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Annual average daily traffic estimation in England and Wales: An application of clustering and regression modelling

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  • Sfyridis, Alexandros
  • Agnolucci, Paolo

Abstract

Collection of Annual Average Daily Traffic (AADT) is of major importance for a number of applications in road transport urban and environmental studies. However, traffic measurements are undertaken only for a part of the road network with minor roads usually excluded. This paper suggests a methodology to estimate AADT in England and Wales applicable across the full road network, so that traffic for both major and minor roads can be approximated. This is achieved by consolidating clustering and regression modelling and using a comprehensive set of variables related to roadway, socioeconomic and land use characteristics. The methodological output reveals traffic patterns across urban and rural areas as well as produces accurate results for all road classes. Support Vector Regression (SVR) and Random Forest (RF) are found to outperform the traditional Linear Regression, although the findings suggest that data clustering is key for significant reduction in prediction errors.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:jotrge:v:83:y:2020:i:c:s096669231930568x
    DOI: 10.1016/j.jtrangeo.2020.102658
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    References listed on IDEAS

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    2. Klapka, Pavel & Kraft, Stanislav & Halás, Marián, 2020. "Network based definition of functional regions: A graph theory approach for spatial distribution of traffic flows," Journal of Transport Geography, Elsevier, vol. 88(C).
    3. Shuang Yin & Guojun Gan & Emiliano A. Valdez & Jeyaraj Vadiveloo, 2021. "Applications of Clustering with Mixed Type Data in Life Insurance," Risks, MDPI, vol. 9(3), pages 1-19, March.

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