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.

Developing a motorcycle and scooter safety compendium for the Asia-Pacific Economic Cooperation (APEC)

Motorcycle and scooter crashes are significant contributors to road trauma in many low, medium and high income countries. The APEC Transportation Working Group has commissioned CARRS-Q to develop a compendium of best practice measures that can be used to reduce crashes, post-crash trauma and associated socio-economic costs. The compendium will be informed by findings from a literature review and an expert survey. The literature review examined motorcycle and scooter safety usage and fatalities along with socio-cultural factors which might influence safety in each economy. A discussion is provided regarding the processes involved in the expert survey and how this might be integrated with the findings from the literature review. The implications for developing the compendium are discussed as is the next step of a workshop to further disseminate findings. This will enable the identification of important motorcycle safety issues in APEC economies and implications for implementation of countermeasures.

Are road traffic crash fatality rates converging among OECD countries?

This article examines the trends of road traffic crash (RTC) fatality rates in OECD countries over the past four decades. Based on recent developments in the economic growth literature we propose and test the hypothesis that RTC fatality rates initially increase with economic development, peak, and then gradually decrease. The theory predicts that, as a result, the RTC fatality rates of different countries will tend to converge over time. Our results for the period 1961–2007 reveal no evidence of the convergence of RTC fatality rates across the OECD as a whole for that time period. Nevertheless, there is evidence of convergence among sub-groups of countries. This evidence may assist policymakers as an additional way of benchmarking their country's performance against that of its peers and to identify the next-closest peer in country sub-groups with superior road safety performance.