Sign inSubmit
ArticleMetrics
Research Article · Open access · Published 4 April 2020

Newcastle Traffic Classification Using Clustering Algorithms

Hamad B Matar1, Talal Almutairi1 & Nayef Z Al-Mutairi2

American Journal of Engineering and Applied Sciences, Volume 13, Issue 2, pp. 165–172 · doi.org/10.3844/ajeassp.2020.165.172

7,214 accesses4,008 downloads1 citations (Crossref)Metrics
Cite Check for updates

Abstract

The urban road traffic network evolution is complex and varies depend on road type, zoning types and social activities. Typical traffic pattern variation of road network could be examined by considering the daily human travel activities. Thus, factor and cluster analysis is carried out. This paper is a comparative analysis of various Data Mining clustering methods for the grouping of roads based on traffic profile. The analysis was carried out using data available from 45 Automatic Traffic Recorder (ATR) sites in Newcastle, UK. Factor and cluster analysis were applied on the road traffic data so that roads could be classified, allowing diurnal traffic profiles to be assigned a group to roads with similar attributes. These groups could be classify based on road traffic characteristics. Five road classifications were found.

Licence and reuse

Open access. This article is licensed under a Creative Commons Attribution 3.0 Licence (CC BY 3.0), as printed on the article, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original authors and the source. No permission is needed.

How to reuse this article

Responses (0)

Have a comment on this research? Sign in to respond. Responses are moderated and published with your name and affiliation; the authors are invited to reply. How we use your dataWrite a response

About this article

Cite this article

Matar, H. B., Almutairi, T. & Al-Mutairi, N. Z. (2020). Newcastle Traffic Classification Using Clustering Algorithms. American Journal of Engineering and Applied Sciences, 13(2), 165–172. https://doi.org/10.3844/ajeassp.2020.165.172

Licence
CC BY 3.0
Issue
Volume 13, Issue 2
Policies
Editorial independenceUse of generative AIData availabilityAppeals and complaints
Version
Version of record
Newcastle Traffic Classification Using Clustering AlgorithmsAmerican Journal of Engineering and Applied Sciences · Volume 13, Issue 2 · pp. 165–172 · CC BY 3.0Download