Time series analysis in road safety research using state space methods

This publication presents a set of comprehensive studies into time series analysis for aggregated road safety data, such as accident counts and victim counts. In particular, the publication describes how the number of fatalities and serious injuries is closely monitored by government agencies and the public, and its relevance to society is not disputed. Much research has been conducted into how road safety can be improved. To that end it is often attempted to explain changes in road safety statistics by factors (or changes in) such as exposure, policy, driving under the influence of alcohol, speeding by drivers and infrastructural measures. Some factors such as regulations, traffic law and policy can be directly observed (although compliance with regulations, traffic law and policy may not). Other factors can be observed in theory but in practice their measurement is either difficult or very expensive. Examples of such factors are exposure, which can be measured using surveys and vehicle counting systems, and driving under the influence of alcohol, which can be measured using road side surveys. Finally, some factors are even harder to observe such as driver skill or experience. Data obtained from diverse sources as described above are likely to differ in accuracy, which may complicate statistical analysis. Another complicating factor in road safety time series analysis is that no unique measure of road safety is available. Usually road safety is measured in terms of the number of accidents or the number of victims. Although in practice the situation is more complicated, some road safety measures may affect either accident occurrence or accident severity.