Master of Science / Department of Civil Engineering / Sunanda Dissanayake / One-ninth of all traffic fatalities in the United States (U.S.) in the past five years have involved large trucks, although large trucks contributed to only 3% of registered vehicles and 7% of vehicle miles traveled. This crash overrepresentation indicates that truck crashes in general tend to be more severe than other crashes, though they constitute a smaller portion of vehicles on the road. To study this issue, fatal crash data from the Fatality Analysis Reporting System (FARS) was used to analyze characteristics and factors contributing to truck-involved crashes. Driver, vehicle, and crash-related contributory causes were identified, and as an extension, the likelihood of occurrence of these contributory causes in truck-involved crashes (with respect to non-truck crashes) was evaluated using the Bayesian Statistical approach. Likelihood ratios indicated that factors such as stopped or unattended vehicles and improper following have greater probability of occurrence in truck crashes than in non-truck crashes. Also, Multinomial Logistic Regression was used to model the type of fatal crash (truck vs. non-truck) to compare the relative significance of various factors in truck and non-truck crashes. Factors such as cellular phone usage, failure to yield right of way, inattentiveness, and failure to obey traffic rules also have a greater probability in fatal truck crashes. Among several other factors, inadequate warning signs and poor shoulder conditions were also found to have greater predominance in contributing to truck crashes than non-truck crashes. By addressing these factors through the implementation of appropriate remedial measures, the truck safety experience could be improved, which would eventually help in improving overall safety of the transportation system.

Working to increase seatbelt usage among teenagers in rural settings: The Drive Alive Program (Under the direction of Dr. Monica Swahn) Background: Motor vehicle crashes are a leading cause of death in the United States. Teens are less likely to wear seatbelts than other age groups and more likely to be involved in a crash. The Drive Alive program was designed to improve seatbelt usage among teens. Purpose: This analysis aims to evaluate seatbelt use among teen drivers in a rural setting. Specifically, are there differences between males and females in terms of seatbelt use? Are drivers more likely to wear their seatbelts than passengers? Methods: Data was gathered from observational surveys (N= 3,743). Surveys were gathered by trained observers in South Georgia from 2010-2011. Records were analyzed in SPSS using three categories: occupant, sex, and belt use. The null hypotheses for this study are: 1) there is no significant difference between male and female drivers or passengers in seatbelt usage; and, 2) there is no significant difference between driver and passenger seatbelt usage. Results: Descriptive analyses were computed to determine average seatbelt use across all occupants. Chi Square for Independence tests were computed to determine differences between drivers and passengers and males and females. Females were significantly more likely than males to wear their seatbelt (Females, 70%; Males, 59%). There were no significant differences in seatbelt use for drivers and their passengers. Conclusions: Results for females being more likely to wear seatbelts is consistent with the literature. Future research might include comparison between schools with different versions of the program. Programs to increase seatbelt usage among teens should include parents, education, enforcement, teen-led activities, and partnership with educators and community organizations. Save the lives of young drivers by modeling seatbelt wearing, appropriately implementing comprehensive seatbelt use improvement programs, and enforcing the law. These simple measures will improve seatbelt use and reduce roadway fatalities.

<p><p>The recent SEC ban on short selling has presented an unrivaled opportunity to explore the effects of short selling constraints on crashes, market efficiency, and volatility. In this paper I carry out two groups of empirical tests on the individual banned stocks and a series of portfolios created from them: the first tests the hypothesis that short sale constraints increase the frequency and magnitude of crashes, by testing Hong & Stein’s (2003) model of market crashes. The second tests the hypothesis that short sale constraints reduce market efficiency, by testing Miller’s (1977) model in which stocks that are hard (or impossible) to short tend to exhibit overpricing. In regards to the first group of tests, the results are ambiguous: the frequency and magnitude of crashes increased during the ban period, while the skewness of the returns distribution of the portfolios became more negative, as expected, but these changes hold for the market as a whole, as well. On the other hand, the skewness of the returns distribution of the individual banned stocks became more positive. The second group of tests provides ample support for Miller’s model, as the results coincide with the models predictions: banning short selling leads to positive abnormal returns (overpricing) in the affected stocks. The ban is also related with a decrease in volatility relative to the market, an important result from a policy perspective.</p></p>

