This dissertation performs a comprehensive analysis of the effect of different environmental, demographic and vehicle variables on the severity of two-vehicle crashes. The limitations associated with previous studies have been addressed by using a large crash database, properly defining the independent variables, using appropriate statistical models, and by considering the effect of factors normally unaccounted for such as crash type, impact speed, and weight or height incompatibilities between the two vehicles.The use of multinomial logit models at the individual occupant and crash levels provides the flexibility to evaluate variables that have opposing effects at different injury levels (such as airbags). Alternative formulations with interaction terms and with instrumental variables are included. An analysis of marginal probabilities and costs is also provided, which is particularly useful when discussing potential safety treatments with transportation officials, politicians and other decision makers.The findings from the different models are consistent and suggest that the type of crash has a great impact on severity. Age is the most significant demographic variable, with children and older occupants being least and most likely to be injured, respectively. Behavior also seems to be critical, as the use of seatbelts greatly decreases occupant injuries. Heavier vehicles increase the safety of its occupants but decrease the safety of occupants of the other vehicle. The effect of vehicle type is not as significant as weight, with the exception of pickups, which are both more crashworthy and more aggressive than passenger cars. Further research is needed on the effects of airbags and impaired driving, as the analyses conducted have been inconclusive.

Master of Science / Department of Civil Engineering / Sunanda Dissanayake / The percentage of the U.S. population aged 65 years or older is increasing rapidly. Statistics also show this age group was 14.9 percent of the population in 2015 and is expected to be 20.7 to 21.4 percent for the years 2030–2050. Kansas has similar statewide trends with its aging population. Therefore, identifying issues, concerns, and factors associated with severity of older-driver crashes in Kansas is necessary. The Kansas Crash Analysis and Reporting System (KCARS) database maintained by Kansas Department of Transportation was used in this study to identify older-driver crash characteristics, compare older drivers with all drivers, and develop crash severity models. According to KCARS data, older drivers were involved in more than one in five fatal injuries out of all drivers in Kansas from 2010 to 2014. When compared with all drivers, older drivers were overly represented in fatal and incapacitating injuries. The percentage of older-driver fatal injuries was more than the twice that of all drivers. When compared with all drivers, older drivers were involved more often in crashes at four-way intersections, on straight and level roads, in daylight hours, and at a stop or yield signs. An in-depth crash severity analysis was carried out for the older drivers involved in crashes. Three separate binary logistic regression models were developed for single-vehicle crashes where only the older driver was present (Model A), single-vehicle crashes involving an older driver with at least one passenger (Model B), and multi-vehicle crashes involving at least one older driver (Model C). From the crash severity analysis, it was found that left turns were significant in changing the crash severity for Model A, but it was not significant in model B, meaning that older drivers may be safer with passengers. For Model B, none of the passenger attributes were significant, though it was originally developed to identify passenger attributes. Gender of the older driver was not significant in any model. For all models, variables such as safety equipment use, crash location, weather conditions, driver ejected or trapped, and light conditions distinguished crash severity. Furthermore, for Model A, variables such as day of the week, speed, accident class, and maneuver, distinguished crash severity. Moreover, accident class, surface type, and vehicle type changed crash severity in Model B. Number of vehicles, speed, collision type, maneuver, and two-lane roads were significant in Model C. A road-user survey was also conducted to identify habits, needs, and concerns of Kansas' aging road users since it was not advisable to conclude safety factors solely on crash data. The probability of occurrence was calculated by taking the weighted average of answers to a question. Then a contingency table analysis was carried out to identify relationships among variables. For older drivers, seatbelt use as a driver had the highest probability of occurrence. Driving in heavy traffic, merging into traffic, moving away from traffic, and judging gaps were dependent on age group. Findings of this research gave an understanding of older-driver crashes and associated factors. Since more than 85 percent of crash contributory causes were related to drivers, driver awareness programs, driver licensing restrictions, providing public transportation, and law enforcement can be used as countermeasures. Accordingly, results of this study can be used to enhance older-driver safety and awareness programs.

