943 resultados para Driver violations
Resumo:
The Centre for Accident Research and Road Safety – Queensland (CARRS-Q) is conducting a 3-year program of research, titled Integrating Technological and Organisational Approaches to Enhance the Safety of Roadworkers. The program is funded by the Australian Research Council (ARC), with support from industry partners Leighton Contractors, GHD, Queensland Transport and Main Roads (TMR), and the Australian Workers Union (AWU). This multidisciplinary project involves working together to enhance roadworker safety by: • Investigating the real and perceived dangers at roadworks. • Strengthening organisational policies and practices for roadworker safety. • Testing innovative initiatives to improve driver behaviour at roadworks. • Developing safety management models spanning different regulatory frameworks. The project outcomes will include the following benefits: • Practical and theoretical contributions at industry and academic levels for developing effective interventions/strategies to improve safety in road construction. • Development of new measures to evaluate effectiveness of policy and organisational interventions to produce behavioural change among organisations involved in roadworks. • Improved safety and productivity in urban and rural areas of Australia as a result of facilitating the delivery of road improvements. This paper presents an overview of the research conducted to date as part of the overall program. The paper concentrates on issues relevant to moving vehicles, although the research recognises the importance of other hazards and risks associated with roadworks and construction projects generally.
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Common method variance (CMV) has received little attention within the field of road safety research despite a heavy reliance on self-report data. Two surveys were completed by 214 motorists over a two-month period, allowing associations between social desirability and key road safety variables and relationships between scales across the two survey waves to be examined. Social desirability was found to have a strong negative correlation with the Driver Behaviour Questionnaire (DBQ) sub-scales as well as age, but not with crashes and offences. Drivers who scored higher on the social desirability scale were also less likely to report aberrant driving behaviours as measured by the DBQ. Controlling for social desirability did not substantially alter the predictive relationship between the DBQ and the crash and offences variables. The strength of the correlations within and between the two waves were also compared with the results strongly suggesting that effects associated with CMV were present. Identification of CMV would be enhanced by the replication of this study with a larger sample size and comparing self-report data with official sources.
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This study aims to examine the severe consequences of traffic crashes related to a special group of drivers in China, which is called the “Second Rich & Powerful Generation” (SRPG). The unique driving behaviors and attitudes of this special group are intertwined with the general cultural and social environment in China. To investigate the difference of traffic crash consequences between drivers who belong to SRPG and general driver population, injuries and fatalities in 2009, 2010, 2011 and 2012 were compared. Results consistently showed that while no significant difference was detected between these two groups in terms of injuries, fatalities per crash caused by SRPG were significantly larger than that caused by general driver population. Findings from our study clearly demonstrate the complexity of road safety issues in developing countries, and can also be used to develop road safety improvement strategies tailored to SRPG.
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A firm’s business model (BM) is an important driver of its relative performance. Constructive adaptation to elements of the BM can therefore sustain the position in light of changing conditions. This study takes a configurational approach to understanding drivers of business model adaptation (BMA) in new ventures. We investigate the effect of human capital, social capital, and technological environment on BMA. We find that a universal, direct effects, analysis can provide useful information, but also risks painting a distorted picture. Contingent, two-way interactions add further explanatory power, but configurational models combining elements of all three (internal resource, external activities, environment) are superior.
Resumo:
Driver sleepiness is a major contributor to road crashes. The current study sought to examine the association between perceptions of effectiveness of six sleepiness countermeasures and their relationship with self-reports of continuing to drive while sleepy among 309 drivers after controlling for the influence of age, sex, motivation for driving sleepy, and risk perception of sleepy driving. The results demonstrate that the variables of age, sex, motivation, and risk perception were significantly associated with self-reports of continuing to drive while sleepy and only one countermeasure was associated with self-reports of continuing to drive while sleepy. Further, it was found that age differences in self-reports of continuing to drive while sleepy was mediated by participants’ motivation and risk perception. These findings highlight modifiable factors that could be focused on with interventions that seek to modify drivers’ attitudes and behaviours of driving while sleepy.
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Vehicle traffic through roadwork sites creates a hazardous work environment, with speed limit noncompliance a major contributor to the high risk and high severity of roadwork crashes. This paper examines responses to an online survey to better understand the factors underlying drivers’ work zone speed choices. Drivers’ stated speed choice was compared between two photographs of the same work zone section – one with workers and machinery present and another with no visible activity. Drivers also provided comments on any aspect of roadwork safety they thought was important. A paired t-test of stated speed choice revealed that significantly lower mean speeds were nominated when workers and machinery were clearly present and active (41.7 vs 53.5 km/h, p<0.01). Participants expressed concern about roadwork signage and reduced speed limits being left in place when there was no apparent work activity. Driver perceptions, and thus compliance, may be improved through technological and operational changes.
