Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 20, Issue 1, Pages 232-243Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2018.2805817
Keywords
Geostatistics; segment-based regression kriging; traffic prediction; road maintenance
Categories
Funding
- Australia Research Council Discovery Early Career Researcher Award [DE170101502]
- Australian Research Council [LP140100873]
- Australian Research Council [LP140100873] Funding Source: Australian Research Council
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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 (R-2 = 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.
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