The Utah Department of Transportation developed life-cycle benefit-cost analysis spreadsheets that allow engineers and analysts to evaluate multiple safety countermeasures. The spreadsheets have included the functionality to evaluate a roadway based on the 11 facility types from the Highway Safety Manual (HSM) with the use of crash severity distributions. The HSM suggests that local agencies develop crash severity distributions based on their local crash data. The Department of Civil and Environmental Engineering at Brigham Young University worked with the Statistics Department to develop crash severity distributions for the facility types from the HSM.The primary objective of this research was to utilize available roadway characteristic and crash data to develop crash severity distributions for the 11 facility types in the HSM. These objectives were accomplished by segmenting the roadway data based on homogeneity and developing statistical models to determine the distributions. Due to insufficient data, the facility types of freeway speed change lanes and freeway ramps were excluded from the scope of this research. In order to accommodate more roadways within the research, the facility type definitions were expanded to include more through lanes.The statistical models that were developed for this research include multivariate regression, frequentist binomial regression, frequentist multinomial, and Bayesian multinomial regression models. A cross-validation study was conducted to determine the models that best described the data. Bayesian Information Criterion, Deviance Information Criterion, and Root-Mean-Square Error values were compared to conduct the comparison. Based on the cross-validation study, it was determined that the Bayesian multinomial regression model is the most effective model to describe the crash severity distributions for the nine facility types evaluated.

Numerous studies have documented the application of crash severity models to explore the relationship between crash severity and its contributing factors. These studies have shown that a large amount of work was conducted on this topic and usually focused on different types of models. However, only a limited amount of research has compared the performance of different crash severity models. Additionally, three major issues related to the modeling process for crash severity analysis have not been sufficiently explored: sample size, model misspecification and underreporting in crash data. Therefore, in this research, three commonly used traffic crash severity models: multinomial logit model (MNL), ordered probit model (OP) and mixed logit model (ML) were studied in terms of the effects of sample size, model misspecification and underreporting in crash data, via a Monte-Carlo approach using simulated and observed crash data. The results of sample size effects on the three models are consistent with prior expectations in that small sample sizes significantly affect the development of crash severity models, no matter which model type is used. Furthermore, among the three models, the ML model was found to require the largest sample size, while the OP model required the lowest sample size. The sample size requirement for the MNL model is intermediate to the other two models. In addition, when the sample size is sufficient, the results of model misspecification analysis lead to the following suggestions: in order to decrease the bias and variability of estimated parameters, logit models should be selected over probit models. Meanwhile, it was suggested to select more general and flexible model such as those allowing randomness in the parameters, i.e., the ML model. Another important finding was that the analysis of the underreported data for the three models showed that none of the three models was immune to this underreporting issue. In order to minimize the bias and reduce the variability of the model, fatal crashes should be set as the baseline severity for the MNL and ML models while, for the OP models, the rank for the crash severity should be set from fatal to property-damage-only (PDO) in a descending order. Furthermore, when the full or partial information about the unreported rates for each severity level is known, treating crash data as outcome-based samples in model estimation, via the Weighted Exogenous Sample Maximum Likelihood Estimator (WESMLE), dramatically improve the estimation for all three models compared to the result produced from the Maximum Likelihood estimator (MLE).

No description available.

Road safety is a serious societal concern in Riyadh city, Kingdom of Saudi Arabia. Because of the negative impact of traffic crashes which cause losses in the form of deaths, injuries and property damage, in addition to the pain and social tragedy affecting families of the victims, it is important for transport policy makers to reduce their impact and increase safety standards by reducing the severity and frequency of crashes in the city of Riyadh. It is therefore important to fully understand the relationship between traffic crash severity and frequency and their contributing factors so to establish effective safety policies which can be implemented to enhance road safety in Riyadh city. Data used in previous research have only consisted of basic information as there was unavailability of suitable and accurate data in Riyadh and there are very few studies that have undertaken as small area-wide crash analysis in Riyadh using appropriate statistical models. Therefore safety policies are not based on rigorous analyses to identify factors affecting both the severity and the frequency of traffic crashes. This research aims to explore the relationship between traffic crash severity and frequency and their contributing factors by using statistical models and a GIS approach. The analysis is based on the data obtained over a period of five years, namely AH 1425, 1426, 1427, 1428, and 1429 (roughly equivalent to 2004, 2005, 2006, 2007, and 2008). Injury crash severity data were classified into three categories: fatal, serious injury and slight injury. A series of statistical models were employed to investigate the factors that affect both crash severity (i.e. ordered logit and mixed logit models) and area-wide crash frequency (i.e. classical Poisson and negative binomial models). Because of a severe underreporting problem on the slight injury crashes, binary and mixed binary logistic regression models were also estimated for two categories of severity: fatal and serious crashes. The mixed binary logit model and the negative binomial model are found to be the best models for crash severity and crash frequency analyses respectively. The model estimation results suggest that the statistically significant factors in crash severity are the age and nationality of the driver who is at fault, the time period from 16.00 to 19.59, excessive speed, road surface and lighting conditions, number of vehicles involved and number of casualties. Older drivers are associated with a higher probability of having a fatal crash, and, as expected, excessive speeds were consistently associated with fatal crashes in all models. In the area-level crash frequency models, population, percentage of illiterate people, income per capita and income per adult were found to be positively associated with the frequency of both fatal and serious injury crashes whereas all types of land use such as percentages of residential use, transport utilities, and educational use in all models were found to be negatively associated with the frequency of occurrence of crashes. Results suggest that safety strategies aimed at reducing the severity and frequency of traffic crashes in Riyadh city should take into account the structure of the resident population and greater emphasis should be put on native residents and older age groups. Tougher enforcement should be introduced to tackle the issue of excessive speed. This thesis contributes to knowledge in terms of examining and identifying a range of factors affecting traffic crash severity and frequency in Riyadh city.