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Collisions between different types of road users at intersections form a substantial component of the road toll. This paper presents an analysis of driver, cyclist, motorcyclist and pedestrian behaviour at intersections that involved the application of an integrated suite of ergonomics methods, the Event Analysis of Systemic Teamwork (EAST) framework, to on-road study data. EAST was used to analyse behaviour at three intersections using data derived from an on-road study of driver, cyclist, motorcyclist and pedestrian behaviour. The analysis shows the differences in behaviour and cognition across the different road user groups and pinpoints instances where this may be creating conflicts between different road users. The role of intersection design in creating these differences in behaviour and resulting conflicts is discussed. It is concluded that currently intersections are not designed in a way that supports behaviour across the four forms of road user studied. Interventions designed to improve intersection safety are discussed.
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Objectives: The purpose of this study was to investigate the characteristics associated with fatal and non-fatal low-speed vehicle run-over (LSVRO) events in relation to person, incident and injury characteristics, in order to identify appropriate points for intervention and injury prevention. Methods: Data on all known LSVRO events in Queensland, Australia, over 11 calendar years (1999–2009) were extracted from five different databases representing the continuum of care ( prehospital to fatality) and manually linked. Descriptive and multivariate analyses were used to analyse the sample characteristics in relation to demographics, health service usage, outcomes, incident characteristics, and injury characteristics. Results: Of the 1641 LSVRO incidents, 98.4% (n=1615) were non-fatal, and 1.6% were fatal (n=26). Over half the children required admission to hospital (56%, n=921); mean length of stay was 3.4 days. Younger children aged 0–4 years were more frequently injured, and experienced more serious injuries with worse outcomes. Patterns of injury (injury type and severity), injury characteristics (eg, time of injury, vehicle type, driver of vehicle, incident location), and demographic characteristics (such as socioeconomic status, indigenous status, remoteness), varied according to age group. Almost half (45.6%; n=737) the events occurred outside major cities, and approximately 10% of events involved indigenous children. Parents were most commonly the vehicle drivers in fatal incidents. While larger vehicles such as four-wheel drives (4WD) were most frequently involved in LSVRO events resulting in fatalities, cars were most frequently involved in non-fatal events. Conclusions: This is the first study, to the authors’ knowledge, to analyse the characteristics of fatal and non-fatal LSVRO events in children aged 0–15 years on a state-wide basis. Characteristics of LSVRO events varied with age, thus age-specific interventions are required. Children living outside major cities, and indigenous children, were over-represented in these data. Further research is required to identify the burden of injury in these groups.
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Over the past decades there has been a considerable development in the modeling of car-following (CF) behavior as a result of research undertaken by both traffic engineers and traffic psychologists. While traffic engineers seek to understand the behavior of a traffic stream, traffic psychologists seek to describe the human abilities and errors involved in the driving process. This paper provides a comprehensive review of these two research streams. It is necessary to consider human-factors in {CF} modeling for a more realistic representation of {CF} behavior in complex driving situations (for example, in traffic breakdowns, crash-prone situations, and adverse weather conditions) to improve traffic safety and to better understand widely-reported puzzling traffic flow phenomena, such as capacity drop, stop-and-go oscillations, and traffic hysteresis. While there are some excellent reviews of {CF} models available in the literature, none of these specifically focuses on the human factors in these models. This paper addresses this gap by reviewing the available literature with a specific focus on the latest advances in car-following models from both the engineering and human behavior points of view. In so doing, it analyses the benefits and limitations of various models and highlights future research needs in the area.
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Road construction and maintenance activities present challenges for ensuring the safety of workers and the traveling public alike. Hazards in work zones are typically studied using historical crash records but the current study took a qualitative approach by interviewing 66 workers from various work zones in Queensland, Australia. This supplemented and enhanced the limited available data regarding the frequency and nature of work zone crashes in Australia, provided worker insights into contributing factors, and assessed their opinions on the likely effectiveness of current or future approaches to hazard mitigation. Workers may not be aware of objective data regarding effectiveness, but their attitudes and consequent levels of compliance can influence both the likelihood of implementation and the outcomes of safety measures. Despite the potential importance of worker perceptions, they have not been studied comprehensively to date, and thus this study fills a significant gap in the literature. Excessive vehicle speeds, driver distraction and aggression towards roadworkers, working in wet weather, at night and close to traffic stream were among the most common hazards noted by workers. The safety measures perceived to be most effective included police presence, active enforcement, and improving driver awareness and education about work zones. Worker perceptions differed according to their level of exposure to hazards.