Momentum investing is the process of buying stocks that have performed strongly in the past and shorting historically weak performers. Past empirical research has consistently shown momentum to generate significantly positive returns on a zero-cost strategy. We show that momentum performs well regardless of the specific time horizon used in formation and investment and motivate a one month gap between forming the portfolio and investing in it. Consistent with literature, we find momentum crashes–months where momentum’s profitability dramatically reverses–and demonstrate that momentum crashes occur across all time horizon variations in momentum. Lastly, we show that the momentum portfolio during crash months is not marked by clustering in specific industries, and that the momentum premium can- not be explained by risk from regulatory uncertainty of the financial services industry.

Doctor of Philosophy / Department of Civil Engineering / Sunanda Dissanayake / Malgorzata J. Rys / Driver workload increases on horizontal curves due to more complicated navigation compared to navigation on straight roadway sections. Although only a small portion of roadways are horizontal curve sections, approximately 25% of all fatal highway crashes occur at horizontal curve sections. According to the Fatality Analysis Reporting System (FARS) database, fatalities associated with horizontal curves were more than 25% during last years from 2008 to 2014, reinforcing that investigation of horizontal curve crashes and corresponding safety improvements are crucial study topics within the field of transportation safety. Improved safety of horizontal curve sections of rural transportation networks can contribute to reduced crash severities and frequencies. Statistical methods can be utilized to develop crash prediction models in order to estimate crashes at horizontal curves and identify contributing factors to crash occurrences, thereby correlating to the primary objectives of this research project. Primary data analysis for 221 randomly selected horizontal curves on undivided two-lane two-way highways with Poisson regression method revealed that annual average daily traffic (AADT), heavy vehicle percentage, degree of curvature, and difference between posted and advisory speeds affect crash occurrence at horizontal curves. The data, however, were relatively overdispersed, so the negative binomial (NB) regression method was utilized. Results indicated that AADT, heavy vehicle percentage, degree of curvature, and long tangent length significantly affect crash occurrence at horizontal curve sections. A new dataset consisted of geometric and traffic data of 5,334 horizontal curves on the entire state transportation network including undivided and divided highways provided by Kansas Department of Transportation (KDOT) Traffic Safety Section as well as crash data from the Kansas Crash and Analysis Reporting System (KCARS) database were used to analyze the single vehicle (SV) crashes. An R software package was used to write a code and combine required information from aforementioned databases and create the dataset for 5,334 horizontal curves on the entire state transportation network. Eighty percent of crashes including 4,267 horizontal curves were randomly selected for data analysis and remaining 20% horizontal curves (1,067 curves) were used for data validation. Since the results of the Poisson regression model showed overdispersion of crash data and many horizontal curves had zero crashes during the study period from 2010 to 2014, NB, zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) methods were used for data analysis. Total number of crashes and severe crashes were analyzed with the selected methods. Results of data analysis revealed that AADT, heavy vehicle percentage, curve length, degree of curvature, posted speed, difference between posted and advisory speed, and international roughness index influenced single vehicle crashes at 4,267 randomly selected horizontal curves for data analysis. Also, AADT, degree of curvature, heavy vehicle percentage, posted speed, being a divided roadway, difference between posted and advisory speeds, and shoulder width significantly influenced severe crash occurrence at selected horizontal curves. The goodness-of-fit criteria showed that the ZINB model more accurately predicted crash numbers for all crash groups at the selected horizontal curve sections. A total of 1,067 horizontal curves were used for data validation, and the observed and predicted crashes were compared for all crash groups and data analysis methods. Results of data validation showed that ZINB models for total crashes and severe crashes more accurately predicted crashes at horizontal curves. This study also investigated the effect of speed limit change on horizontal curve crashes on K-5 highway in Leavenworth County, Kansas. A statistical t-test proved that crash data from years 2006 to 2012 showed only significant reduction in equivalent property damage only (EPDO) crash rate for adverse weather condition at 5% significance level due to speed limit reduction in June 2009. However, the changes in vehicles speeds after speed limit change and other information such as changes in surface pavement condition were not available. According to the results of data analysis for 221 selected horizontal curves on undivided two-lane highways, tangent section length significantly influenced total number of crashes. Therefore, providing more information about upcoming changes in horizontal alignment of the roadway via doubling up warning sings, using bigger sings, using materials with higher retroreflectivity, or flashing beacons were recommended for horizontal curves with long tangent section lengths and high number of crashes. Also, presence of rumble strips and wider shoulders significantly and negatively influenced severe SV crashes at horizontal curve sections; therefore, implementing rumble strips and widening shoulders for horizontal curves with high number of severe SV crashes were recommended.

