Traffic Volume Prediction With Segment-Based Regression Kriging and its Implementation in Assessing the Impact of Heavy Vehicles

Song, Yongze; Wang, Xiangyu; Wright, Graeme; Thatcher, Dominique; Wu, Peng; Felix, Pascal

doi:10.1109/tits.2018.2805817

Cited by 53 publications

(27 citation statements)

References 38 publications

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“…Next, more attention should be paid to the impacts of climate and surrounding environmental conditions on pavement performance. In general road management and pavement engineering practices, traffic volumes, especially the masses of heavy vehicles are regarded as the primary source of pavement damage [53]. In this study, the spatial segment-based stratified heterogeneity analysis indicates that the contributions of climate and environmental factors are as high as the impacts of vehicles, and they have nonlinearly enhanced interactive impacts on the pavement.…”

Section: Practical Recommendationsmentioning

confidence: 85%

“…In general, the weight of a standard light vehicle is merely 1.65 t and the gross vehicle mass (GVM) is limited to 4.5 t in Australia [62], but the mass of heavy vehicles used for freight transportation ranges from 42.5 t to 147.5 t [63]. Thus, the total traffic volumes and masses, and the respective percentages of heavy vehicles are calculated using segment-based regression kriging (SRK) methods [53] with traffic statistical data provided by Main Roads, WA [52]. SRK is a spatial prediction model for line segment-based spatial data, such as road properties and traffic conditions, by involving the spatial characteristics and spatial homogeneity of line segments [53].…”

Section: Explanatory Variablesmentioning

confidence: 99%

“…Thus, the total traffic volumes and masses, and the respective percentages of heavy vehicles are calculated using segment-based regression kriging (SRK) methods [53] with traffic statistical data provided by Main Roads, WA [52]. SRK is a spatial prediction model for line segment-based spatial data, such as road properties and traffic conditions, by involving the spatial characteristics and spatial homogeneity of line segments [53]. Compared with raw data released by Main Roads, WA, and the data predicted using traditional aspatial and point-based spatial methods, SRK provides more accurate prediction results with significantly reduced errors and autocorrelations of residuals.…”

Section: Explanatory Variablesmentioning

confidence: 99%

“…In previous studies, only a few segment-based spatial analysis methods were used for processing segment-based spatial data. Road and traffic data, such as pavement deflections, are typical segment-based observations, which are spatially homogeneous within a segment, but spatially heterogeneous on different segments across the road network [53]. In this study, optimal discretization for segment-based pavement data and geographical detector are integrated in the segment-based spatial stratified heterogeneity analysis of exploring the impacts of potential variables on pavement infrastructure performance.…”

Section: The Segment-based Spatial Stratified Heterogeneity Analysismentioning

confidence: 99%

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

Song

Wright

et al. 2018

Remote Sensing

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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.

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Section: Practical Recommendationsmentioning

confidence: 85%

Section: Explanatory Variablesmentioning

confidence: 99%

Section: Explanatory Variablesmentioning

confidence: 99%

Section: The Segment-based Spatial Stratified Heterogeneity Analysismentioning

confidence: 99%

See 2 more Smart Citations

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

Song

Wright

et al. 2018

Remote Sensing

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“…With the ever-increasing pace of urbanization in China, limited financial resources of the government have been unable to keep up [1][2][3][4]. To relieve infrastructure funding pressure and raise the funds rate, an increasing number of local governments in China began to encourage the private sector to participate in public investment projects.…”

Section: Introductionmentioning

confidence: 99%

Multi-Criterion Two-Sided Matching of Public–Private Partnership Infrastructure Projects: Criteria and Methods

Liang

Sheng

et al. 2018

Sustainability

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Two kinds of evaluative criteria are associated with Public-Private Partnership (PPP) infrastructure projects, i.e., private evaluative criteria and public evaluative criteria. These evaluative criteria are inversely related, that is, the higher the public benefits; the lower the private surplus. To balance evaluative criteria in the Two-Sided Matching (TSM) decision, this paper develops a quantitative matching decision model to select an optimal matching scheme for PPP infrastructure projects based on the Hesitant Fuzzy Set (HFS) under unknown evaluative criterion weights. In the model, HFS is introduced to describe values of the evaluative criteria and multi-criterion information is fully considered given by groups. The optimal model is built and solved by maximizing the whole deviation of each criterion so that the evaluative criterion weights are determined objectively. Then, the match-degree of the two sides is calculated and a multi-objective optimization model is introduced to select an optimal matching scheme via a min-max approach. The results provide new insights and implications of the influence on evaluative criteria in the TSM decision.

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Performance prediction of magnetorheological fluid‐based liquid gating membrane by kriging machine learning method

Zhang

Yuan

Jian

et al. 2022

Interdisciplinary Materials

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Smart liquid gating membrane is a responsive structural material as a pressure-driven system that consists of solid membrane and dynamic liquid, responding to the external field. An accurate prediction of rheological and mechanical properties is important for the designs of liquid gating membranes for various applications. However, high predicted accuracy by the traditional sequential method requires a large amount of experimental data, which is not practical in some situations. To conquer these problems, artificial intelligence has promoted the rapid development of material science in recent years, bringing hope to solve these challenges. Here we propose a Kriging machine learning model with an active candidate region, which can be smartly updated by an expected improvement probability method to increase the local accuracy near the most sensitive search region, to predict the mechanical and rheological performance of liquid gating system with an active minimal size of experimental data. Besides this, this new machine learning model can instruct our experiments with optimal size. The methods are then verified by liquid gating membrane with magnetorheological fluids, which would be of wide

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Traffic Volume Prediction With Segment-Based Regression Kriging and its Implementation in Assessing the Impact of Heavy Vehicles

Cited by 53 publications

References 38 publications

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

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

Multi-Criterion Two-Sided Matching of Public–Private Partnership Infrastructure Projects: Criteria and Methods

Performance prediction of magnetorheological fluid‐based liquid gating membrane by kriging machine learning method

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