A Thesis submitted to The University of Arizona College of Medicine - Phoenix in partial fulfillment of the requirements for the Degree of Doctor of Medicine. / Motor vehicle accidents involving pedestrians are some of the most common and lethal forms of injury for children in the United States. Among younger children, a common mechanism of action for severe trauma is when a vehicle runs over the child in a forward or backward motion at low speed resulting in a blunt crush injury. This typically occurs in non‐traffic settings including driveways, sidewalks, and roadways. Such incidents have been referred to in many different ways in the literature but for the purposes of this paper will be referred to as low speed vehicle run‐overs. This is a retrospective chart review carried out at Phoenix Children’s Hospital in affiliation with the University of Arizona College of Medicine‐Phoenix that categorizes and examines the injuries sustained by patients involved in low speed vehicle runovers occurring between December 2007 and August 2013. Fifty‐five pediatric patients were included with a median age of 24 months and 6 of these patients were fatally injured. Internal injuries were common overall and significantly more common in children ≤24months. Over half of the cohort sustained fractures, with a 24% incidence of skull fractures. All fatalities were the result of traumatic brain injury. Twenty percent of victims required operative intervention. It was concluded that the severity of these types of incidents varies from minimal to life threatening and best care requires close and thorough evaluation by the trauma and emergency department teams.

Master of Science / Department of Civil Engineering / Sunanda Dissanayake / In order to improve safety of the overall surface transportation system, each of the critical areas needs to be addressed separately with more focused attention. Statistics clearly show that large-truck crashes contribute significantly to an increased percentage of high-severity crashes. It is therefore important for the highway safety community to identify characteristics and contributory causes related to large-truck crashes. During the first phase of this study, fatal crash data from the Fatality Analysis Reporting System (FARS) database were studied to achieve that objective. In this second phase, truck-crashes of all severity levels were analyzed with the intention of understanding characteristics and contributory causes, and identifying factors contributing to increased severity of truck-crashes, which could not be achieved by analyzing fatal crashes alone. Various statistical methodologies such as cross-classification analysis and severity models were developed using Kansas crash data. Various driver-, road-, environment- and vehicle- related characteristics were identified and contributory causes were analyzed. From the cross-classification analysis, severity of truck-crashes was found to be related with variables such as road surface (type, character and condition), accident class, collision type, driver- and environment-related contributory causes, traffic-control type, truck-maneuver, crash location, speed limit, light and weather conditions, time of day, functional class, lane class, and Average Annual Daily Traffic (AADT). Other variables such as age of truck driver, day of the week, gender of truck-driver, pedestrian- and truck-related contributory causes were found to have no relationship with crash severity of large trucks. Furthermore, driver-related contributory causes were found to be more common than any other type of contributory cause for the occurrence of truck-crashes. Failing to give time and attention, being too fast for existing conditions, and failing to yield right of way were the most dominant truck-driver-related contributory causes, among many others. Through the severity modeling, factors such as truck-driver-related contributory cause, accident class, manner of collision, truck-driver under the influence of alcohol, truck maneuver, traffic control device, surface condition, truck-driver being too fast for existing conditions, truck-driver being trapped, damage to the truck, light conditions, etc. were found to be significantly related with increased severity of truck-crashes. Truck-driver being trapped had the highest odds of contributing to a more severe crash with a value of 82.81 followed by the collision resulting in damage to the truck, which had 3.05 times higher odds of increasing the severity of truck-crashes. Truck-driver under the influence of alcohol had 2.66 times higher odds of contributing to a more severe crash. Besides traditional practices like providing adequate traffic signs, ensuring proper lane markings, provision of rumble strips and elevated medians, use of technology to develop and implement intelligent countermeasures were recommended. These include Automated Truck Rollover Warning System to mitigate truck-crashes involving rollovers, Lane Drift Warning Systems (LDWS) to prevent run-off-road collisions, Speed Limiters (SLs) to control the speed of the truck, connecting vehicle technologies like Vehicle-to-Vehicle (V2V) integration system to prevent head-on collisions etc., among many others. Proper development and implementation of these countermeasures in a cost effective manner will help mitigate the number and severity of truck-crashes, thereby improving the overall safety of the transportation system.

