This thesis presents a case study of the drink drive initiatives, including marketing communications, legislation and enforcement practices implemented in the state of Victoria (Australia) between 1989-2000. It has been argued that the 51% reduction in road toll was related to these initiatives. In order to explore the veracity of these claims, a holistic case study approach was adopted. In addition to an examination of the communications tactics and extant practices of enforcement agencies, the study involved interviews with two distinct groups: professionals in various fields pertaining to road safety, and members of the general Victorian driving community. The focus of this work was on the advertising and communications campaigns that were run by the Transport Accident Commission from 1989-2000; however the policy and enforcement initiatives were also examined in terms of their potential impact on the lowering of the road toll. Suggestions for the improvement of policy and communication strategies within a social marketing context are made.

The aim of this thesis was to examine the extent, and correlates, of self-regulation of driving behaviour among a sample of South Australian older drivers (aged 60 or more). The first of four studies was an analysis of official crash statistics in South Australia over a period of five years. The patterns of crash involvement for South Australian older drivers were found to resemble those reported in the literature for other jurisdictions. Lower levels of crash involvement for older drivers in difficult driving situations (peak hour, rain, darkness) were interpreted as indirect evidence for self-regulation of driving behaviour. The second study involved pilot testing a measure developed specifically for assessing the visual attention of older adults (the Computerised Visual Attention Test - CVAT). The CVAT assesses visual attention by measuring target detection and reaction time for central and peripheral stimuli, and in conditions requiring selective and divided attention. The third study involved assessing the test-retest reliability, construct validity and predictive validity of the CVAT. It was concluded that the CVAT is a reliable measure of abilities including, but not restricted to, attention, and that it is correlated with on-road driving ability. The fourth study involved an examination of the driving behaviour and attitudes of 104 drivers aged over 60, with avoidance of difficult driving situations providing an index of self-regulation. These drivers also completed a battery of tests measuring psychological factors, vision, physical functioning, various cognitive abilities, and attention (the CVAT). Ninety participants additionally completed an on-road assessment of driving ability. It was found that older drivers most often avoided reverse parallel parking and driving at night in the rain, while driving alone was avoided least often. Measures of visual attention, medication use and visual acuity were most predictive of levels of self-regulation, while poorer driving ability was only associated with avoidance of a small number of specific situations. Functional deficits related to poorer driving ability but not to self-regulation included poorer contrast sensitivity, speed of information processing and spatial memory. Such deficits could identify drivers who may need to restrict their driving more than they do. / Thesis (Ph.D.)--Department of Psychology, 2004.

<p>The driving simulator is a vital tool for much of the research performed at theSwedish National Road and Transport Institute (VTI). Currently VTI posses three driving simulators, two high fidelity simulators developed and constructed by VTI, and a medium fidelity simulator from the German company Dr.-Ing. Reiner Foerst GmbH. The two high fidelity simulators run the same simulation software, developed at VTI. The medium fidelity simulator runs a proprietary simulation software. At VTI there is a wish to integrate the medium fidelity Foerst Trainer simulator hardware into the VTI simulation software environment. This would increase research, development and maintanance flexibility and simulator availability since development and research could be performed on one additional simulator. Anintegration would lead to a homogenous software environment that also decreasesdevelopment, maintanance and training costs.To integrate the Foerst Trainer simulator and the VTI simulation software to communicate a program that translates and relays input and output between the two was developed. An assessment of the hardware-software integration was performed through an experiment where the high fidelity Simulator 3 and the medium fidelity Foerst Trainer simulator were compared. The experiment was designed to measure the participants driving performances and the perceived realism of the simulator. The results of the experiment shows that there is suprisingly small differences between the simulators, but more research is needed for more conclusive results.</p>

<p>It is important to detect faults in bus details at an early stage. Because the driving style affects the breakdown of different details in the bus, identification of the driving style is important to minimize the number of failures in buses.</p><p>The identification of the driving style of the driver was based on the input data which contained examples of the driving runs of each class. K-nearest neighbor and neural networks algorithms were used. Different models were tested.</p><p>It was shown that the results depend on the selected driving runs. A hypothesis was suggested that the examples from different driving runs have different parameters which affect the results of the classification.</p><p>The best results were achieved by using a subset of variables chosen with help of the forward feature selection procedure. The percent of correct classifications is about 89-90 % for the k-nearest neighbor algorithm and 88-93 % for the neural networks.</p><p>Feature selection allowed a significant improvement in the results of the k-nearest neighbor algorithm and in the results of the neural networks algorithm received for the case when the training and testing data sets were selected from the different driving runs. On the other hand, feature selection did not affect the results received with the neural networks for the case when the training and testing data sets were selected from the same driving runs.</p><p>Another way to improve the results is to use smoothing. Computing the average class among a number of consequent examples allowed achieving a decrease in the error.</p>

