Evaluation of design analysis procedures and acceptance criteria for roadside hardware. Volume III - evaluating pre-report 230 crash tests. Final report.

Federal Highway Administration, Safety Design Division, McLean, Va.

Preliminary analysis of the National Crash Severity Study: factors in fatal accidents.

Mode of access: Internet.

Analysis of insurance claims to determine bumper effect on crash damage - 1979 model year. Final report addendum.

National Highway Traffic Safety Administration, Office of Plans and Programs, Washington, D.C.

Frontal crash responses: steering column and intrusion analysis. Final report.

Mode of access: Internet.

An analysis of ASAP patrol effectiveness on crash reductions in Pulaski County, Arkansas. Preliminary report.

National Highway Traffic Safety Administration, Washington, D.C.

Traffic safety facts 2001: a compilation of motor vehicle crash data from the Fatality Analysis Reporting System and the General Estimates System.

Mode of access: Internet.

Traffic safety facts 2003: a compilation of motor vehicle crash data from the Fatality Analysis Reporting System and the General Estimates System.

Both systems were designed and developed by NHTSA's National Center for Statistics and Analysis (NCSA) to provide an overall measure of highway safety, to help identify traffic safety problems, to suggest solutions, and to help provide an objective basis on which to evaluate the effectiveness of motor vehicle safety standards and highway safety initiatives. Data from these systems are used to answer requests for information from the international and national highway traffic safety communities, including state and local governments, the Congress, Federal agencies, research organizations, industry, the media, and private citizens.

National Center for Statistics and Analysis collected technical studies. Volume II: accident data analysis of occupant injuries and crash characteristics - eight papers.

Mode of access: Internet.

Traffic safety facts 2002: a compilation of motor vehicle crash data from the Fatality Analysis Reporting System and the General Estimates System.

Mode of access: Internet.

Analysis of personality traits as a risk factor in crash related trauma.

Background: Due to increasing stress, individual personality traits are becoming a significant contributor to CRT (Crash Related Trauma). In the present study, we hypothesized that there will be no difference in personality characteristics of CRT patients and control subjects and there will be no association between trauma and personality characteristics of CRT patients. Method: A total of 119 cases and 112 controls of age >18 years were selected as per criteria decided. After obtaining ethical clearance, patients presenting to the emergency orthopedic unit were included in the study. After primary management all enrolled subjects were assessed by ICD 10 module screening questionnaire and analyzed for nine personality traits, subject to written informed consent. Results: Of all the cases enrolled 82.35% were males. Impulsive personality trait is found in 84.78% (39/46) cases. There were 46 motorcyclists out of 119 cases enrolled. Most of the personality traits showed a statistical significant association (p < 0.0003) with CRT. Conclusion: Majority of CRT victims attending orthopedic emergency unit at trauma center had impulsive and histrionic personality characteristics which accounted for 84.78% and 82.61% cases respectively. These traits showed a statistical significant association with CRT.

Crash Risk Assessment with Cooperative Systems

Crash risk is the statistical probability of a crash. Its assessment can be performed through ex post statistical analysis or in real-time with on-vehicle systems. These systems can be cooperative. Cooperative Vehicle-Infrastructure Systems (CVIS) are a developing research avenue in the automotive industry worldwide. This paper provides a survey of existing CVIS systems and methods to assess crash risk with them. It describes the advantages of cooperative systems versus non-cooperative systems. A sample of cooperative crash risk assessment systems is analysed to extract vulnerabilities according to three criteria: market penetration, over-reliance on GPS and broadcasting issues. It shows that cooperative risk assessment systems are still in their infancy and requires further development to provide their full benefits to road users.

Applying data mining to assess crash risk on curves

The wide range of contributing factors and circumstances surrounding crashes on road curves suggest that no single intervention can prevent these crashes. This paper presents a novel methodology, based on data mining techniques, to identify contributing factors and the relationship between them. It identifies contributing factors that influence the risk of a crash. Incident records, described using free text, from a large insurance company were analysed with rough set theory. Rough set theory was used to discover dependencies among data, and reasons using the vague, uncertain and imprecise information that characterised the insurance dataset. The results show that male drivers, who are between 50 and 59 years old, driving during evening peak hours are involved with a collision, had a lowest crash risk. Drivers between 25 and 29 years old, driving from around midnight to 6 am and in a new car has the highest risk. The analysis of the most significant contributing factors on curves suggests that drivers with driving experience of 25 to 42 years, who are driving a new vehicle have the highest crash cost risk, characterised by the vehicle running off the road and hitting a tree. This research complements existing statistically based tools approach to analyse road crashes. Our data mining approach is supported with proven theory and will allow road safety practitioners to effectively understand the dependencies between contributing factors and the crash type with the view to designing tailored countermeasures.