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This paper reports profiling information for speeding offenders and is part of a larger project that assessed the deterrent effects of increased speeding penalties in Queensland, Australia, using a total of 84,456 speeding offences. The speeding offenders were classified into three groups based on the extent and severity of an index offence: once-only low-rang offenders; repeat high-range offenders; and other offenders. The three groups were then compared in terms of personal characteristics, traffic offences, crash history and criminal history. Results revealed a number of significant differences between repeat high-range offenders and those in the other two offender groups. Repeat high-range speeding offenders were more likely to be male, younger, hold a provisional and a motorcycle licence, to have committed a range of previous traffic offences, to have a significantly greater likelihood of crash involvement, and to have been involved in multiple-vehicle crashes than drivers in the other two offender types. Additionally, when a subset of offenders’ criminal histories were examined, results revealed that repeat high-range speeding offenders were also more likely to have committed a previous criminal offence compared to once only low-range and other offenders and that 55.2% of the repeat high-range offenders had a criminal history. They were also significantly more likely to have committed drug offences and offences against order than the once only low-range speeding offenders, and significantly more likely to have committed regulation offences than those in the other offenders group. Overall, the results indicate that speeding offenders are not an homogeneous group and that, therefore, more tailored and innovative sanctions should be considered and evaluated for high-range recidivist speeders because they are a high-risk road user group.
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This report presents the final deliverable from the project titled Conceptual and statistical framework for a water quality component of an integrated report card’ funded by the Marine and Tropical Sciences Research Facility (MTSRF; Project 3.7.7). The key management driver of this, and a number of other MTSRF projects concerned with indicator development, is the requirement for state and federal government authorities and other stakeholders to provide robust assessments of the present ‘state’ or ‘health’ of regional ecosystems in the Great Barrier Reef (GBR) catchments and adjacent marine waters. An integrated report card format, that encompasses both biophysical and socioeconomic factors, is an appropriate framework through which to deliver these assessments and meet a variety of reporting requirements. It is now well recognised that a ‘report card’ format for environmental reporting is very effective for community and stakeholder communication and engagement, and can be a key driver in galvanising community and political commitment and action. Although a report card it needs to be understandable by all levels of the community, it also needs to be underpinned by sound, quality-assured science. In this regard this project was to develop approaches to address the statistical issues that arise from amalgamation or integration of sets of discrete indicators into a final score or assessment of the state of the system. In brief, the two main issues are (1) selecting, measuring and interpreting specific indicators that vary both in space and time, and (2) integrating a range of indicators in such a way as to provide a succinct but robust overview of the state of the system. Although there is considerable research and knowledge of the use of indicators to inform the management of ecological, social and economic systems, methods on how to best to integrate multiple disparate indicators remain poorly developed. Therefore the objective of this project was to (i) focus on statistical approaches aimed at ensuring that estimates of individual indicators are as robust as possible, and (ii) present methods that can be used to report on the overall state of the system by integrating estimates of individual indicators. It was agreed at the outset, that this project was to focus on developing methods for a water quality report card. This was driven largely by the requirements of Reef Water Quality Protection Plan (RWQPP) and led to strong partner engagement with the Reef Water Quality Partnership.
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Abnormal event detection has attracted a lot of attention in the computer vision research community during recent years due to the increased focus on automated surveillance systems to improve security in public places. Due to the scarcity of training data and the definition of an abnormality being dependent on context, abnormal event detection is generally formulated as a data-driven approach where activities are modeled in an unsupervised fashion during the training phase. In this work, we use a Gaussian mixture model (GMM) to cluster the activities during the training phase, and propose a Gaussian mixture model based Markov random field (GMM-MRF) to estimate the likelihood scores of new videos in the testing phase. Further-more, we propose two new features: optical acceleration, and the histogram of optical flow gradients; to detect the presence of any abnormal objects and speed violations in the scene. We show that our proposed method outperforms other state of the art abnormal event detection algorithms on publicly available UCSD dataset.
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Driver training is one of the interventions aimed at mitigating the number of crashes that involve novice drivers. Our failure to understand what is really important for learners, in terms of risky driving, is one of the many drawbacks restraining us to build better training programs. Currently, there is a need to develop and evaluate Advanced Driving Assistance Systems that could comprehensively assess driving competencies. The aim of this paper is to present a novel Intelligent Driver Training System (IDTS) that analyses crash risks for a given driving situation, providing avenues for improvement and personalisation of driver training programs. The analysis takes into account numerous variables acquired synchronously from the Driver, the Vehicle and the Environment (DVE). The system then segments out the manoeuvres within a drive. This paper further presents the usage of fuzzy set theory to develop the safety inference rules for each manoeuvre executed during the drive. This paper presents a framework and its associated prototype that can be used to comprehensively view and assess complex driving manoeuvres and then provide a comprehensive analysis of the drive used to give feedback to novice drivers.
Resumo:
The Haddon Matrix was developed in the 1960s road safety arena, and has since been used in many public health settings. The literature and two specific case studies are reviewed to describe the background to the Haddon Matrix, identify how it has been critiqued and developed over time and practical applications in the work-related road safety context. Haddon’s original focus on the road, vehicle and driver has been extended and applied to include organisational safety culture, journey management and wider issues in society that affect occupational drivers and the communities in which they work. The paper shows that the Haddon Matrix has been applied in many projects and contexts. Practical work-related road safety applications include providing a comprehensive systems-based safety management framework to inform strategy. It has also been used to structure the review or gap analysis of current programs and processes, identify and develop prevention measures and as a tool for effective post-event investigations.