Managing Traffic Data through Clustering and Radial Basis Functions

Due to the importance of road transport an adequate identification various network levels is necessary for efficient and sustainable management infrastructure. Additionally, traffic values are key data any pavement system. In this work volume 2019 in Basque Autonomous Community (Spain) were analyzed modeled. Having a multidimensional sample, average annual daily (AADT) was considered as main variable interest, which used many areas management. First, exploratory analysis performed, from descriptive statistical information obtained continuing with clustering by variables order standardize its behavior translation. second stage, interest estimated entire studied country using linear-based radial basis functions (RBFs). The model compared sample statistically, evaluating estimation cross-validation highest-traffic sectors defined. From analysis, it observed that useful identifying real each segment, function not based on other criteria. It also interpolation methods linear-type (RBF) can be preliminary method estimate AADT.