This thesis investigates the associations between crash outcomes, the existence and type of driver distraction as well as driver’s age. The crash outcomes considered in this thesis consist of the type of crash as well as the injury severity sustained by occupants involved in the crash. An ordered logit model was built to predict the likelihood of severe injuries and a multinomial model was developed to predict the likelihood that a driver will be involved in one of three common crash types: singular, angular, and rearend. In these models, various factors (e.g., weather, driver’s gender, and speeding) have been statistically controlled for, but the main focus was on the interaction of driver’s age and distraction type. The findings of this thesis have implications for policy making and prioritizing capabilities of distraction-related safety systems.

This thesis investigates the associations between crash outcomes, the existence and type of driver distraction as well as driver’s age. The crash outcomes considered in this thesis consist of the type of crash as well as the injury severity sustained by occupants involved in the crash. An ordered logit model was built to predict the likelihood of severe injuries and a multinomial model was developed to predict the likelihood that a driver will be involved in one of three common crash types: singular, angular, and rearend. In these models, various factors (e.g., weather, driver’s gender, and speeding) have been statistically controlled for, but the main focus was on the interaction of driver’s age and distraction type. The findings of this thesis have implications for policy making and prioritizing capabilities of distraction-related safety systems.

Safety literature traditionally employs crash frequency models over aggregated data on different spatial scales – micro level (such as segment or intersection) and macro level (such as zone or block) to examine crash occurrence while crash outcome models are employed at the disaggregate level (such as crash or driver record) to examine crash consequences. However, such independent model systems ignore the embedded relationship within data across different resolutions and result in mis-specified models. Recognizing these drawbacks, the current research proposes multiple frameworks for integrating multi-level crash analysis models. Specifically, the proposed frameworks integrate (i) macro and micro level crash frequency models, (ii) aggregate and disaggregate level models to estimate crash frequency by severity, (iii) aggregate and disaggregate level models to jointly estimate crash frequency by crash type and severity, and (iv) macro, micro and disaggregate level models to estimate crash frequency by severity while accounting for hierarchical relationships among the different levels. The frameworks employ econometric building blocks including negative binomial (NB), NB-ordered probit fractional split, multinomial logit and ordered probit models while accommodating for unobserved heterogeneity. The empirical analysis is conducted using data from the City of Orlando, Florida. Several model fit measures, validation exercises and elasticity analysis augment the model analysis. The study results highlighted that all the integrated frameworks showed superior performance relative to the non-integrated (independent) model systems at corresponding analysis resolutions in terms of model fit and predictive performance. The validation exercises also highlighted the superiority of the proposed integrated frameworks. Further, capturing spatial unobserved heterogeneity and random parameter effects improved the performance of the proposed integrated frameworks. The study findings show that the application of the proposed integrated frameworks can allow transportation professionals to adopt policy-based, site-specific, and outcome-specific solutions simultaneously.

Safety at intersections is of significant interest to transportation professionals due to the large number of possible conflicts that occur at those locations. In particular, rural intersections have been recognized as one of the most hazardous locations on roads. However, most models of crash frequency at rural intersections, and road segments in general, do not differentiate between crash type (such as angle, rear-end or sideswipe) and injury severity (such as fatal injury, non-fatal injury, possible injury or property damage only). Thus, there is a need to be able to identify the differential impacts of intersection-specific and other variables on crash types and severity levels. This thesis builds upon the work of Bhat et al., (2013b) to formulate and apply a novel approach for the joint modeling of crash frequency and combinations of crash type and injury severity. The proposed framework explicitly links a count data model (to model crash frequency) with a discrete choice model (to model combinations of crash type and injury severity), and uses a multinomial probit kernel for the discrete choice model and introduces unobserved heterogeneity in both the crash frequency model and the discrete choice model, while also accommodates excess of zeros. The results show that the type of traffic control and the number of entering roads are the most important determinants of crash counts and crash type/injury severity, and the results from our analysis underscore the value of our proposed model for data fit purposes as well as to accurately estimate variable effects. / text

