Prevalence and characteristics of road traffic injuries among young drivers in Oman, 2009-2011

Objectives Studies from different parts of the world have indicated that the impact of road traffic incidents disproportionally affects young adults. Few known studies have been forthcoming from Arabian Gulf countries. Within Oman, a high proportion of the population is under the age of 20. Coupled with the drastic increase of motorization in recent years there is a need to understand the state of road safety among young people in Oman. The current research aimed to explore the prevalence and characteristics of road traffic injuries among young drivers aged 17-25 years. Methods Crash data from 2009-2011 was extracted from the Directorate General of Traffic, Royal Oman Police (ROP) database in Oman. The data was analyzed to explore the impact of road crashes on young people (17-25 years), the characteristics of young driver crashes and how these differ from older drivers and to identify key predictors of fatalities in young driver crashes. Results Overall, young people were over-represented in injuries and fatalities within the sample time period. While it is true that many young people in crashes were driving at the time, it was also evident that young people were often a victim in a crash caused by someone else. Thus, to reduce the impact of road crashes on young people, there is a need to generally address road safety within Oman. When young drivers were involved in crashes they were predominantly male. The types of crashes these drivers have can be broadly attributed to risk taking and inexperience. Speeding and night time driving were the key risk factors for fatalities. Conclusion The results highlight the need to address young driver safety in Oman. From these findings, the introduction of a graduated driver licensing system with night time driving restrictions could significantly improve young driver safety.

Road Traffic Injury Prevention Initiatives: A Systematic Review and Metasummary of Effectiveness in Low and Middle Income Countries.

BACKGROUND: Road traffic injuries (RTIs) are a growing but neglected global health crisis, requiring effective prevention to promote sustainable safety. Low- and middle-income countries (LMICs) share a disproportionately high burden with 90% of the world's road traffic deaths, and where RTIs are escalating due to rapid urbanization and motorization. Although several studies have assessed the effectiveness of a specific intervention, no systematic reviews have been conducted summarizing the effectiveness of RTI prevention initiatives specifically performed in LMIC settings; this study will help fill this gap. METHODS: In accordance with PRISMA guidelines we searched the electronic databases MEDLINE, EMBASE, Scopus, Web of Science, TRID, Lilacs, Scielo and Global Health. Articles were eligible if they considered RTI prevention in LMICs by evaluating a prevention-related intervention with outcome measures of crash, RTI, or death. In addition, a reference and citation analysis was conducted as well as a data quality assessment. A qualitative metasummary approach was used for data analysis and effect sizes were calculated to quantify the magnitude of emerging themes. RESULTS: Of the 8560 articles from the literature search, 18 articles from 11 LMICs fit the eligibility and inclusion criteria. Of these studies, four were from Sub-Saharan Africa, ten from Latin America and the Caribbean, one from the Middle East, and three from Asia. Half of the studies focused specifically on legislation, while the others focused on speed control measures, educational interventions, enforcement, road improvement, community programs, or a multifaceted intervention. CONCLUSION: Legislation was the most common intervention evaluated with the best outcomes when combined with strong enforcement initiatives or as part of a multifaceted approach. Because speed control is crucial to crash and injury prevention, road improvement interventions in LMIC settings should carefully consider how the impact of improvements will affect speed and traffic flow. Further road traffic injury prevention interventions should be performed in LMICs with patient-centered outcomes in order to guide injury prevention in these complex settings.

Lower limb and associated injuries in frontal-impact road traffic collisions.

Objectives: To study the relationship between severity of injury of the lower limb and severity of injury of the head, thoracic, and abdominal regions in frontal-impact road traffic collisions. Methods: Consecutive hospitalised trauma patients who were involved in a frontal road traffic collision were prospectively studied over 18 months. Patients with at least one Abbreviated Injury Scale (AIS) ≥3 or AIS 2 injuries within two AIS body regions were included. Patients were divided into two groups depending on the severity of injury to the head, chest or abdomen. Low severity group had an AIS < 2 and high severity group had an AIS ≥ 2. Backward likelihood logistic regression models were used to define significant factors affecting the severity of head, chest or abdominal injuries. Results: Eighty-five patients were studied. The backward likelihood logistic regression model defining independent factors affecting severity of head injuries was highly significant (p=0.01, nagelkerke r square = 0.1) severity of lower limb injuries was the only significant factor (p=0.013) having a negative correlation with head injury (Odds ratio of 0.64 (95% CI: 0.45-0.91). Conclusion: Occupants who sustain a greater severity of injury to the lower limb in a frontal-impact collision are likely to be spared from a greater severity of head injury.

