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.

As one of the major data analysis methods, statistical models play an important role in traffic safety analysis. A common situation associated with crash data is the phenomenon known as overdispersion which has been discussed and investigated frequently in recent years. As such, researchers have proposed several models, such as the Poisson Gamma (PG) or Negative Binomial (NB), the Poisson-lognormal, or the Poisson-Weibull, to handle the overdispersion. Unfortunately, very few models have been proposed for specifically analyzing the sources of dispersions in the data. Better understanding of sources of variation and overdispersion could help in managing safety, such as establishing relationships and applying appropriate treatments or countermeasures, more efficiently. Given the limitations of existing models for exploring the source of overdispersion of crash data, this research examined a new model function that could be applied to explore sources of extra variability through the use of the Generalized Waring (GW) models. This model, which was recently introduced by statisticians, divides the observed variability into three components: randomness, internal differences between road segments or intersections, and the variances caused by other external factors that have not been included as covariates in the model. To evaluate these models, GW models were examined using both simulated and empirical crash datasets, and the results were compared to the most commonly used NB model and the recently developed NB-Lindley models. For model parameter estimation, both the maximum likelihood method and a Bayesian approach were adopted for better comparison. A simulation study was used to show the better performance of this model compared to NB model for overdispersed data, and then an application in the empirical crash data illustrates its capability of modeling data sets with great accuracy and exploring the source of overdispersion. The performances of hotspot identification for these two kinds of models (i.e., GW models and NB models) were also examined and compared based on the estimated models from the empirical dataset. Finally, bias properties related to the choice of prior distributions for parameters in GW model were examined by using a simulation study. In addition, the suggestions on the choice of minimum sample size and priors were presented for different kinds of datasets.