The Gambia is the smallest country in mainland Africa. Along with the rapid urbanization rate, motorization has increased rapidly as well, contributing to an increased number of road traffic crashes. Road traffic crashes are the 4th leading cause of in-patient deaths in adults in the Gambia and currently are a significant public health problem. This study utilized the Gambia Traffic Force’s data registry to become the first epidemiological study on road traffic injuries in the Gambia as well as the first to analyze the Gambia’s traffic data registry on a national level. Reported crashes from October 1st, 2014 to June 30, 2015 were converted from the paper-based data registry into an electronic database and analyzed statistically, and the location data were geocoded and plotted on the Gambian map. The results of this study showed that crashes involving pedestrian victims and crashes that occurred on unpaved roads were more likely to be associated with outcomes that were fatal or serious. When multiple vehicles were involved in a crash, the involvement of motorcycles and bicycles were more likely to lead to a fatal or serious injury. The mapped data showed that towards the center of each district, the number of crashes increased as pedestrian and vehicle density increased, but that injury severity outcomes were generally minor or none. In contrast, as pedestrian and vehicle density decreased, crash frequency decreased as well, but injury outcomes were more likely to be severe or fatal. The findings of the study also helped in identifying areas in policy and education that need improvement.

The recent SEC ban on short selling has presented an unrivaled opportunity to explore the effects of short selling constraints on crashes, market efficiency, and volatility. In this paper I carry out two groups of empirical tests on the individual banned stocks and a series of portfolios created from them: the first tests the hypothesis that short sale constraints increase the frequency and magnitude of crashes, by testing Hong &amp; Stein’s (2003) model of market crashes. The second tests the hypothesis that short sale constraints reduce market efficiency, by testing Miller’s (1977) model in which stocks that are hard (or impossible) to short tend to exhibit overpricing. In regards to the first group of tests, the results are ambiguous: the frequency and magnitude of crashes increased during the ban period, while the skewness of the returns distribution of the portfolios became more negative, as expected, but these changes hold for the market as a whole, as well. On the other hand, the skewness of the returns distribution of the individual banned stocks became more positive. The second group of tests provides ample support for Miller’s model, as the results coincide with the models predictions: banning short selling leads to positive abnormal returns (overpricing) in the affected stocks. The ban is also related with a decrease in volatility relative to the market, an important result from a policy perspective.

