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Managing Traffic Data through Clustering and Radial Basis Functions

Author

Listed:
  • Heber Hernández

    (Nube Minera, La Serena 1700000, Chile)

  • Elisabete Alberdi

    (Department of Applied Mathematics, University of the Basque Country UPV/EHU, 48013 Bilbao, Spain)

  • Heriberto Pérez-Acebo

    (Mechanical Engineering Department, University of the Basque Country UPV/EHU, 48013 Bilbao, Spain)

  • Irantzu Álvarez

    (Department of Graphical Expression and Engineering Projects, University of the Basque Country UPV/EHU, 48013 Bilbao, Spain)

  • María José García

    (Department of Graphical Expression and Engineering Projects, University of the Basque Country UPV/EHU, 48013 Bilbao, Spain)

  • Isabel Eguia

    (Department of Applied Mathematics, University of the Basque Country UPV/EHU, 48013 Bilbao, Spain)

  • Kevin Fernández

    (Department of Applied Mathematics, University of the Basque Country UPV/EHU, 48013 Bilbao, Spain)

Abstract

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.

Suggested Citation

  • Heber Hernández & Elisabete Alberdi & Heriberto Pérez-Acebo & Irantzu Álvarez & María José García & Isabel Eguia & Kevin Fernández, 2021. "Managing Traffic Data through Clustering and Radial Basis Functions," Sustainability, MDPI, vol. 13(5), pages 1-15, March.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:5:p:2846-:d:511658
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    References listed on IDEAS

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    1. Hyun-ho Chang & Seung-hoon Cheon, 2019. "The potential use of big vehicle GPS data for estimations of annual average daily traffic for unmeasured road segments," Transportation, Springer, vol. 46(3), pages 1011-1032, June.
    2. Sfyridis, Alexandros & Agnolucci, Paolo, 2020. "Annual average daily traffic estimation in England and Wales: An application of clustering and regression modelling," Journal of Transport Geography, Elsevier, vol. 83(C).
    3. Zongyuan Sun & Shuo Liu & Dongxue Li & Boming Tang & Shouen Fang, 2020. "Crash analysis of mountainous freeways with high bridge and tunnel ratios using road scenario-based discretization," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-15, August.
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    Cited by:

    1. Heriberto Pérez-Acebo & Robert Ziolkowski & Hernán Gonzalo-Orden, 2021. "Evaluation of the Radar Speed Cameras and Panels Indicating the Vehicles’ Speed as Traffic Calming Measures (TCM) in Short Length Urban Areas Located along Rural Roads," Energies, MDPI, vol. 14(23), pages 1-17, December.

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