A Spatial-statistical model to analyse historical rutting data

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Jourdain, Natoya O. A. S.; Steinsland, Ingelin; Vedvik, Emil; Birkhez-Shami, Mamoona; Olsen, William; Gryteselv, Dagfin; Siebert, Doreen; Klein-Paste, Alex, 2024, "A Spatial-statistical model to analyse historical rutting data", https://doi.org/10.18710/WD05DG, DataverseNO, V1

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Description	The rutting dataset comprises of the annual rutting for years 2010-2020 (millimetre, calculated as the difference between current and previous year's data), with rut depth measurement from the previous year (millimetre), annual average daily traffic (AADT), lane width (metre), bearing capacity for year 2021 (tonnes), surface curvature index for year 2021, and base curvature index data (2021). The rutting data was collected for 20-metre road segments at specific latitude and longitude locations. The rutting is assumed to be linearly related to known explanatory variables (e.g., lane width) and random and spatial components. Rutting measurements were used to fit spatial-statistical models with random and spatial components in a Bayesian Hierarchical framework. Non spatial-statistical models with random yearly effects were also fitted. We compared these models to determine the importance of accounting for spatial information and to properly account for the rutting variability. (2024-02-14)
Subject	Engineering; Mathematical Sciences
Keyword	Flexible pavement, Predictive maintenance, Rutting, Spatial-statistical model, Uncertainty, Bayesian Hierarchical models, Linear mixed models with INLA, Spatial and random effects
Related Publication	Jourdain N.O.A.S et al (2024) A Spatial-statistical model to analyse historical rutting data. Preprint submitted, and it will be submitted for review
License/Data Use Agreement	CC0 1.0

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	00_ReadMe.txt Plain Text - 11.1 KB Published Feb 22, 2024 16 Downloads MD5: 2fa22d1aecd1df02d832f2c0c046a17a Description of data	Preview "00_ReadMe.txt" Access File File Access Public Download Options Plain Text Download Metadata Data File Citation EndNote XML RIS BibTeX
	01_2024_Figures.zip ZIP Archive - 13.2 MB Published Feb 22, 2024 11 Downloads MD5: cd572537d71f1d1a1bbf134d164c959f All figures and plot used in preprint	Preview "01_2024_Figures.zip" Access File File Access Public Download Options ZIP Archive Download Metadata Data File Citation EndNote XML RIS BibTeX
	02_2024_Dataset.geojson GeoJSON - 993.2 MB Published Feb 22, 2024 15 Downloads MD5: a1d5e6768a5829f877f2eec6a4e0b532 Annual rutting data, with known explanatory variables, and geographical location	Preview "02_2024_Dataset.geojson" Access File File Access Public Download Options GeoJSON Download Metadata Data File Citation EndNote XML RIS BibTeX
	03_A_spatial_statistical_model_1_Jourdain_New_update.R R Syntax - 62.4 KB Published Feb 22, 2024 11 Downloads MD5: 6c3a71cb4468a916980dc955544fee34 Spatial statistical rutting model with all explanatory variables (R codes)	Preview "03_A_spatial_statistical_model_1_Jourdain_New_update.R" Access File File Access Public Download Options R Syntax Download Metadata Data File Citation EndNote XML RIS BibTeX
	04_A_spatial_statistical_model_2_Jourdain_New_update.R R Syntax - 61.0 KB Published Feb 22, 2024 11 Downloads MD5: 01909e473400fd3e58216975c7c13a75 Spatial statistical rutting model with up to two explanatory variables (R codes)	Preview "04_A_spatial_statistical_model_2_Jourdain_New_update.R" Access File File Access Public Download Options R Syntax Download Metadata Data File Citation EndNote XML RIS BibTeX
	05_A_spatial_statistical_model_3_Jourdain_New_update.R R Syntax - 61.0 KB Published Feb 22, 2024 10 Downloads MD5: 89a9c9795ba60cbb0b369d81e9a94e43 Spatial statistical rutting model with up to one explanatory variables (R codes)	Preview "05_A_spatial_statistical_model_3_Jourdain_New_update.R" Access File File Access Public Download Options R Syntax Download Metadata Data File Citation EndNote XML RIS BibTeX
	06_A_NON_spatial_statistical_model_4_Jourdain_New_update.R R Syntax - 17.5 KB Published Feb 22, 2024 13 Downloads MD5: 9d33c3149461ca6bdb3a41d6e6b1c89e Non spatial statistical rutting model with all explanatory variables (R codes)	Preview "06_A_NON_spatial_statistical_model_4_Jourdain_New_update.R" Access File File Access Public Download Options R Syntax Download Metadata Data File Citation EndNote XML RIS BibTeX
	07_A_NON_spatial_statistical_model_5_Jourdain_New_update.R R Syntax - 17.5 KB Published Feb 22, 2024 11 Downloads MD5: 09093f5b1a2160cd30ca7c060f2e5850 Non spatial statistical rutting model with up to two explanatory variables (R codes)	Preview "07_A_NON_spatial_statistical_model_5_Jourdain_New_update.R" Access File File Access Public Download Options R Syntax Download Metadata Data File Citation EndNote XML RIS BibTeX
	08_A_NON_spatial_statistical_model_6_Jourdain_New_update.R R Syntax - 16.7 KB Published Feb 22, 2024 10 Downloads MD5: f2effb1a48b8305d0b8e6cdb8df6a54d Non spatial statistical rutting model with up to one explanatory variables (R codes)	Preview "08_A_NON_spatial_statistical_model_6_Jourdain_New_update.R" Access File File Access Public Download Options R Syntax Download Metadata Data File Citation EndNote XML RIS BibTeX
	09_Results_from_fitting_all_models.R R Syntax - 232.8 KB Published Feb 22, 2024 12 Downloads MD5: 31065f0a4aad2a3e9cc521c9a44f3196 Results from fitting spatial and non spatial statistical models (R codes)	Preview "09_Results_from_fitting_all_models.R" Access File File Access Public Download Options R Syntax Download Metadata Data File Citation EndNote XML RIS BibTeX

