Accessibility and Centrality Based Estimation of Pedestrian Activity and Safety in Urban Areas

Abstract

The following thesis investigates the feasibility of using metrics of accessibility to jobs, betweenness centrality, and automobile traffic levels to estimate pedestrian behav- ior levels and automobile-pedestrian collision risks within an urban area. Multimodal count and crash report data from Minneapolis, Minnesota are used as a test of this scal- able, translatable modeling framework; multiple stepwise linear regression is performed to compile a set of explanatory variables from which to construct a predictive model of pedestrian movement. The existence of the Safety In Numbers (SIN) phenomenon is in- vestigated within both the raw and estimated pedestrian movement data; the SIN effect is the phenomenon where pedestrians are found to be safer from collisions, on average, when there are more pedestrians present in a given intersection, street, or area - that is, that the per-pedestrian risk of injury inflicted by drivers of automobiles decreases as a function of the increasing volume of pedestrian traffic. Economic accessibility, between- ness centrality, and Average Annual Daily Traffic (AADT) were found to be significant predictors of pedestrian traffic at intersections in Minneapolis, and the SIN effect was observed in both the raw and estimated pedestrian movement data when combined with the aggregated crash data. This investigation shows the potential utility of such a model that is both scalable to larger geographic areas, and translatable to varying jurisdictions due to its reliance on nationally-available datasets. Policy implications and concerns surrounding use of the Safety In Numbers effect in planning and engineering, and issues of data quality and availability in urban geographic science, are discussed.