UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

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

Sfyridis, A; Agnolucci, P; (2020) Annual average daily traffic estimation in England and Wales: An application of clustering and regression modelling. Journal of Transport Geography , 83 , Article 102658. 10.1016/j.jtrangeo.2020.102658. Green open access

[img]
Preview
Text
1-s2.0-S096669231930568X-main(1).pdf - Published version

Download (2MB) | Preview

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.

Type: Article
Title: Annual average daily traffic estimation in England and Wales: An application of clustering and regression modelling
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.jtrangeo.2020.102658
Publisher version: https://doi.org/10.1016/j.jtrangeo.2020.102658
Language: English
Additional information: This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: Annual Average Daily Traffic (AADT), ClusteringK-prototypes, Support Vector Regression (SVR), Random ForestGIS
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources
URI: https://discovery.ucl.ac.uk/id/eprint/10093070
Downloads since deposit
14Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item