Citation Metadata

Persistent Identifier	doi:10.18710/WD05DG
Publication Date	2024-02-22
Title	A Spatial-statistical model to analyse historical rutting data
Author	Jourdain, Natoya O. A. S.; Steinsland, Ingelin; Vedvik, Emil; Birkhez-Shami, Mamoona; Olsen, William; Gryteselv, Dagfin; Siebert, Doreen; Klein-Paste, Alex (NTNU – Norwegian University of Science and Technology) - ORCID: https://orcid.org/0000-0001-6265-4438
Point of Contact	Use email button above to contact. Jourdain, Natoya (NTNU – Norwegian University of Science and Technology)
Description	The rutting dataset comprises of the annual rutting for years 2010-2020 (millimetre, calculated as the difference between current and previous year's data), with rut depth measurement from the previous year (millimetre), annual average daily traffic (AADT), lane width (metre), bearing capacity for year 2021 (tonnes), surface curvature index for year 2021, and base curvature index data (2021). The rutting data was collected for 20-metre road segments at specific latitude and longitude locations. The rutting is assumed to be linearly related to known explanatory variables (e.g., lane width) and random and spatial components. Rutting measurements were used to fit spatial-statistical models with random and spatial components in a Bayesian Hierarchical framework. Non spatial-statistical models with random yearly effects were also fitted. We compared these models to determine the importance of accounting for spatial information and to properly account for the rutting variability. (2024-02-14)
Subject	Engineering; Mathematical Sciences
Keyword	Flexible pavement Predictive maintenance Rutting Spatial-statistical model Uncertainty Bayesian Hierarchical models Linear mixed models with INLA Spatial and random effects
Related Publication	Jourdain N.O.A.S et al (2024) A Spatial-statistical model to analyse historical rutting data. Preprint submitted, and it will be submitted for review arXiv: https://doi.org/10.48550/arXiv.2401.03633
Producer	NTNU – Norwegian University of Science and Technology (NTNU) https://www.ntnu.edu/
Production Date	2020-05-21
Production Location	Trøndelag
Contributor	Project Leader : Andersen, Trond Michael Data Manager : Gryteselv, Dagfin Project Manager : Klein-Paste, Alex Project Manager : Siebert, Doreen Supervisor : Ingelin Steinsland Researcher : Jourdain, Natoya O.A.S
Funding Information	Statens Vegvesen: https://www.vegvesen.no/ Norwegian University of Science and Technology: https://www.ntnu.no/
Distributor	NTNU – Norwegian University of Science and Technology (NTNU) https://dataverse.no/dataverse/ntnu
Depositor	Jourdain, Natoya Olita Amantha Stephen
Deposit Date	2024-02-14
Time Period	Start Date: 2010-05-21 ; End Date: 2020-05-21
Date of Collection	Start Date: 2010-05-21 ; End Date: 2020-05-21
Data Type	Survey data
Software	R studio, Version: Version: 4.3.2 INLA, Version: Version: 29-01-2024
Data Source	Norwegian Public Roads Administration data bank: NVDB; Statens Vegvesen Vegkart and Rosita data base.

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Geographic Coverage	Norway, Trøndelag, Trondheim to Storlien on the Swedish border

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