Many state agencies consider exclusive truck facilities to be an alternative to handle the safety and operational issues due to the increasing truck volumes. No such facilities exist, and there are no standard tools or procedures for measuring safety performance of an exclusive truck facility. This thesis aims at identifying factors that affect truck crashes, whose results could be used for better designing exclusive truck facilities. To accomplish the objectives of this thesis, five years’ roadway and crash data for Texas was collected to develop a comprehensive crash database. Negative binomial regression models were used to establish a relationship between truck crashes and various environmental, geometric and traffic variables. Separate models were developed for truck-related (involving at least one truck and another vehicle), truck-only (two trucks or more) and single-truck crashes. The results suggested that the percentage of trucks in Average Annual Daily Traffic (AADT), classification of the roadway (Rural/Urban), posted speed limit, surface condition, alignment and shoulder width are associated with truck crashes. It was observed that truck-related and truck-only crashes decreased as the percentage of trucks increased on freeway facilities. Based on conclusions derived from the literature review and statistical analyses, straight segments with wider shoulders and uniform grades are recommended for exclusive truck facilities. It is also recommended to provide ramps, horizontal and vertical curvature and signing based on truck size, driver eye height, braking ability and maneuverability. These models were developed using mixed-flow traffic data to understand the association of various factors with truck crashes. These models should not be used directly to estimate or predict truck crashes. Further analysis with more detailed data under different flow conditions might help in quantifying the safety performance of exclusive truck facilities.

Many state agencies consider exclusive truck facilities to be an alternative to handle the safety and operational issues due to the increasing truck volumes. No such facilities exist, and there are no standard tools or procedures for measuring safety performance of an exclusive truck facility. This thesis aims at identifying factors that affect truck crashes, whose results could be used for better designing exclusive truck facilities. To accomplish the objectives of this thesis, five years' roadway and crash data for Texas was collected to develop a comprehensive crash database. Negative binomial regression models were used to establish a relationship between truck crashes and various environmental, geometric and traffic variables. Separate models were developed for truck-related (involving at least one truck and another vehicle), truck-only (two trucks or more) and single-truck crashes. The results suggested that the percentage of trucks in Average Annual Daily Traffic (AADT), classification of the roadway (Rural/Urban), posted speed limit, surface condition, alignment and shoulder width are associated with truck crashes. It was observed that truck-related and truck-only crashes decreased as the percentage of trucks increased on freeway facilities. Based on conclusions derived from the literature review and statistical analyses, straight segments with wider shoulders and uniform grades are recommended for exclusive truck facilities. It is also recommended to provide ramps, horizontal and vertical curvature and signing based on truck size, driver eye height, braking ability and maneuverability. These models were developed using mixed-flow traffic data to understand the association of various factors with truck crashes. These models should not be used directly to estimate or predict truck crashes. Further analysis with more detailed data under different flow conditions might help in quantifying the safety performance of exclusive truck facilities.

This thesis analyzes aggregated county-level data of fatal alcohol related traffic crashes where a driver was killed in the state of Texas during 1996 to 2005. Alcohol has constantly threatened drivers and passengers alike and continues to be a major cause of fatal crashes in Texas. Specifically, this paper targets those drivers that were killed while driving under the influence (0.01 BAC). With an increase in manageable data and the ease of availability of aggregated crash records, accident analysis can provide a closer look into trends such as spatial-temporal patterns, clustering and correlations to various factors. Furthermore, Geographic Information Systems (GIS) have enabled researchers to more efficiently interpret and study a large amount of datasets using techniques that were previously difficult or inaccessible in applications related to traffic safety and transportation. Loose-coupling of GIS with other spatial analysis programs and/or statistical software packages can now provide important results that in turn relate vital information which can be used towards understanding and potentially alleviating problems in the transportation domain. The following sections concluded that aggregated datasets at the county level are currently incomplete and do not provide the level of detail necessary to formulate a solid conclusion regarding relationships between the chosen factors and the crash dataset. Though this research was successful in mapping spatial variations and clusters, linking variables such as age, gender, location and population to the aggregated crash dataset requires more detailed information about the crash than was available. However, the objectives were successful in representing spatial-temporal patterns across the study period for all designated variables. This was an important step and solid contribution towards the representation of large datasets and their impact on policy, traffic safety, and transportation geography.