La prise de conscience de l’implication des médicaments dans la genèse des accidents de la route date d’une vingtaine d’années. Les médicaments psycho-actifs peuvent altérer les capacités de conduite par leur action sur le système nerveux (par exemple, un effet sédatif le lendemain d’une prise d’hypnotique). D’autres médicaments sont susceptibles d’affecter les fonctions psychomotrices par leur action sur les fonctions physiologiques (tel que les hypoglycémies liées à un traitement antidiabétique). L’étude CESIR-A a été mise en place pour contribuer à la connaissance du lien épidémiologique entre médicaments et accidents de la route. L’étude utilise trois bases de données françaises : le Système National d’Information Inter-Régimes de l’Assurance Maladie (SNIIR-AM), les Procès Verbaux d’accidents (PV) et les Bulletins d’Analyse des Accidents Corporels de la circulation (BAAC). L’appariement de ces données a conduit à l’inclusion de 72,685 conducteurs impliqués dans un accident corporel sur la période juillet 2005-mai 2008. L’analyse a été réalisée grâce à deux méthodes: une analyse cas-témoin comparant les responsables aux non-responsables des accidents et une analyse dite en case-crossover. Les périodes d’exposition aux médicaments ont été estimées à partir des dates de délivrances de médicaments prescrits, puis remboursés par l’assurance maladie. L’étude des médicaments regroupés selon les quatre niveaux de risque sur la conduite définis par l’Agence Française de Sécurité Sanitaire des Produits de Santé (AFSSAPS) [du niveau 0 (pas de risque) au niveau 3 (risque élevé)], a montré que les utilisateurs de médicaments prescrits de niveau 2 et de niveau 3 ont un risque significativement plus élevé d’être responsables de leur accident (OR=1,31 [1,24-1,40] et OR=1,25 [1,12-1,40], respectivement). La fraction de risque attribuable à l’utilisation de ces médicaments était de 3,3% [2,7%-3,9%]. Le risque d’être responsable d’un accident était augmenté chez les utilisateurs de zolpidem (OR=1,28 [1,07-1,53]) mais pas chez les utilisateurs de zopiclone ou de benzodiazépines hypnotiques. Plus particulièrement, ce risque était augmenté chez les 139 conducteurs ayant eu plus d’un comprimé de zolpidem délivré par jour au cours des cinq mois précédant l’accident (OR=2,38 [1,61-3,52]). L’analyse case-crossover a mis en évidence un sur-risque d’accident de la route chez les utilisateurs de benzodiazépines hypnotiques seulement (OR=1,42 [1,09-1,85]). Les conducteurs exposés aux hypnotiques partagent les mêmes caractéristiques au regard du type d’accident, qui survenaient plus fréquemment sur autoroute. Dans notre base de données, 196 conducteurs ont été exposés à la buprénorphine et/ou à la méthadone, le jour de leur accident. Cette population spécifique était jeune, essentiellement masculine, avec d’importantes co-consommations, notamment d’alcool de médicaments de niveau 3. Les conducteurs exposés à la buprénorphine et/ou à la méthadone présentaient un risque accru d’être responsables de leur accident (OR= 2,19 [1,51-3,16]). Notre étude fournit des informations importantes sur la contribution des médicaments au risque d’accident de la route. D’après nos résultats, la classification de l’AFSSAPS semble appropriée concernant les médicaments de niveaux 2 et 3. Les sur-risques d’être responsable d’un accident chez les exposés au zolpidem ou aux traitements de substitution pourraient être liés, au moins en partie, au comportement à risque de ces conducteurs. L’amélioration du comportement des conducteurs représente un des défis pour la sécurité routière. L’objectif de la classification française et de la signalétique apposée sur les boîtes de médicaments est donc de fournir aux patients une information appropriée sur les effets des médicaments sur leur capacité de conduite. / In recent decades, attention has been increasingly focused on the impact of disabilities and medicinal drug use on road safety. Psychoactive medicines may impair driving abilities due to their action on the central nervous system (e.g. sedation in the morning following administration of a hypnotic), while other medicines may affect psychomotor functions by their action on physiological functions (e.g hypoglycaemic seizures related to diabetic treatment). The CESIR-A project was set up to improve the epidemiological knowledge on medicines and the risk of road traffic crashes. The study matched three French nationwide databases: the national healthcare insurance database, police reports, and the police national database of injurious crashes, leading to the inclusion of 72,685 drivers involved in an injurious road traffic crash from July 2005 to May 2008. Two methods were performed for data analysis: a case-control analysis in which cases where responsible drivers and controls non-responsible ones and a case-crossover analysis. Medicine exposures were estimated from prescription drug dispensations in the healthcare reimbursement database. The study of medicines grouped according to the four levels of driving impairment risk of the French classification system [from 0 (no risk) to 3 (high risk)], showed that users of level 2 and level 3 prescribed medicines were at higher risk of being responsible for the crash (OR=1.31 [1.24-1.40] and OR=1.25 [1.12-1.40], respectively). The fraction of road traffic crashes attributable to levels 2 and 3 medicines was 3.3% [2.7%-3.9%]. Zolpidem use was associated with an increased risk of being responsible for a road traffic crash (OR=1.28 [1.07-1.53]) whereas use of zopiclone and benzodiazepine hypnotics use was not. Responsibility risk was only increased in the 139 drivers with dispensing of more than one pill of zolpidem a day during the five months before the crash (OR=2.38 [1.61-3.52]). Case-crossover analysis showed an increased risk of crash for benzodiazepine hypnotic users only (OR=1.42 [1.09-1.85]). Hypnotic users shared similar crash characteristics, with crashes more likely to occur on highways. In our database, 196 drivers were exposed to buprenorphine and/or methadone on the day of crash. This specific population was young, essentially males, with important co-consumption of other substances, in particular alcohol and level 3 medicines. Injured drivers exposed to buprenorphine and/or methadone on the day of crash, had an increased risk of being responsible (OR=2.19 [1.51-3.16]). The case cross-over analysis did not demonstrate any association (OR=1.26 [0.93 - 1.70]). Our study provides evidence of the contribution of medicines to the risk of road traffic crashes. According to our results, the French risk classification seems relevant regarding medicines classified as levels 2 and 3 of risk for road traffic crashes. The observed increased risks of being responsible for a crash for zolpidem and substitution maintenance treatment users may be linked to risky behaviors. Improving driver behaviour is one of the challenges for road safety. Providing patients with proper information on the potential effect of medicines on their driving abilities is the main objective of drug and risk classifications such as the French one.

