This page has only limited features, please log in for full access.
Due to the importance of road transport an adequate identification of the various road network levels is necessary for an efficient and sustainable management of the road infrastructure. Additionally, traffic values are key data for any pavement management system. In this work traffic volume data of 2019 in the Basque Autonomous Community (Spain) were analyzed and modeled. Having a multidimensional sample, the average annual daily traffic (AADT) was considered as the main variable of interest, which is used in many areas of the road network management. First, an exploratory analysis was performed, from which descriptive statistical information was obtained continuing with the clustering by various variables in order to standardize its behavior by translation. In a second stage, the variable of interest was estimated in the entire road network of the studied country using linear-based radial basis functions (RBFs). The estimated model was compared with the sample statistically, evaluating the estimation using cross-validation and highest-traffic sectors are defined. From the analysis, it was observed that the clustering analysis is useful for identifying the real importance of each road segment, as a function of the real traffic volume and not based on other criteria. It was also observed that interpolation methods based on linear-type radial basis functions (RBF) can be used as a preliminary method to estimate the AADT.
Heber Hernández; Elisabete Alberdi; Heriberto Pérez-Acebo; Irantzu Álvarez; María García; Isabel Eguia; Kevin Fernández. Managing Traffic Data through Clustering and Radial Basis Functions. Sustainability 2021, 13, 2846 .
AMA StyleHeber Hernández, Elisabete Alberdi, Heriberto Pérez-Acebo, Irantzu Álvarez, María García, Isabel Eguia, Kevin Fernández. Managing Traffic Data through Clustering and Radial Basis Functions. Sustainability. 2021; 13 (5):2846.
Chicago/Turabian StyleHeber Hernández; Elisabete Alberdi; Heriberto Pérez-Acebo; Irantzu Álvarez; María García; Isabel Eguia; Kevin Fernández. 2021. "Managing Traffic Data through Clustering and Radial Basis Functions." Sustainability 13, no. 5: 2846.
This work presents the results obtained from a spatial modeling and analysis process on pollutants measured in the air through forty-three monitoring stations located in the three provinces of the Basque Autonomous Community (Spain). The pollutants measured correspond to the set of nitrogen oxides (nitric oxide, NO; nitrogen dioxide, NO 2 ; and nitrogen oxides, NO x ) and atmospheric particulate matter with a diameter less than or equal to 10 micrometers (PM 10 ). The objective of this work was to generate a map of the pollutants that exhaustively covers the entire area of the Basque Autonomous Community using geostatistical techniques, in such a way that it serves as a basis for short and midterm environmental studies.
Elisabete Alberdi; Irantzu Alvarez; Heber Hernández; Aitor Oyarbide-Zubillaga; Aitor Goti. Analysis of the Air Quality of the Basque Autonomous Community Using Spatial Interpolation. Sustainability 2020, 12, 4164 .
AMA StyleElisabete Alberdi, Irantzu Alvarez, Heber Hernández, Aitor Oyarbide-Zubillaga, Aitor Goti. Analysis of the Air Quality of the Basque Autonomous Community Using Spatial Interpolation. Sustainability. 2020; 12 (10):4164.
Chicago/Turabian StyleElisabete Alberdi; Irantzu Alvarez; Heber Hernández; Aitor Oyarbide-Zubillaga; Aitor Goti. 2020. "Analysis of the Air Quality of the Basque Autonomous Community Using Spatial Interpolation." Sustainability 12, no. 10: 4164.
In the present study, the influence of the sampling density on the coestimation error of a regionalized, locally stationary and geo-mining nature variable is analyzed. The case study is two-dimensional (2D) and synthetic-type, and it has been generated using a non-conditional Sequential Gaussian Simulation (SGS), with subsequent transformation to Gaussian distribution, seeking to emulate the structural behavior of the aforementioned variable. A primary and an auxiliary variable with different spatial and statistical properties are constructed using the same methodology. The collocated ordinary cokriging method has been applied, in which the auxiliary variable is spatially correlated with the primary one and it is known exhaustively. Fifteen sampling densities are extracted from the target population of the primary variable, which are compared with the simulated values after performing coestimation. The obtained results follow a potential function that indicates the mean global error (MGE) based on the sampling density percentage (SDP) ( M G E = 1.2366 · S D P − 0.224 ).
Heber Hernandez Guerra; Elisabete Alberdi; Aitor Goti. Influence of the Sampling Density in the Coestimation Error of a Regionalized Locally Stationary Variable. Minerals 2020, 10, 90 .
AMA StyleHeber Hernandez Guerra, Elisabete Alberdi, Aitor Goti. Influence of the Sampling Density in the Coestimation Error of a Regionalized Locally Stationary Variable. Minerals. 2020; 10 (2):90.
Chicago/Turabian StyleHeber Hernandez Guerra; Elisabete Alberdi; Aitor Goti. 2020. "Influence of the Sampling Density in the Coestimation Error of a Regionalized Locally Stationary Variable." Minerals 10, no. 2: 90.