Predicting Pedestrian Counts per Street Segment in Urban Environments

Abstract

Cities are continuously growing all over the world and the complexity of designing urban environments increases. Therefore, there is a need to build a better understanding in how our cities work today. One of the essential parts of this is understanding the pedestrian movement. Using pedestrian count data from Amsterdam, London and Stockholm, this thesis explore new variables to further explain pedestrian counts using negative binomial and random forest. The models explored includes variables that represent street centrality, built density, land division, attractions and the road network. The result of the thesis suggests ways for variables to be represented or created to increase the explanatory value in regards to pedestrian counts. These suggestions include: including street centrality measurements at multiple scales, attraction counts within the surrounding area instead of counts on the street segment, counting attractions instead of calculating the distance to the nearest attraction, using network reach to constrain the network at different scales instead of bounding box, and counting intersections in the road network instead of computing the network length.