Crash prediction models are used to estimate the number of crashes using a set of explanatory variables. The highway safety community has used modeling techniques to predict vehicle-to-vehicle crashes for decades. Specifically, generalized linear models (GLMs) are commonly used because they can model non-linear count data such as motor vehicle crashes. Regression models such as the Poisson, Zero-inflated Poisson (ZIP), and the Negative Binomial are commonly used to model crashes. Until recently very little research has been conducted on crash prediction modeling for pedestrian-motor vehicle crashes. This thesis considers several candidate crash prediction models using a variety of explanatory variables and regression functions. The goal of this thesis is to develop a pedestrian crash prediction model to contribute to the field of pedestrian safety prediction research. Additionally, the thesis contributes to the work done by the Federal Highway Administration to estimate pedestrian exposure in urban areas. The results of the crash prediction analyses indicate the pedestrian-vehicle crash model is similar to models from previous work. An analysis of two pedestrian volume estimation methods indicates that using a scaling technique will produce volume estimates highly correlated to observed volumes. The ratio of crash and exposure estimates gives a crash rate estimation that is useful for traffic engineers and transportation policy makers to evaluate pedestrian safety at signalized intersections in an urban environment. / Master of Science

Analyses of traffic accidents are often focused on the characteristics of the accident event and hence do not take into account the broader neighborhood contexts in which accidents are located. This thesis seeks to extend empirical analyses of accidents by understanding the link between accidents and their surroundings. The case study for this thesis is Hillsborough County, Florida, within which the city of Tampa is located. The Tampa Bay region ranks very high in terms of accident rates within U.S. metropolitan areas and is also characterized by transport policies which favor private automobiles over mass transit options, making it an especially valuable case study. This thesis seeks explanations for accidents through regression models which relate accident occurrence and accident rates to traffic, roadway and socioeconomic characteristics of census tracts. The overall findings are that socioeconomic variables, especially poverty rates and percent non-white, and transport characteristics, such as density of bus stops, show a significant relationship with both dependent variables. This research provides support for considering the wider urban context of social inequalities in order to understand the complex geographic distribution of accidents.