Abstract
Although speed is considered to be one of the main crash contributory factors, research findings are inconsistent. Independent of the robustness of their statistical approaches, crash frequency models typically employ crash data that are aggregated using spatial criteria (e.g., crash counts by link termed as a link-based approach). In this approach, the variability in crashes between links is explained by highly aggregated average measures that may be inappropriate, especially for time-varying variables such as speed and volume. This paper re-examines crash–speed relationships by creating a new crash data aggregation approach that enables improved representation of the road conditions just before crash occurrences. Crashes are aggregated according to the similarity of their pre-crash traffic and geometric conditions, forming an alternative crash count dataset termed as a condition-based approach. Crash–speed relationships are separately developed and compared for both approaches by employing the annual crashes that occurred on the Strategic Road Network of England in 2012. The datasets are modelled by injury severity using multivariate Poisson lognormal regression, with multivariate spatial effects for the link-based model, using a full Bayesian inference approach. The results of the condition-based approach show that high speeds trigger crash frequency. The outcome of the link-based model is the opposite; suggesting that the speed–crash relationship is negative regardless of crash severity. The differences between the results imply that data aggregation is a crucial, yet so far overlooked, methodological element of crash data analyses that may have direct impact on the modelling outcomes.
Highlights
The primary objective of developing a traffic crash model is to elucidate the association between crashes and their potential contributory factors so as to formulate efficient and targeted crash mitigating measures
Motorway crashes appear to have a decreasing trend, especially in western countries; the number of casualties is still anything but negligible (IRTAD, 2014; WHO, 2013)
This paper introduces a new crash data aggregation concept termed as condition-based approach that aims to represent in more detail the actual pre-crash conditions in order to explore the relationship between motorway crashes and their contributory factors
Summary
The primary objective of developing a traffic crash model is to elucidate the association between crashes and their potential contributory factors so as to formulate efficient and targeted crash mitigating measures. The question arises: are the crash models we currently use accurate enough to develop appropriate preventive measures?. The grouping attribute of crashes in the proposed method is the similarity of precrash conditions rather than a link-level spatial relationship. In this way, crash counts are represented more precisely by explanatory variables that approximate the actual conditions enabling, possibly, improved relationships. The condition-based dataset can be modelled using multivariate Poisson lognormal regression. In order to compare the two methods with respect to their outcomes, the same data are used to build a link-based spatial multivariate Poisson lognormal regression model
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