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.

No description available.

No description available.

<p>This thesis is devoted to understanding and counteracting the primary contributing factor in traffic crashes: inattention. Foremost, it demonstrates the fundamental importance of proactive gaze in the road centre area for action guidance in driving. Inattention is explained with regard to two visual functions (vision-for-action and vision-for-identification), three forms of attentional selection (action-driven-, stimulus-driven-, and goal-directed attention), and two forms of prediction influences (extrapolation-based- and decision-based prediction influences). In Study I an automated eye-movement analysis method was developed for a purpose-built eye-tracking sensor, and was successfully validated. This analysis method was further developed, and several new measures of gaze concentration to the road centre area were created. Study II demonstrated that a sharp decrease in the amount of road centre viewing time is accompanied by a dramatic spatial concentration towards the road centre area in returning gaze during visual tasks. During cognitive tasks, a spatial gaze concentration to road centre is also evident; however contrary to visual tasks, road centre viewing time is increased because the eyes are not directed towards an object within the vehicle. Study III found that gaze concentration measures are highly sensitive to driving task demands as well as to visual and auditory in-vehicle tasks. Gaze concentration to the road centre area was found as driving task complexity increased, as shown in differences between rural curved- and straight sections, between rural and motorway road types, and between simulator and field motorways. Further, when task duration was held constant and the in-vehicle visual task became more difficult, drivers looked less at the road centre area ahead, and looked at the display more often, for longer periods, and for more varied durations. In closing, it is shown how this knowledge can be applied to create in-vehicle attention support functions that counteract the effects of inattention.</p>

This thesis is devoted to understanding and counteracting the primary contributing factor in traffic crashes: inattention. Foremost, it demonstrates the fundamental importance of proactive gaze in the road centre area for action guidance in driving. Inattention is explained with regard to two visual functions (vision-for-action and vision-for-identification), three forms of attentional selection (action-driven-, stimulus-driven-, and goal-directed attention), and two forms of prediction influences (extrapolation-based- and decision-based prediction influences). In Study I an automated eye-movement analysis method was developed for a purpose-built eye-tracking sensor, and was successfully validated. This analysis method was further developed, and several new measures of gaze concentration to the road centre area were created. Study II demonstrated that a sharp decrease in the amount of road centre viewing time is accompanied by a dramatic spatial concentration towards the road centre area in returning gaze during visual tasks. During cognitive tasks, a spatial gaze concentration to road centre is also evident; however contrary to visual tasks, road centre viewing time is increased because the eyes are not directed towards an object within the vehicle. Study III found that gaze concentration measures are highly sensitive to driving task demands as well as to visual and auditory in-vehicle tasks. Gaze concentration to the road centre area was found as driving task complexity increased, as shown in differences between rural curved- and straight sections, between rural and motorway road types, and between simulator and field motorways. Further, when task duration was held constant and the in-vehicle visual task became more difficult, drivers looked less at the road centre area ahead, and looked at the display more often, for longer periods, and for more varied durations. In closing, it is shown how this knowledge can be applied to create in-vehicle attention support functions that counteract the effects of inattention.

In roadway projects, design exceptions are implemented when the project site consists of one or more substandard design elements. The objective of this thesis is to conduct an initial investigation for a monitoring program for the safety performance of design exceptions in Georgia. A total of 467 projects containing design exceptions were reported in Georgia from 1995 to 2011, and from this crash data for 179 projects from 2003 to 2008 were sampled. The crash data collected in this research pertains to all roadway segments within the projects and is not necessarily related to the design exceptions. Future efforts will be required to explore potential connections between the crash rates and design exceptions. The annual crash results generally revealed a high variability and randomness in the data. For this reason, the average 3-year crash frequencies before design exception approval date and after it were calculated to determine the safety performance for projects containing design exceptions. A method for determining expected results using the Highway Safety Manual (HSM) predictive method is also discussed. The findings will be used to guide future research on design exceptions and mitigation measures to improve roadway safety.