Clustering Approach to Generate Pedestrian Traffic Pattern Groups

Abstract

This study shows the development of patterns of temporal hourly volume distributions in an urban area in Costa Rica, based on a cluster analysis of pedestrian data. This study aims to establish specific pattern groups for the temporal variation of weekday pedestrian volumes applying cluster analysis in the central business district of Guadalupe in San José. For 46 counting sites, vectors with the weekday hourly factors, the proportion of the daily pedestrian traffic, were estimated. A hierarchical cluster method was implemented to group the vectors of hourly factors from the different counting sites. This method groups elements by minimizing the Euclidean distance between elements of the same group and, at the same time, maximizing the distances from elements of other groups. In addition, the groups found through this analysis are related to land use through buffers of different radios. Eight temporal pattern groups were obtained through cluster analysis. Two pattern groups account for more than two-thirds of the sites included in the study. Fisher’s exact independence test shows that banks and public services could explain some of the patterns observed. The classification of 46 counting sites based on temporal distribution patterns, and the relation with the establishments in the area, allows a simplification of the information and facilitates an understanding of the pedestrian mobility in the area. Further research is required that leads towards geographical elements that could explain the differences in temporal and mobility patterns.