How accurate is the identification of serious traffic injuries by Police? The concordance between Police and hospital reported traffic injuries

Police reported crash data are the primary source of crash information in most jurisdictions. However, the definition of serious injury within police-reported data is not consistent across jurisdictions and may not be accurate. With the Australian National Road Safety Strategy targeting the reduction of serious injuries, there is a greater need to assess the accuracy of the methods used to identify these injuries. A possible source of more accurate information relating to injury severity is hospital data. While other studies have compared police and hospital data to highlight the under-reporting in police-reported data, little attention has been given to the accuracy of the methods used by police to identify serious injuries. The current study aimed to assess how accurate the identification of serious injuries is in police-reported crash data, by comparing the profiles of transport-related injuries in the Queensland Road Crash Database with an aligned sample of data from the Queensland Hospital Admitted Patients Data Collection. Results showed that, while a similar number of traffic injuries were recorded in both data sets, the profile of these injuries was different based on gender, age, location, and road user. The results suggest that the ‘hospitalisation’ severity category used by police may not reflect true hospitalisations in all cases. Further, it highlights the wide variety of severity levels within hospitalised cases that are not captured by the current police-reported definitions. While a data linkage study is required to confirm these results, they highlight that a reliance on police-reported serious traffic injury data alone could result in inaccurate estimates of the impact and cost of crashes and lead to a misallocation of valuable resources.

Estimating the under-reporting of road crash injuries to police using multiple linked data collections

The reliance on police data for the counting of road crash injuries can be problematic, as it is well known that not all road crash injuries are reported to police which under-estimates the overall burden of road crash injuries. The aim of this study was to use multiple linked data sources to estimate the extent of under-reporting of road crash injuries to police in the Australian state of Queensland. Data from the Queensland Road Crash Database (QRCD), the Queensland Hospital Admitted Patients Data Collection (QHAPDC), Emergency Department Information System (EDIS), and the Queensland Injury Surveillance Unit (QISU) for the year 2009 were linked. The completeness of road crash cases reported to police was examined via discordance rates between the police data (QRCD) and the hospital data collections. In addition, the potential bias of this discordance (under-reporting) was assessed based on gender, age, road user group, and regional location. Results showed that the level of under-reporting varied depending on the data set with which the police data was compared. When all hospital data collections are examined together the estimated population of road crash injuries was approximately 28,000, with around two-thirds not linking to any record in the police data. The results also showed that the under-reporting was more likely for motorcyclists, cyclists, males, young people, and injuries occurring in Remote and Inner Regional areas. These results have important implications for road safety research and policy in terms of: prioritising funding and resources; targeting road safety interventions into areas of higher risk; and estimating the burden of road crash injuries.

Creating a proactive decision-support environment in managing road traffic noise

Properly designed decision support environments encourage proactive and objective decision making. The work presented in this paper inquires into developing a decision support environment and a tool to facilitate objective decision making in dealing with road traffic noise. The decision support methodology incorporates traffic amelioration strategies both within and outside the road reserve. The project is funded by the CRC for Construction Innovation and conducted jointly by the RMIT University and the Queensland Department of Main Roads (MR) in collaboration with the Queensland Department of Public Works, Arup Pty Ltd., and the Queensland University of Technology. In this paper, the proposed decision support framework is presented in the way of a flowchart which enabled the development of the decision support tool (DST). The underpinning concept is to establish and retain an information warehouse for each critical road segment (noise corridor) for a given planning horizon. It is understood that, in current practice, some components of the approach described are already in place but not fully integrated and supported. It provides an integrated user-friendly interface between traffic noise modeling software, noise management criteria and cost databases.

Development and implementation of the areawide Dynamic ROad traffic NoisE (DRONE) simulator

This paper discusses the areawide Dynamic ROad traffic NoisE (DRONE) simulator, and its implementation as a tool for noise abatement policy evaluation. DRONE involves integrating a road traffic noise estimation model with a traffic simulator to estimate road traffic noise in urban networks. An integrated traffic simulation-noise estimation model provides an interface for direct input of traffic flow properties from simulation model to noise estimation model that in turn estimates the noise on a spatial and temporal scale. The output from DRONE is linked with a geographical information system for visual representation of noise levels in the form of noise contour maps.

Integration of a road traffic noise model (ASJ) and traffic simulation (AVENUE) for a built-up area

A road traffic noise prediction model (ASJ MODEL-1998) has been integrated with a road traffic simulator (AVENUE) to produce the Dynamic areawide Road traffic NoisE simulator-DRONE. This traffic-noise-GIS based integrated tool is upgraded to predict noise levels in built-up areas. The integration of traffic simulation with a noise model provides dynamic access to traffic flow characteristics and hence automated and detailed predictions of traffic noise. The prediction is not only on the spatial scale but also on temporal scale. The linkage with GIS gives a visual representation to noise pollution in the form of dynamic areawide traffic noise contour maps. The application of DRONE on a real world built-up area is also presented.

Predictions of road traffic noise on residential balconies using a specular and diffusion model

A combined specular reflection and diffusion model using the radiosity technique was developed to calculate road traffic noise level on residential balconies. The model is capable of numerous geometrical configurations for a single balcony situated in the centre of a street canyon. The geometry of the balcony and the street can be altered with width,length and height. The model was used to calculate for three different geometrical and acoustic absorption characteristics for a balcony. The calculated results are presented in this paper.

