Analysis of 1981 and 1982 insurance claims to determine effect of 1981 and 1982 bumpers on crash damage. Final report.

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

Analysis of insurance premiums and payouts for 1983-1984 designated high theft rate passenger cars. Final report.

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

Final design and implementation plan for evaluating the effectiveness of FMVSS 215: exterior protection. Tasks #4 & #5 report.

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

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.

The Spectator Insurance Year Book

Title varies: 1874-1937, The Insurance Year Book

Attitudes towards consumer transgressions in the marketplace

A sample of 815 adults drawn from the general American population provided their perception of the appropriateness of 12 questionable consumer actions in the marketplace. The scenarios investigated ranged from illegal actions such as inflating one’s losses when filing an insurance claim to actions that while not illegal, may raise questions of ethics. The 12 scenarios exhibited a wide range of mean responses on the six-point scale thereby supporting an often stated premise that consumer ethics is situational in nature. An array of commonly employed demographic questions was also included on the Internet-based survey, and differences of opinion were documented across the various demographic segments.

A novel approach to assessing road-curve crash severity

Curves are a common feature of road infrastructure; however crashes on road curves are associated with increased risk of injury and fatality to vehicle occupants. Countermeasures require the identification of contributing factors. However, current approaches to identifying contributors use traditional statistical methods and have not used self-reported narrative claim to identify factors related to the driver, vehicle and environment in a systemic way. Text mining of 3434 road-curve crash claim records filed between 1 January 2003 and 31 December 2005 at a major insurer in Queensland, Australia, was undertaken to identify risk levels and contributing factors. Rough set analysis was used on insurance claim narratives to identify significant contributing factors to crashes and their associated severity. New contributing factors unique to curve crashes were identified (e.g., tree, phone, over-steer) in addition to those previously identified via traditional statistical analysis of Police and licensing authority records. Text mining is a novel methodology to improve knowledge related to risk and contributing factors to road-curve crash severity. Future road-curve crash countermeasures should more fully consider the interrelationships between environment, the road, the driver and the vehicle, and education campaigns in particular could highlight the increased risk of crash on road-curves.

Injury scaling research. Final report.

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

Annual report of the commissioner.

Reports treating chiefly of insurance. Brief reports of the State library are included, 18__-19__.

Best's insurance reports.

"Best's insurance reports ... upon American and foreign joint-stock companies, American mutual companies, inter-insurance associations, and individual underwriting organizations."

Principles of claim adjusting; a manual explaining the procedure , with background material, for settling claims under life, health and accident insurance policies.

Mode of access: Internet.

Dunne's international insurance reports.

Mode of access: Internet.

Best's life insurance reports.

Mode of access: Internet.