The goal of this thesis is to determine the contributing factors affecting severe traffic crashes (severe: incapacitating and fatal - non-severe: no injury, possible injury, and non-incapacitating), and in particular those factors influencing crashes involving large vehicles (heavy trucks, truck tractors, RVs, and buses). Florida Department of Highway Safety and Motor Vehicles (DHSMV) crash reports of 2008 have been used. The data included 352 fatalities and 9,838 injuries due to large vehicle crashes. Using the crashes involving large vehicles, a model comparison between binary logit model and a Chi-squared Automatic Interaction Detection (CHAID) decision tree model is provided. There were 13 significant factors (i.e. crash type with respect to vehicle types, residency of driver, DUI, rural-urban, etc.) found significant in the logistic procedure while 7 factors found (i.e. posted speed limit, intersection, etc.) in the CHAID model. The model comparison results indicate that the logit analysis procedure is better in terms of prediction power. The following analysis is a modeling structure involving three binary logit models. The first model was conducted to estimate the crash severity of crashes that involved only personal vehicles (PV). Second model uses the crashes that involved large vehicles (LV) and passenger vehicles (PV). The final model estimated the severity level of crashes involving only large vehicles (LV). Significant differences with respect to various risk factors including driver, iii vehicle, environmental, road geometry and traffic characteristics were found to exist between those crash types and models. For example, driving under the influence of Alcohol (DUI) has positive effect on the severity of PV vs. PV and LV vs. PV while it has no effect on LV vs. LV. As a result, 4 of the variables found to be significant were similar in all three models (although often with quite different impact) and there were 11 variables that significantly influenced crash injury severity in PV vs. PV crashes, and 9 variables that significantly influenced crash injury severity in LV vs. PV crashes. Based on the significant variables, maximum posted speed, number of vehicles involved, and intersections are among the factors that have major impact on injury severity. These results could be used to identify potential countermeasures to reduce crash severity in general, and for LVs in particular. For example, restricting the speed limits and enforcing it for large vehicles could be a suggested countermeasure based on this study.

This research investigates factors that influence severity of speed-related crashes on mainline roadway segments, with a particular emphasis on comparing single-vehicle and multiple-vehicle incidents and distinguishing between crashes involving legal speed limit violations and those exceeding the maximum safe travel speed as determined by law enforcement. Additionally, it examines significant factors related to classifying a crash as exceeding the maximum safe travel speed. Using crash data from the Traffic Records Electronic Data System (TREDS) for Virginia for 2023, the research employs both Ordinal and Nominal Logistic Regression models for analysis. The findings reveal that higher vehicle speeds before a crash significantly increase crash severity level across all scenarios. Rain and snow/sleet weather conditions exhibit significant impacts on crash outcomes, with adverse conditions often leading to increased severity levels. Roadway characteristics in terms of presence of medians and road surface conditions, are also found to be significant, as are. the driver-related factors of age, safety equipment used, EMS transport after the crash, and vehicle type. The study's comparative analysis between single and multiple vehicles speeding crashes, as well as speeding beyond legal limits and exceeding maximum safe travel speed highlights the contextual differences in crash severity determinants. The findings on classifying crashes as exceeding maximum safe travel speed highlight conditions that influence this designation as well as factors that can lead to inconsistencies in that classification. For example, environmental conditions like rain or snow, certain crash types, and work zone crashes may result in subjective assessments rather than objective determinations. The research offers valuable insights for informing targeted road safety strategies within the Safe System framework – targeted at reducing the severity of speed-related crashes for mainline road segments. The findings support implementing comprehensive strategies that address the complex interplay of speed, road conditions, vehicle characteristics, and driver factors in mitigating crash severity. / Master of Science / This research explores how speeding affects the severity of car crashes, seeking to understand why some accidents are more dangerous than others. By analyzing crash data from Virginia in 2023, the study looks at different types of crash scenarios – those involving just one vehicle and those involving multiple vehicles – and examines how factors like weather, road conditions, vehicle and driver characteristics contribute to the seriousness of these crashes. The research compares crashes where drivers exceed the legal speed limit with those where they drive faster than is safe under the given road conditions. Additionally, it investigates key factors that potentially influence law enforcement at the scene to designate that a crash involves a driver exceeding the safest speed for road and traffic conditions. The findings show that driving at higher speeds before a crash significantly increases the chances of severe injuries or fatalities. The study indicates how weather conditions, design characteristics of roads, or the condition of the road surface, impact crash severity. Driver age and whether drivers were under the influence of alcohol or drugs, and whether vehicle safety equipment like seatbelts were used, are significant in determining the severity of a crash. The findings on classifying crashes as exceeding maximum safe travel speed highlight conditions that influence this designation as well as factors that can lead to inconsistencies in that classification. This research is important because it provides insights for improving road safety.