No description available.

Statistics show that signalized intersections are among the most dangerous locations of a roadway network. Different approaches including crash frequency and severity models have been used to establish the relationship between crash occurrence and intersection characteristics. In order to model crash occurrence at signalized intersections more efficiently and eventually to better identify the significant factors contributing to crashes, this dissertation investigated the temporal, spatial, and site correlations for total, rear-end, right-angle and left-turn crashes. Using the basic regression model for correlated crash data leads to invalid statistical inference, due to incorrect test statistics and standard errors based on the misspecified variance. In this dissertation, the Generalized Estimating Equations (GEEs) were applied, which provide an extension of generalized linear models to the analysis of longitudinal or clustered data. A series of frequency models are presented by using the GEE with a Negative Binomial as the link function. The GEE models for the crash frequency per year (using four correlation structures) were fitted for longitudinal data; the GEE models for the crash frequency per intersection (using three correlation structures) were fitted for the signalized intersections along corridors; the GEE models were applied for the rear-end crash data with temporal or spatial correlation separately. For right-angle crash frequency, models at intersection, roadway, and approach levels were fitted and the roadway and approach level models were estimated by using the GEE to account for the "site correlation"; and for left-turn crashes, the approach level crash frequencies were modeled by using the GEE with a Negative Binomial link function for most patterns and using a binomial logit link function for the pattern having a higher proportion of zeros and ones in crash frequencies. All intersection geometry design features, traffic control and operational features, traffic flows, and crashes were obtained for selected intersections. Massive data collection work has been done. The autoregression structure is found to be the most appropriate correlation structure for both intersection temporal and spatial analyses, which indicates that the correlation between the multiple observations for a certain intersection will decrease as the time-gap increase and for spatially correlated signalized intersections along corridors the correlation between intersections decreases as spacing increases. The unstructured correlation structure was applied for roadway and approach level right-angle crashes and also for different patterns of left-turn crashes at the approach level. Usually two approaches at the same roadway have a higher correlation. At signalized intersections, differences exist in traffic volumes, site geometry, and signal operations, as well as safety performance on various approaches of intersections. Therefore, modeling the total number of left-turn crashes at intersections may obscure the real relationship between the crash causes and their effects. The dissertation modeled crashes at different levels. Particularly, intersection, roadway, and approach level models were compared for right-angle crashes, and different crash assignment criteria of "at-fault driver" or "near-side" were applied for disaggregated models. It shows that for the roadway and approach level models, the "near-side" models outperformed the "at-fault driver" models. Variables in traffic characteristics, geometric design features, traffic control and operational features, corridor level factor, and location type have been identified to be significant in crash occurrence. In specific, the safety relationship between crash occurrence and traffic volume has been investigated extensively at different studies. It has been found that the logarithm of traffic volumes per lane for the entire intersection is the best functional form for the total crashes in both the temporal and spatial analyses. The studies of right-angle and left-turn crashes confirm the assumption that the frequency of collisions is related to the traffic flows to which the colliding vehicles belong and not to the sum of the entering flows; the logarithm of the product of conflicting flows is usually the most significant functional form in the model. This study found that the left-turn protection on the minor roadway will increase rear-end crash occurrence, while the left-turn protection on the major roadway will reduce rear-end crashes. In addition, left-turn protection reduces Pattern 5 left-turn crashes (left-turning traffic collides with on-coming through traffic) specifically, but it increases Pattern 8 left-turn crashes (left-turning traffic collides with near-side crossing through traffic), and it has no significant effect on other patterns of left-turn crashes. This dissertation also investigated some other factors which have not been considered before. The safety effectiveness of many variables identified in this dissertation is consistent with previous studies. Some variables have unexpected signs and a justification is provided. Injury severity also has been studied for Patterns 5 left-turn crashes. Crashes were located to the approach with left-turning vehicles. The "site correlation" among the crashes occurred at the same approach was considered since these crashes may have similar propensity in crash severity. Many methodologies and applications have been attempted in this dissertation. Therefore, the study has both theoretical and implementational contribution in safety analysis at signalized intersections.