A Spatial-statistical model to analyse historical rutting datadoi:10.18710/WD05DGDataverseNO2024-02-221Jourdain, 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, V1A Spatial-statistical model to analyse historical rutting datadoi:10.18710/WD05DGJourdain, Natoya O. A. S.; Steinsland, Ingelin; Vedvik, Emil; Birkhez-Shami, Mamoona; Olsen, William; Gryteselv, Dagfin; Siebert, Doreen; Klein-Paste, AlexAndersen, Trond MichaelGryteselv, DagfinKlein-Paste, AlexSiebert, DoreenIngelin SteinslandJourdain, Natoya O.A.SNTNU – Norwegian University of Science and Technology2020-05-21TrøndelagR studioINLAhttps://www.vegvesen.no/https://www.ntnu.no/DataverseNONTNU – Norwegian University of Science and TechnologyJourdain, NatoyaJourdain, Natoya Olita Amantha Stephen2024-02-14EngineeringMathematical SciencesFlexible pavementPredictive maintenanceRuttingSpatial-statistical modelUncertaintyBayesian Hierarchical modelsLinear mixed models with INLASpatial and random effectsThe 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.2010-05-212020-05-212010-05-212020-05-21NorwayTrøndelagTrondheim to Storlien on the Swedish borderSurvey dataNorwegian Public Roads Administration data bank: NVDB; Statens Vegvesen Vegkart and Rosita data base.<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>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 reviewJourdain N.O.A.S et al (2024) A Spatial-statistical model to analyse historical rutting data. Preprint submitted, and it will be submitted for review00_ReadMe.txtDescription of datatext/plain01_2024_Figures.zipAll figures and plot used in preprintapplication/zip02_2024_Dataset.geojsonAnnual rutting data, with known explanatory variables, and geographical locationapplication/geo+json03_A_spatial_statistical_model_1_Jourdain_New_update.RSpatial statistical rutting model with all explanatory variables (R codes)type/x-r-syntax04_A_spatial_statistical_model_2_Jourdain_New_update.RSpatial statistical rutting model with up to two explanatory variables (R codes)type/x-r-syntax05_A_spatial_statistical_model_3_Jourdain_New_update.RSpatial statistical rutting model with up to one explanatory variables (R codes)type/x-r-syntax06_A_NON_spatial_statistical_model_4_Jourdain_New_update.RNon spatial statistical rutting model with all explanatory variables (R codes)type/x-r-syntax07_A_NON_spatial_statistical_model_5_Jourdain_New_update.RNon spatial statistical rutting model with up to two explanatory variables (R codes)type/x-r-syntax08_A_NON_spatial_statistical_model_6_Jourdain_New_update.RNon spatial statistical rutting model with up to one explanatory variables (R codes)type/x-r-syntax09_Results_from_fitting_all_models.RResults from fitting spatial and non spatial statistical models (R codes)type/x-r-syntax10_inlaResults_EV14_model_1.RDataResults from fitting the spatial model 1application/x-rlang-transport11_inlaResults_EV14_model_2.RDataResults from fitting the spatial model 2application/x-rlang-transport12_inlaResults_EV14_model_3.RDataResults from fitting the spatial model 3application/x-rlang-transport13_inlaResults_EV14_ns.model_4.RDataResults from fitting the non spatial model 4application/x-rlang-transport14_inlaResults_EV14_ns.model_5.RDataResults from fitting the non spatial model 5application/x-rlang-transport15_inlaResults_EV14_ns.model_6.RDataResults from fitting the non spatial model 6application/x-rlang-transport