Estimating Pedestrian Counts in Urban Areas for Transportation Planning and Safety Analyses

This paper investigates factors that can be used to quantify pedestrian counts in urban areas. The best subset regression is used to develop models to estimate pedestrian counts considering variables identified using general linear regression. The F-test is used to support the analysis. The models are developed using data collected at 15 selected locations with high pedestrian activity in the Las Vegas metropolitan area. The findings show that the pedestrian counts are a function of number of lanes, average annual household income and residential area proximate to the study location. Results show that the pedestrian counts are independent of the commercial area and the number of bus stops in the vicinity of the location. The developed models can be used to estimate pedestrian counts at any high pedestrian activity location provided the socioeconomic and demographic characteristics are known. The methodology is also applicable to other urban settings.