An assessment of models to estimate pedestrian demand based on the level of activity

This paper presents the development and assessment of models to estimate pedestrian demand based on the level of pedestrian activity (high and low). As activity varies by the time of the day, temporal variations were evaluated by considering different time periods. Data collected at 128 low and 48 high pedestrian activity signalized intersections (a total of 176 signalized intersections) in the City of Charlotte, North Carolina were used to develop and assess the models using stepwise regression analysis through backward elimination of independent variables (includes demographic, land use, and network characteristics). The use of different buffer widths (proximal area) to extract these characteristics was also evaluated. Results, in general, show that pedestrian demand varied by the level of activity, explanatory variables extracted by buffer width, and time of the day. The estimates from the models could be used in transportation planning (identify required pedestrian facilities, resource allocation), safety, and operational analyses. Copyright © 2010 John Wiley & Sons, Ltd.