An analysis of cyclists crashes to identify ITS-based intervention

Cycling provides a number of health and environmental benefits. However, cyclists are more likely to suffer serious injury or be killed in traffic accidents than car drivers and the estimated cost of crashes in Australia is $1.25AU billion per year. Current interventions to reduce bicycle crashes include compulsory helmet use, media campaigns, and the provision of cycling lanes, as well as road user education and training. It is difficult to assess the effectiveness of current interventions as there is no accurate measure of cyclist exposure in South East Queensland (SEQ). This paper analyses cyclist crash characteristics in Queensland with the view to identifying appropriate Intelligent Transport Systems (ITS) based intervention to reduce cyclist injury and death. The inappropriateness of some ITS interventions to improve cyclist safety is highlighted and a set of ITS interventions are identified, based on Queensland crash data 2002-2006.

Mining patterns and factors contributing to crash severity on road curves

Road curves are an important feature of road infrastructure and many serious crashes occur on road curves. In Queensland, the number of fatalities is twice as many on curves as that on straight roads. Therefore, there is a need to reduce drivers’ exposure to crash risk on road curves. Road crashes in Australia and in the Organisation for Economic Co-operation and Development(OECD) have plateaued in the last five years (2004 to 2008) and the road safety community is desperately seeking innovative interventions to reduce the number of crashes. However, designing an innovative and effective intervention may prove to be difficult as it relies on providing theoretical foundation, coherence, understanding, and structure to both the design and validation of the efficiency of the new intervention. Researchers from multiple disciplines have developed various models to determine the contributing factors for crashes on road curves with a view towards reducing the crash rate. However, most of the existing methods are based on statistical analysis of contributing factors described in government crash reports. In order to further explore the contributing factors related to crashes on road curves, this thesis designs a novel method to analyse and validate these contributing factors. The use of crash claim reports from an insurance company is proposed for analysis using data mining techniques. To the best of our knowledge, this is the first attempt to use data mining techniques to analyse crashes on road curves. Text mining technique is employed as the reports consist of thousands of textual descriptions and hence, text mining is able to identify the contributing factors. Besides identifying the contributing factors, limited studies to date have investigated the relationships between these factors, especially for crashes on road curves. Thus, this study proposed the use of the rough set analysis technique to determine these relationships. The results from this analysis are used to assess the effect of these contributing factors on crash severity. The findings obtained through the use of data mining techniques presented in this thesis, have been found to be consistent with existing identified contributing factors. Furthermore, this thesis has identified new contributing factors towards crashes and the relationships between them. A significant pattern related with crash severity is the time of the day where severe road crashes occur more frequently in the evening or night time. Tree collision is another common pattern where crashes that occur in the morning and involves hitting a tree are likely to have a higher crash severity. Another factor that influences crash severity is the age of the driver. Most age groups face a high crash severity except for drivers between 60 and 100 years old, who have the lowest crash severity. The significant relationship identified between contributing factors consists of the time of the crash, the manufactured year of the vehicle, the age of the driver and hitting a tree. Having identified new contributing factors and relationships, a validation process is carried out using a traffic simulator in order to determine their accuracy. The validation process indicates that the results are accurate. This demonstrates that data mining techniques are a powerful tool in road safety research, and can be usefully applied within the Intelligent Transport System (ITS) domain. The research presented in this thesis provides an insight into the complexity of crashes on road curves. The findings of this research have important implications for both practitioners and academics. For road safety practitioners, the results from this research illustrate practical benefits for the design of interventions for road curves that will potentially help in decreasing related injuries and fatalities. For academics, this research opens up a new research methodology to assess crash severity, related to road crashes on curves.

Development of accident modification factors on intersection crash maneuvers

In order to estimate the safety impact of roadway interventions engineers need to collect, analyze, and interpret the results of carefully implemented data collection efforts. The intent of these studies is to develop Accident Modification Factors (AMF's), which are used to predict the safety impact of various road safety features at other locations or in upon future enhancements. Models are typically estimated to estimate AMF's for total crashes, but can and should be estimated for crash outcomes as well. This paper first describes data collected with the intent estimate AMF's for rural intersections in the state of Georgia within the United Sates. Modeling results of crash prediction models for the crash outcomes: angle, head-on, rear-end, sideswipe (same direction and opposite direction) and pedestrian-involved crashes are then presented and discussed. The analysis reveals that factors such as the Annual Average Daily Traffic (AADT), the presence of turning lanes, and the number of driveways have a positive association with each type of crash, while the median width and the presence of lighting are negatively associated with crashes. The model covariates are related to crash outcome in different ways, suggesting that crash outcomes are associated with different pre-crash conditions.