression Kriging and its Implementation in Assessing the Impact of Heavy Vehicles |
| title_short | Traffic Volume Prediction With Segment-Based Regression Kriging and its Implementation in Assessing the Impact of Heavy Vehicles |
| title_sort | traffic volume prediction with segment-based regression kriging and its implementation in assessing the impact of heavy vehicles |
| url | http://purl.org/au-research/grants/arc/DE170101502 http://purl.org/au-research/grants/arc/DE170101502 http://hdl.handle.net/20.500.11937/67275 |