 Annual average daily traffic estimation in England and Wales: An application of clustering and regression modellingAlexandros Sfyridis and Paolo Agnolucci Journal of Transport Geography, 2020, vol. 83, issue C 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. Keywords: Annual Average Daily Traffic (AADT); Clustering; K-prototypes; Support Vector Regression (SVR); Random Forest; GIS (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jotrge:v:83:y:2020:i:c:s096669231930568x DOI: 10.1016/j.jtrangeo.2020.102658 Access Statistics for this article Journal of Transport Geography is currently edited by Frank Witlox More articles in Journal of Transport Geography from Elsevier |
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