Segment-Based Spatial Analysis for Assessing Road Infrastructure Performance Using Monitoring Observations and Remote Sensing Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Road Condition Data
2.2. Explanatory Variables
2.3. Methods
2.3.1. Segment-Based Factor Detector Model
2.3.2. Optimal Discretization for Segment-Level Variables
2.3.3. Segment-Based Interaction Detector and Risk Detector Models
3. Results
3.1. Optimal Discretization
3.2. Segment-Based Factor Detector
3.3. Segment-Based Interaction Detector
3.4. Segment-Based Risk Detector
4. Discussion
4.1. The Segment-Based Spatial Stratified Heterogeneity Analysis
4.2. Comprehensive Impacts of Climate and Heavy Vehicles
4.3. Practical Recommendations
4.4. Recommendations for Future Research
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Category | No. | Code | Name (Unit) | Data Source |
---|---|---|---|---|
Vehicles and heavy vehicles (C1) | 1 | vlmsum | Total volume of vehicles | Main Roads Western Australia [52] and calculating using segment-based regression kriging methods [53]. |
2 | pcthvvlm | Percentage of heavy vehicle volume (%) | ||
3 | masssum | Total mass of vehicles (t/(km·day) | ||
4 | pcthvmass | Percentage of heavy vehicle mass (%) | ||
Climate and environment (C2) | 5 | ap | Annual precipitation (mm) | Bureau of Meteorology, Australia [54,55]. |
6 | maxtemp | Annual maximum temperature (°C) | Land surface temperature (LST) MOD11A2 from Moderate Resolution Imaging Spectroradiometer (MODIS) [56]. | |
7 | mintemp | Annual minimum temperature (°C) | ||
8 | meantemp | Annual mean temperature (°C) | ||
9 | tempdif | Annual temperature difference (°C) | ||
10 | soiltype 1 | Soil type | 2016 State of the Environment (SoE) Land Australian Soil Classification Orders dataset [57,58]. | |
11 | rzsm | Root-zone soil moisture (%) | Bureau of Meteorology, Australia, and the Australian Soil Resources Information System (ASRIS) dataset [54,59]. | |
12 | usm | Upper soil moisture (%) | ||
13 | lsm | Lower soil moisture (%) | ||
14 | dsm | Deep soil moisture (%) | ||
15 | dd | Deep drainage (mm) | ||
16 | ae | Actual evapotranspiration (mm) | ||
17 | evi | Enhanced vegetation index (EVI) | Enhanced vegetation index (EVI) MOD13A2 from Moderate Resolution Imaging Spectroradiometer (MODIS) [60]. | |
Road characteristics (C3) | 18 | ravnw 1 | Restricted access vehicles (RAV) network | Main Roads Western Australia. |
19 | speed 1 | Road speed limit (km/h) | ||
20 | surftype 1 | Road surfacing type | ||
Socioeconomic factors (C4) | 21 | popbf1 | Population within 1 km | Population data with 1-km spatial resolution is from Gridded Population of the World fourth version (GPWv4) [61]. |
22 | popbf5 | Population within 5 km | ||
23 | popbf10 | Population within 10 km | ||
24 | wpop | Weighted population within 50 km |
No. | Code | Min | Max | Method | Number of Intervals |
---|---|---|---|---|---|
1 | vlmsum | 100 | 5105 | standard deviation (SD) | 7 |
2 | pcthvvlm | 6.1 | 56.4 | quantile | 6 |
3 | masssum | 1780.1 | 138,391.3 | equal | 6 |
4 | pcthvmass | 67.5 | 98.0 | quantile | 7 |
5 | ap | 237.6 | 761.8 | geometric | 7 |
6 | maxtemp | 20.7 | 33.9 | SD | 6 |
7 | mintemp | 9.8 | 15.4 | natural | 6 |
8 | meantemp | 16.4 | 24.0 | geometric | 4 |
9 | tempdif | 8.6 | 20.6 | SD | 6 |
10 | soiltype | Categorical variable, including Calcarosol, Chromosol, Hydrosol, Kandosol, Podosol, Rudosol, Sodosol and Tenosol. | 8 | ||
11 | rzsm | 4.64 | 33.11 | equal | 6 |
12 | usm | 1.96 | 10.43 | natural | 7 |
13 | lsm | 4.12 | 37.33 | equal | 6 |
14 | dsm | 9.84 | 54.31 | equal | 5 |
15 | dd | 0.5 | 179.3 | natural | 7 |
16 | ae | 239.3 | 807.4 | equal | 5 |
17 | evi | 0.04 | 0.38 | SD | 6 |
18 | ravnw | Categorical variable, including 3, 4, 5, 6, 7 and 10. | 6 | ||
19 | speed | Categorical variable, including 50, 60, 70, 80, 90, 100 and 110 (km/h). | 7 | ||
20 | surftype | Categorical variable, including single seal, two coat seal, slurry seal, primer seal, asphalt dense graded, asphalt intersection mix, rubberized seal and asphalt open graded. | 8 | ||
21 | popbf1 | 1 | 2490 | geometric | 7 |
22 | popbf5 | 13 | 6946 | geometric | 7 |
23 | popbf10 | 49 | 12,422 | quantile | 6 |
24 | wpop | 8 | 2787 | geometric | 7 |
Category | C1 1 | C2 2 | |||||
---|---|---|---|---|---|---|---|
Variable | vlmsum | pcthvvlm | masssum | pcthvmass | ap | dd | |
C1 | vlmsum | ||||||
pcthvvlm | 0.543 *,3 | ||||||
masssum | 0.451 | ||||||
pcthvmass | 0.457 | 0.532 | |||||
C2 | ap | 0.535 | 0.505 | ||||
soiltype | 0.481 | 0.464 | 0.566 * | ||||
dd | 0.496 | 0.544 | 0.525 | ||||
ae | 0.516 | 0.435 | |||||
C4 4 | popbf1 | 0.407 * | |||||
popbf5 | 0.393 | ||||||
wpop | 0.385 | 0.384 | 0.453 * | 0.423 |
Level of Risk | Description |
---|---|
Very high risk | Road segments within the sub-region of the highest risk of pavement deflections. |
High risk | Road segments within the sub-region of the second highest risk of pavement deflections. |
Medium risk | Road segments within the sub-regions of median risks of pavement deflections, and outside of other levels of risks. |
Low risk | Road segments within the sub-region of the second lowest risk of pavement deflections. |
Very low risk | Road segments within the sub-region of the lowest risk of pavement deflections. |
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Song, Y.; Wright, G.; Wu, P.; Thatcher, D.; McHugh, T.; Li, Q.; Li, S.J.; Wang, X. Segment-Based Spatial Analysis for Assessing Road Infrastructure Performance Using Monitoring Observations and Remote Sensing Data. Remote Sens. 2018, 10, 1696. https://doi.org/10.3390/rs10111696
Song Y, Wright G, Wu P, Thatcher D, McHugh T, Li Q, Li SJ, Wang X. Segment-Based Spatial Analysis for Assessing Road Infrastructure Performance Using Monitoring Observations and Remote Sensing Data. Remote Sensing. 2018; 10(11):1696. https://doi.org/10.3390/rs10111696
Chicago/Turabian StyleSong, Yongze, Graeme Wright, Peng Wu, Dominique Thatcher, Tom McHugh, Qindong Li, Shuk Jin Li, and Xiangyu Wang. 2018. "Segment-Based Spatial Analysis for Assessing Road Infrastructure Performance Using Monitoring Observations and Remote Sensing Data" Remote Sensing 10, no. 11: 1696. https://doi.org/10.3390/rs10111696