The Type-II dilemma zone refers to the segment of roadway approaching an intersection where drivers have difficulty deciding to stop or proceed through at the onset of the circular yellow (CY) indication. Signalized intersection safety can be improved when the dilemma zone is correctly identified and steps are taken to reduce the likelihood that vehicles are caught in it. This research employs driving simulation as a means to collect driver response data at the onset of the CY indication to better understand and describe the dilemma zone. The data obtained was compared against that from previous experiments documented in the literature and the evidence suggests that driving simulator data is valid for describing driver behavior under the given conditions. Fuzzy logic was proposed as a tool to model driver behavior in the dilemma zone, and three such models were developed to describe driver behavior as it relates to the speed and position of the vehicle. These models were shown to be consistent with previous research on this subject and were able to predict driver behavior with up to 90% accuracy. / Graduation date: 2013

Thesis (Ph.D.)--North Carolina State University, 2005. / Includes vita. Includes bibliographical references (p. 109-113). Also available online via the North Carolina State University Libraries website (http://www.lib.ncsu.edu/).

The purpose of this thesis was to improve the understanding of what cognitive functions are important for driving performance, investigate the impact of impaired cognitive functions on drivers with brain injury, and study adaptation strategies relevant for driving performance after brain injury. Finally, the predictive value of a neuropsychological test battery was evaluated for driving performance. Main results can be summarized in the following conclusions: (a) Cognitive functions in terms of attentional and dynamic working memory-related functions are relevant for driving performance. (b) Neuropsychological impairments in information processing speed, divided and focused attention, requiring working memory, are associated to limitations in driving performance. In addition, qualitative aspects of driving problems especially impaired anticipatory attention appeared to constrain driving performance. (c) A neuropsychological test battery assessing speed of information processing and attention in terms of working memory predicted driving performance. In addition, cognitive factors are relevant for interpretation of driving problems qualitatively. (d) Driving speed adjustment and anticipatory attention were adaptive strategies for driving after brain injury. Interest in driving, motivation for driving safely, and driving experience appeared also relevant for driving after brain injury. (e) Collaboration between medical, neuropsychological and driving expertise is recommended for a total evaluation of driving performance after brain injury. Anticipatory attention was considered a working memory based attentional system, directing the processing resources flexibly and appropriately between the different information processing components. Thus, anticipatory attention demonstrated qualitatively that working memory is a prominent function in a real driving context.

It is important to detect faults in bus details at an early stage. Because the driving style affects the breakdown of different details in the bus, identification of the driving style is important to minimize the number of failures in buses. The identification of the driving style of the driver was based on the input data which contained examples of the driving runs of each class. K-nearest neighbor and neural networks algorithms were used. Different models were tested. It was shown that the results depend on the selected driving runs. A hypothesis was suggested that the examples from different driving runs have different parameters which affect the results of the classification. The best results were achieved by using a subset of variables chosen with help of the forward feature selection procedure. The percent of correct classifications is about 89-90 % for the k-nearest neighbor algorithm and 88-93 % for the neural networks. Feature selection allowed a significant improvement in the results of the k-nearest neighbor algorithm and in the results of the neural networks algorithm received for the case when the training and testing data sets were selected from the different driving runs. On the other hand, feature selection did not affect the results received with the neural networks for the case when the training and testing data sets were selected from the same driving runs. Another way to improve the results is to use smoothing. Computing the average class among a number of consequent examples allowed achieving a decrease in the error.

No description available.

This research investigated the association of street network connectivity differences across travel modes with travel behaviour – mode choice, distance traveled and number of trips. To date research on travel behaviour relationships with urban form has not developed empirical evidence on street designs as distinct networks for walking and driving. A street network having greater connectivity and continuity for the pedestrian mode of travel vis-à-vis the vehicular network, like the Fused Grid, will likely encourage more walking. This hypothesis was investigated using a quasi-experimental approach within a rational utility behavioural framework. Local travel behaviour is theorized to be affected by desire to access goods and services (broadly termed, ‘activities’) in the community where people live. Using inferential statistics, the research tested for relationships between measured street patterns and self-reported local travel by King County, WA households. The main variables were ratios (walking : driving) of network connectivity and density, in the vicinity of travel survey households. Demographics and household characteristics, as well as other behaviourally influential urban form factors (residential density, proximity of destinations, etc.), were included in regression models, allowing control for confounding factors. Findings suggest that street networks with connectivity that provides better routing for one mode of transportation over others encourage more travel by the favored mode. The regression model demonstrated that a change from a pure small-block grid to a modified grid (i.e. Fused Grid) can result in an 11.3% increase in odds of a home-based trip being walked. The modified street pattern like a Fused Grid is also associated with a 25.9% increase, over street patterns with equivalent route directness for walking and driving, in the odds a person will meet recommended levels of physical activity. Finally, the Fused Grid’s 10% increase in relative connectivity for pedestrians is associated with a 23% decrease in local vehicle travel distance (VMT), and its improved continuity is associated with increased walking trips and distance. Conclusions: Other factors being equal, residential street networks with either more direct routing for pedestrians or more pedestrian facilities relative to vehicular network are associated with improved odds of walking and reduced odds of driving.