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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.

Despite the introduction of road safety measures and media campaigns, crashes are a leading cause of death in Western Australia. While economic costs of crashes are relatively easy to determine, their psychosocial burden remains appreciably under-studied, as are the social, cultural, historical, temporal, and political contexts within which grief experiences are housed. As such, I explored the experience of grief resulting from losing a loved one in a crash in Western Australia and described the influence of contextual factors on those grief experiences.

Statistical models have been very popular for estimating the performance of highway safety improvement programs which are intended to reduce motor vehicle crashes. The traditional Poisson and Poisson-gamma (negative binomial) models are the most popular probabilistic models used by transportation safety analysts for analyzing traffic crash data. The Poisson-gamma model is usually preferred over traditional Poisson model since crash data usually exhibit over-dispersion. Although the Poisson-gamma model is popular in traffic safety analysis, this model has limitations particularly when crash data are characterized by small sample size and low sample mean values. Also, researchers have found that the Poisson-gamma model has difficulties in handling under-dispersed crash data. The primary objective of this research is to evaluate the performance of the Conway-Maxwell-Poisson (COM-Poisson) model for various situations and to examine its application for analyzing traffic crash datasets exhibiting over- and under-dispersion. This study makes use of various simulated and observed crash datasets for accomplishing the objectives of this research. Using a simulation study, it was found that the COM-Poisson model can handle under-, equi- and over-dispersed datasets with different mean values, although the credible intervals are found to be wider for low sample mean values. The computational burden of its implementation is also not prohibitive. Using intersection crash data collected in Toronto and segment crash data collected in Texas, the results show that COM-Poisson models perform as well as Poisson-gamma models in terms of goodness-of-fit statistics and predictive performance. With the use of crash data collected at railway-highway crossings in South Korea, several COM-Poisson models were estimated and it was found that the COM-Poisson model can handle crash data when the modeling output shows signs of under-dispersion. The results also show that the COM-Poisson model provides better statistical performance than the gamma probability and traditional Poisson models. Furthermore, it was found that the COM-Poisson model has limitations similar to that of the Poisson-gamma model when handling data with low sample mean and small sample size. Despite its limitations for low sample mean values for over-dispersed datasets, the COM-Poisson is still a flexible method for analyzing crash data.

Pedestrian and cyclist collisions with motor vehicles are a growing problem in the United States and in particular Austin, Texas. These collisions cause unnecessary loss of life and injury. This research explores strategies in the realms of engineering, education and enforcement from three leading cities for pedestrian and cyclist safety. Based on the analysis, I have identified possible strategies for use in Austin, Texas to prevent these collision types. I also offer a conceptual model that can be used as a framework for organizing and thinking about the various components involved in preventing pedestrian and cyclist collisions with motor vehicles. / text

Cross-centerline crashes occur rarely in the United States but are especially severe. This type of crash is characterized by one vehicle departing over a centerline and encountering a vehicle traveling in the opposite direction. In recent years, automakers have started developing and implementing lane departure warning (LDW) on newer vehicles. This system provides the potential to reduce or significantly impact the frequency of cross-centerline crashes. The objective of this thesis was to estimate the potential crash and injury benefits of a LDW system if installed on every vehicle in the US fleet. This research includes the following 1) a characterization of cross-centerline crashes in the United States today with current and future prevention methods, 2) a reconstruction methodology used for all crashes including rollovers and heavy vehicles, and 3) a simulation model and approach method used to estimate potential benefits of LDW systems on cross-centerline crashes. Cross over to left crashes account for only 4% of non-junction non-interchange crashes but account for 44% of serious injury crashes of the same type. As part of this research, 42 cross-centerline crashes were reconstructed and simulated as if they had a LDW system installed. Accounting for driver capability to react to a LDW alert, crash reduction benefits ranged from 22 – 30%.Using injury risk curves, the probability of experiencing a MAIS2+ injury in a cross-centerline crash was reduced by 29% when using a LDW system. / Master of Science

[pt] Nesta dissertação utilizamos um modelo baseado na teoria de fenômenos críticos para explicar a formação de preços de ativos financeiros no período précrash. A evolução dos preços é descrita por um crescimento lento em forma de lei de potência, superposto a oscilações periódicas em escala logarítmica, sendo denominado modelo log-periódico. Este crescimento é eventualmente interrompido por um colapso dos preços que ocorre em um curto e crítico intervalo de tempo.O objetivo deste trabalho é o de investigar o modelo logperiódico do ponto de vista econométrico, criticando e propondo melhoramentos na sua especificação de forma que as inferências estatísticas dos seus parâmetros sejam mais confiáveis. Baseado nesta análise é proposta uma extensão do modelo log-periódico, com a incorporação de estrutura auto- regressiva e heterocedástica condicional no termo aleatório do modelo original. O modelo é aplicado a índices de diversos mercados mundiais, a saber: HANG SENG (Hong Kong), NASDAQ (EUA), IBOVESPA (Brasil), MERVAL (Argentina), INDIA BSE NATIONAL (Índia) e FTSE100 (Grã-Bretanha). Os nossos resultados indicam que a utilização destes modelos na prática requer alguma cautela uma vez que a sua base inferencial é frágil. / [en] In this work we employ a model based on the critical phenomena theory to explain the asset price formation associated to the pre- crash period. The evolution of the price is given by an over-all power law acceleration decorated by oscillations called log-periodic model. This growth is likely to be interrupted by a crash of prices that happen in a short and critical time interval. The purpose of this work is to investigate the log-periodic model within the econometric approach by suggesting guidelines to achieve its performance in order to accomplish reliable statistical inferences. Based on this analysis we here propose a stretching of the log-periodic model through the introduction of an autoregressive structure and an autoregressive conditional heteroskedasticity at the residual of the original model. The current model is applied to the study of financial index of the stock markets worldwide as: HANG SENG (Hong Kong), NASDAQ (USA), IBOVESPA (Brazil), MERVAL (Argentina), INDIA BSE NATIONAL (India) and FTSE100 (United Kingdom). The output of such work indicates that the use of the logperiodic model requires some care as far as its inference basis is fragile.

The purpose of this thesis is to quantify the economic costs associated with traffic crashes for 83 of the largest metropolitan areas in the United States and compare those costs to that of congestion. This was done by collecting injury and fatality data for each area and multiplying those by economic cost estimates for each developed by the FHWA. The findings of this analysis show that the economic cost of traffic crashes exceeds the economic costs of congestion in every metropolitan area studied. These results indicate that transportation safety deserves similar consideration to that of traffic congestion when allocation transportation funds.

Road traffic injuries (RTI) currently rank as the 11th leading cause of death world wide and the leading cause of injury related deaths worldwide. Globally, road traffic crashes kill over 1.2 million people per year with over 90% of the deaths occurring in low and middle income countries. Pedestrians, cyclists, and motorcyclists, collectively called vulnerable road users, are often times the victims. Therefore, the purpose of this study was to identify the factors that are associated with traffic fatalities as well as the factors that are associated with being a vulnerable road user in traffic injuries and traffic fatalities in Pasto, Colombia. The results from this study showed that males had increased odds of being involved in a traffic fatality (OR=2.16 95%;CI 1.03-4.53). Rural road users are more likely to be involved in fatal traffic crashes than their urban counterparts (OR=5.92 95%;CI 3.00-11.71) Other groups such as young adults, vulnerable road users, and those not using safety equipment were also more likely to be injured or die in a traffic crash. Interventions specifically targeting these groups are needed.

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