A review of road traffic noise indicators and their correlation with the LA10(18hour)

This paper outlines a study to determine the correlation between the LA10(18hour) and other road traffic noise indicators. It is based on a database comprising of 404 measurement locations including 947 individual days of valid noise measurements across numerous circumstances taken between November 2001 and November 2007. This paper firstly discusses the need and constraints on the indicators and their nature of matching a suitable indicator to the various road traffic noise dynamical characteristics. The paper then presents a statistical analysis of the road traffic noise monitoring data, correlating various indicators with the LA10(18hour) statistical indicator and provides a comprehensive table of linear correlations. There is an extended analysis on relationships across the night time period. The paper concludes with a discussion on the findings.

Evolution of traffic management measures on road traffic noise

Road traffic noise affects the quality of life in the areas adjoining the road. The effect of traffic noise on people is wide ranging and may include sleep disturbance and negative impact on work efficiency. To address the problem of traffic noise, it is necessary to estimate the noise level. For this, a number of noise estimation models have been developed which can estimate noise at the receptor points, based on simple configuration of buildings. However, for a real world situation we have multiple buildings forming built-up area. In such a situation, it is almost impossible to consider multiple diffractions and reflections in sound propagation from the source to the receptor point. An engineering solution to such a real world problem is needed to estimate noise levels in built-up area.

Study on road traffic noise prediction model taking into account the citywide network

For the further noise reduction in the future, the traffic management which controls traffic flow and physical distribution is important. To conduct the measure by the traffic management effectively, it is necessary to apply the model for predicting the traffic flow in the citywide road network. For this purpose, the existing model named AVENUE was used as a macro-traffic flow prediction model. The traffic flow model was integrated with the road vehicles' sound power model, and the new road traffic noise prediction model was established. By using this prediction model, the noise map of entire city can be made. In this study, first, the change of traffic flow on the road network after the establishment of new roads was estimated, and the change of the road traffic noise caused by the new roads was predicted. As a result, it has been found that this prediction model has the ability to estimate the change of noise map by the traffic management. In addition, the macro-traffic flow model and our conventional micro-traffic flow model were combined, and the coverage of the noise prediction model was expanded.

Modeling random effect and excess zeros in road traffic accident prediction

Poisson distribution has often been used for count like accident data. Negative Binomial (NB) distribution has been adopted in the count data to take care of the over-dispersion problem. However, Poisson and NB distributions are incapable of taking into account some unobserved heterogeneities due to spatial and temporal effects of accident data. To overcome this problem, Random Effect models have been developed. Again another challenge with existing traffic accident prediction models is the distribution of excess zero accident observations in some accident data. Although Zero-Inflated Poisson (ZIP) model is capable of handling the dual-state system in accident data with excess zero observations, it does not accommodate the within-location correlation and between-location correlation heterogeneities which are the basic motivations for the need of the Random Effect models. This paper proposes an effective way of fitting ZIP model with location specific random effects and for model calibration and assessment the Bayesian analysis is recommended.

Reducing road related injuries for young adolescents : an investigation of truancy as a risk factor

Truancy is recognised as an indicator of engagement in high-risk behaviours for adolescents. Injuries from road related risk behaviours continue to be a leading cause of death and disability for early adolescents (13-14 years). The aim of this research is to determine the extent to which truancy relates to increased risk of road related injuries for early adolescents. Four hundred and twenty-seven Year 9 students (13-14 years) from five high schools in Queensland, Australia, completed a questionnaire about their perceptions of risk and recent injury experience. Self-reported injuries were assessed by the Extended Adolescent Injury Checklist (E-AIC). Injuries resulting from motorcycle use, bicycle use, vehicle use (as passenger or driver), and as a pedestrian were measured for the preceding three months. Students were also asked to indicate whether they sought medical attention for their injuries. Truancy rates were assessed from self-reported skipping class or wagging school over the same three month period. The findings explore the relationship between early adolescent truancy and road related injuries. The relationship between road related injuries and truancy was analysed separately for males and females. Results of this study revealed that road related injuries and reports of associated medical treatment are higher for young people who engage in truancy when compared with non-truant adolescents. The results of this study contribute knowledge about truancy as a risk factor for engagement in road related risks. The findings have the potential to enhance school policies and injury prevention programs if emphasis is placed on increasing school attendance as a safety measure to decrease road related injuries for young adolescents.

Estimating health related costs and savings from balcony acoustic design for road traffic noise

A multi-faceted study is conducted with the objective of estimating the potential fiscal savings in annoyance and sleep disturbance related health costs due to providing improved building acoustic design standards. This study uses balcony acoustic treatments in response to road traffic noise as an example. The study area is the State of Queensland in Australia, where regional road traffic noise mapping data is used in conjunction with standard dose–response curves to estimate the population exposure levels. The background and the importance of using the selected road traffic noise indicators are discussed. In order to achieve the objective, correlations between the mapping indicator (LA10 (18 hour)) and the dose response curve indicators (Lden and Lnight) are established via analysis on a large database of road traffic noise measurement data. The existing noise exposure of the study area is used to estimate the fiscal reductions in health related costs through the application of simple estimations of costs per person per year per degree of annoyance or sleep disturbance. The results demonstrate that balcony acoustic treatments may provide a significant benefit towards reducing the health related costs of road traffic noise in a community.