<p dir="ltr">The number of vehicle-pedestrian crashes is increasing nationally and globally. Further, due to their lack of physical protection, pedestrians typically sustain severe injury when a crash occurs. There exists a need to identify the factors that affect pedestrian crashes frequency and severity, and to examine how these factors are different at signalized and non-signalized intersections. Therefore, this dissertation presents a comprehensive approach to investigating pedestrian safety (frequency and severity of crashes) at signalized and non-signalized intersections. Also, by identifying factors associated with higher risk of pedestrian crashes, this dissertation addresses the adequacy of the existing signalization warrants in practice.</p><p dir="ltr">The study dataset combines crash data, pedestrian and vehicle volumes, and land use data. To streamline the data collection of intersection features, a software was developed in this study, reducing the time required by threefold. The data contains emerging crowdsourced and mobile-based data to capture pedestrian volumes. Negative binomial and ordered logit models were for the model calibration.</p><p dir="ltr">Regarding crash severity, the study reveals that both driver and pedestrian impairment, multilane roads, and the presence of clear zones are significant factors of pedestrian crashes at both intersection types. However, at signalized intersections, proximity to a college campus and the presence of push-button devices are associated with less severe outcomes, while nighttime conditions significantly increase crash severity. At non-signalized intersection, the absence of lighting infrastructure during nighttime contributes to more severe crashes.</p><p dir="ltr">A key methodological contribution is the hybrid approach developed to correct misclassified pedestrian crashes by integrating structured crash data with unstructured narrative reports. This method combines manual and semi-automated processes with natural language processing to accurately classify crash severity, identifying and reclassifying 5.5% of crashes in the study. The study provides a comprehensive comparative analysis of pedestrian crashes at signalized and non-signalized intersections, offering valuable insights for urban planners, traffic engineers, and policymakers in developing safer intersection designs and implementing data-driven safety interventions across diverse urban environments.</p>

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.