In this thesis, security measures and vulnerability mitigation are mainly addressed. How to improve the system vulnerability is one of the main issues for power system operation and planning. Recent research revealed that Energy Storage Systems (ESSs) have a great potential to be used to improve system vulnerability. A vulnerability assessment is proposed in this thesis to identify the impact factors in the power systems due to generation outage and line outage. A Bus Impact Severity (BIS) analysis is then proposed and used to find the vulnerable buses in the system. The buses with the larger BIS value defined in this thesis are the better locations for ESSs placement. Formulations for optimal locations and capacities of ESSs placement are derived and then solved by Genetic Algorithm (GA). Test results show that the proposed method can be used to find the optimal locations and capacities for ESSs for system vulnerability improvement.

Arterial roads constitute the majority of the centerline miles of the Florida State Highway System. Severe injury involvements on these roads account for a quarter of the total severe injuries reported statewide. This research focuses on driver injury severity analysis of statewide multilane high speed arterials using crash data for the years 2002 to 2004. The first goal is to test different ways of analyzing crash data (by road entity and crash types) and find the best method of driver injury severity analysis. A second goal is to find driver, vehicle, road and environment related factors that contribute to severe involvements on multilane arterials. Exploratory analysis using one year of crash data (2004) using binary logit regression was used to measure the risk of driver severe injury given that a crash occurs. A preliminary list of significant factors was obtained. A massive data preparation effort was undertaken and a random sample of multivehicle crashes was selected for final analysis. The final injury severity analysis consisted of six road entity models and twenty crash type models. The data preparation and sampling was successful in allowing a robust dataset. The overall model was a good candidate for the analysis of driver injury severity on multilane high speed roads. Driver injury severity resulting from angle and left turn crashes were best modeled by separate non-signalized intersection crash analysis. Injury severity from rear end and fixed object crashes was best modeled by combined analysis of pure segment and non-signalized intersection crashes. The most important contributing factors found in the overall analysis included driver related variables such as age, gender, seat belt use, at-fault driver, physical defects and speeding. Crash and vehicle related contributing factors included driver ejection, collision type (harmful event), contributing cause, type of vehicle and off roadway crash. Multivehicle crashes and interactions with intersection and off road crashes were also significant. The most significant roadway related variables included speed limit, ADT per lane, access class, lane width, roadway curve, sidewalk width, non-high mast lighting density, type of friction course and skid resistance. The overall model had a very good fit but some misspecification symptoms appeared due to major differences in road entities and crash types by land use. Two additional models of crashes for urban and rural areas were successfully developed. The land use models' goodness of fit was substantially better than any other combination by road entity or the overall model. Their coefficients were substantially robust and their values agreed with scientific or empirical principles. Additional research is needed to prove these results for crash type models found most reliable by this investigation. A framework for injury severity analysis and safety improvement guidelines based on the results is presented. Additional integration of road characteristics (especially intersection) data is recommended for future research. Also, the use of statistical methods that account for correlation among crashes and locations are suggested for use in future research.