Newcastle Traffic Classification Using Clustering Algorithms

Hamad B Matar,Talal Almutairi,Nayef Z Al-Mutairi

doi:10.3844/ajeassp.2020.165.172

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.

Highlights

Due to the growth of vehicle ownership and the road traffic demand exceeding the road network capacity in urban area, the traffic congestion causes a longer traveling time, more pollution emissions and higher accident risk
The first group is consisted of 21 traffic counter sites which classified into weekday, Saturday and Sunday clusters
The second group was from ten traffic counter sites which classified into two clusters of weekday and weekend

Summary

Introduction

Due to the growth of vehicle ownership and the road traffic demand exceeding the road network capacity in urban area, the traffic congestion causes a longer traveling time, more pollution emissions and higher accident risk. The morning and evening traffic peak hours caused by traffic to and from workplace or school are recurrent traffic pattern due to repetitive daily traveling activities (Francesc, 2012), There are numerous monitoring systems available to conduct traffic measurement surveys. These are divided into intrusive and non-intrusive sensors. Intrusive sensors such as induction tubes are positioned in or on the road surface to monitor traffic flow (Heidemann et al, 2008)

Methods

Results

Conclusion