Les recherches en sécurité routière suscitent largement l'intérêt des chercheurs. Indépendamment des techniques de modélisation, un facteur important d'imprécision -qui caractérise les études dans ce domaine- concerne le niveau d'agrégation des données. Aujourd'hui, la plupart des autoroutes sont équipées de systèmes permanents de surveillance qui fournissent des données désagrégées. Dans ce contexte, l'objectif de la thèse est d'exploiter les données trafic recueillies en temps réel au moment des accidents, afin d'élargir le champ des travaux précédents et de mettre en évidence un potentiel d'applications innovantes. À cette fin, nous examinons les effets du trafic sur le type d'accident ainsi que sur la gravité subie par les occupants des véhicules, tout en tenant compte des facteurs environnementaux et géométriques. Des modèles Probit sont appliqués aux données de trafic et d'accidents enregistrés pendant quatre années sur le tronc commun aux autoroutes A4 et A86 en Ile-de-France. Les résultats empiriques indiquent que le type d'accident peut être presque exclusivement défini par les conditions de trafic prévalant peu avant son occurrence. En outre, l'augmentation du débit s'avère exercer un effet constamment positif sur la gravité, alors que la vitesse exerce un effet différentiel sur la gravité en fonction des conditions d'écoulement. Nous établissons ensuite un cadre conceptuel pour des applications de gestion des incidents qui s'appuie sur les données trafic recueillies en temps réel. Nous utilisons les résultats de la thèse afin d'explorer des implications qui ont trait à la propension et à la détection des incidents, ainsi qu'à l'amélioration de leur gestion / Research on road safety has been of great interest to engineers and planners for decades. Regardless of modeling techniques, a serious factor of inaccuracy - in most past studies - has been data aggregation. Nowadays, most freeways are equipped with continuous surveillance systems making disaggregate traffic data readily available ; these have been used in few studies. In this context, the main objective of this dissertation is to capitalize highway traffic data collected on a real-time basis at the moment of accident occurrence in order to expand previous road safety work and to highlight potential further applications. To this end, we first examine the effects of various traffic parameters on type of road crash as well as on the injury level sustained by vehicle occupants involved in accidents, while controlling for environmental and geometric factors. Probit models are specified on 4-years of data from the A4-A86 highway section in the Ile-de -France region, France. Empirical findings indicate that crash type can almost exclusively be defined by the prevailing traffic conditions shortly before its occurrence. Increased traffic volume is found to have a consistently positive effect on severity, while speed has a differential effect on severity depending on flow conditions. We then establish a conceptual framework for incident management applications using real-time traffic data on urban freeways. We use dissertation previous findings to explore potential implications towards incident propensity detection and enhanced management

Southeastern states have about 26 percent of the nations total fatalities, and are about 24 percent above the national mean over recent years. Descriptive statistics, graphs, and figures are used to illustrate and quantify the crash trends, which depict a comprehensive picture of status and trends of the fatal crashes in southeastern states. The severity of crashes is studied as a function of characteristics of the person involved in the crash, vehicle, traffic condition, physical road geometry, and environmental factors. Detailed geometric feature data were collected for this study, which makes it possible to investigate the relationship between geometric features and crash severity. This study identifies causal factors contributing to the high fatality rate in southeastern states, and sheds light on the differences and similarities among these states for reducing the severity of fatal crashes, by developing multinomial logit models to explain the severity and type of fatal crashes.

Research on road safety has been of great interest to engineers and planners for decades. Regardless of modeling techniques, a serious factor of inaccuracy - in most past studies - has been data aggregation. Nowadays, most freeways are equipped with continuous surveillance systems making disaggregate traffic data readily available ; these have been used in few studies. In this context, the main objective of this dissertation is to capitalize highway traffic data collected on a real-time basis at the moment of accident occurrence in order to expand previous road safety work and to highlight potential further applications. To this end, we first examine the effects of various traffic parameters on type of road crash as well as on the injury level sustained by vehicle occupants involved in accidents, while controlling for environmental and geometric factors. Probit models are specified on 4-years of data from the A4-A86 highway section in the Ile-de -France region, France. Empirical findings indicate that crash type can almost exclusively be defined by the prevailing traffic conditions shortly before its occurrence. Increased traffic volume is found to have a consistently positive effect on severity, while speed has a differential effect on severity depending on flow conditions. We then establish a conceptual framework for incident management applications using real-time traffic data on urban freeways. We use dissertation previous findings to explore potential implications towards incident propensity detection and enhanced management

Movements of cyclists and m.otor vehicles at intersections involve a wide variety of potential conflicting interactions. In Australia, the high numbers of motor vehicles, particularly light passenger vehicles, mixed with cyclists results in many bicycle-light passengervehicle (LPV) crashes (3,135 crashes during 2002-2014). About 68% of cyclist deaths at Australian intersections in 2016 were due to crashes between bicycles and LPVs (DITRLDG, 2016). The high number ofLPV crashes among fatalities among cyclists is an increasing safety concem. When an LPV collides with a cyclist, the resulting impact forces in.tluence the probability of cyclist injury severity outcom.e. Therefore, the goa1 at intersections should be to understand whether and which particular crash patterns are more injurious, in order to better inform approaches to reduce the impact forces to levels that do not result in severe injury outcomes. To examine how crash pattem (or mechanism) influences the injury severity of cyclists in bicycle-motor vehicle crashes at intersections, researchers typically describe the crash mechanism in terms of crash types, such as angle crashes, head--on crashes, rear-end crashes, and sideswipe crashes (e.g., Kim et al., 2007; Pai, 2011 ). While crash types explain crash mechanisms to some extent, this study hypothesiz.es that the trajectories of the crash involved vehicles may provide additional information because they better capture the movements of the vehicles prior to collision. Furthermore, it is argued that injury pattem might be in.tluenced by vehicle travel direction and manoeuvre (Isaksson-Hellman and Wemeke, 2017). For example, when a car is moving straight ahead it is likely to have a higher speed than when it is turning, and if cyclists are struck at a higher impact speed, they tend to sustain more severe injury (Badea-Romero and Lenard, 2013). While many studies have evaluated the association between cyclist injwy severity and crash types, the factors that might influence cyclist injury severity related to trajectory types (vehicle movement and travel direction) have not yet been thoroughly investigated. This study aims to examine the factors associated with cyclists' injury severity for 'trajectory types• compared with the typically used 'crash types' at intersections.