TY  - JOUR
AU  - Matar, Hamad B 
AU  - Almutairi, Talal 
AU  - Al-Mutairi, Nayef Z 
PY  - 2020
TI  - Newcastle Traffic Classification Using Clustering Algorithms
JF  - American Journal of Engineering and Applied Sciences
VL  - 13
IS  - 2
DO  - 10.3844/ajeassp.2020.165.172
UR  - https://thescipub.com/abstract/ajeassp.2020.165.172
AB  - 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.