Abstract
Increased safety is one of the main motivations for traffic research and planning. The arduous task has two components: (i) improving the existing traffic policies based on a good understanding of risk factors related to trends in traffic accidents, and (ii) underpinning the emerging technologies that will advance the safety of vehicles. For the latter route, the introduction of connected and automated vehicles (CAVs) is a promising option as CAVs can potentially reduce the number of accidents. However, to reap their benefits, they need to be introduced in a safe manner and tested for their ability to safely deal with risky scenarios. Unfortunately, the identification of such test scenarios remains a key challenge for the industry. This study contributes to increased safety by (i) analyzing UK’s STATS19 accident data to identify patterns in past traffic accidents, and (ii) utilizing this information to systematically generate scenarios for CAV testing. For task (i), the patterns in the accidents were identified in terms of static and time-dependent internal and external factors. For this purpose, the study employed a clustering algorithm, COOLCAT, which is particularly suitable for dealing with high-dimensional categorical data. Six different clusters emerged naturally as a result of the algorithm. To interpret the clusters, we applied a frequency analysis to each cluster. The frequency tests showed that in each cluster, certain distinct real-world situations were represented more significantly compared to the non-clustered reference case, which are the markers of each cluster. The second task (ii) complemented the first task by synthesizing the relationships between attributes. This was done by association rule mining using the market basket analysis approach. The method enabled us to develop, drawing from the characteristics of the clusters, non-trivial test scenarios that can be used in the testing of CAVs, especially in virtual testing.
Highlights
Over the past five years, more than a half million traffic accidents have been reported in the UK, distributed more or less evenly in each year [1] (‘‘Road Safety Data - STATS19,’’ 2020)
As the first order of business, it is of prime importance to identify and analyze the factors leading to severe accidents in order to reduce the chances of occurrence
The previously explained COOLCAT clustering method was applied to a sample of 20000 data points that were randomly selected from the collection of accident records
Summary
Over the past five years, more than a half million traffic accidents have been reported in the UK, distributed more or less evenly in each year [1] (‘‘Road Safety Data - STATS19,’’ 2020). An alternative approach is not to assume a pre-set relationship and let the data reveal itself This provides more flexibility and fidelity for data mining methods. One type of data mining strategy, which has been explored to a lesser extent (in the context of traffic accident data) is cluster analysis [38]. The crux of this technique is to group traffic accidents according to microscopically or macroscopically defined criteria, which allows for comparative examination of these groups [39]. Related k-means clustering methods were used by [12] for crash analysis at road junctions, by [43] for pedestrian precrash scenarios and by [44], [45] for the assessment of automated emergency braking systems in accidents
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