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At the end of each year, the German Federal Highway Research Institute (BASt) publishes the road safety balance of the closing year. They describe the development of accident and casualty numbers disaggregated by road user types, age groups, type of road and the consequences of the accidents. However, at the time of publishing, these series are only available for the first eight or nine months of the year. To make the balance for the whole year, the last three or four months are forecasted. The objective of this study was to improve the accuracy of these forecasts through structural time-series models that include effects of meteorological conditions. The results show that, compared to the earlier heuristic approach, root mean squared errors are reduced by up to 55% and only two out of the 27 different data series yield a modest rise of prediction errors. With the exception of four data series, prediction accuracies also clearly improve incorporating meteorological data in the analysis. We conclude that our approach provides a valid alternative to provide input to policy makers in Germany.

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Driving speeds were monitored during a period of 16 weeks encompassing different stages of an anti-speeding campaign in the Netherlands. This campaign targeted speed limit violations in built-up areas. The observation periods differed in terms of intensity and media used for the campaign. Small road-side radars, mounted in light poles, were used and registered the speeds on 20 locations in built-up areas. Speeds of over 10 million vehicles were measured. Ten locations had a posted speed limit of 50km/h; the other ten had a posted speed limit of 30km/h. Posters were placed at half of each group of locations to remind drivers of the speed limit. The average speed on the 50km/h roads was 46.2km/h, and 36.1km/h on the 30km/h roads. The average proportions of vehicles exceeding the speed limit were 33.3% and 70.1% respectively. For the 30km/h roads, the data shows differences in speed and speeding behaviour between the six distinguished observation periods, but overall these differences cannot be logically linked to the contents of the phases and, hence, cannot be explained as an effect of the campaign. The only exception was an effect of local speed limit reminders on the 30km/h roads. This effect, however, was temporary and had disappeared within a week.

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This paper examines the determinants of road traffic crash fatalities in Queensland for the period 1958-2007 using a state-space time-series model. In particular, we investigate the effects of policies that aimed to reduce drink-driving on traffic fatalities, as well as indicators of the economic environment that may affect exposure to traffic, and hence affect the number of accidents and fatalities. The results show that the introduction of a random breath testing program in 1988 was associated with a 11.3% reduction in traffic fatalities; its expansion in 1998 was associated with a 26.2% reduction in traffic fatalities; and the effect of the "Safe4life" program, which was introduced in 2004, was a 14.3% reduction in traffic fatalities. Reductions in economic activity are also associated with reductions in road fatalities: we estimate that a one percent increase in the unemployment rate is associated with a 0.2% reduction in traffic fatalities.

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We review the basic concepts of intervention analysis in the context of structural time series models and we apply this methodology to investigate the possible reduction in monthly crime rates reported from January 1984 up to and including December 2010 after Virginia abolished parole and reformed sentencing in January 1995. We find that the change in legislation has significantly reduced the burglary rates and to a lesser extent the murder rates in Virginia. The robustness of our results is investigated with an automatic detection of breaks procedure as well as with analyses of quarterly rather than monthly data.

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Based on existing literature, a system thinking approach was used to set up a conceptual model on the interrelationships among the components influencing adolescent road mortality, distinguishing between components at the individual level and at the system level. At the individual level the role of risk behaviour (sometimes deliberate and sometimes from inexperience or other non-deliberate causes) in adolescent road mortality is well documented. However, little is known about the extent to which the 'road system' itself may also have an impact on younger adolescents' road mortality. This, by providing a safe or unsafe road environment for all road users (System-induced exposure) and by allowing access to high-risk vehicles at a young or older age through the legal licensing age. This study seeks to explore these relationships by analysing the extent to which the road mortality of 10 to 17 year olds in various jurisdictions can be predicted from the System-induced Exposure (SiE) in a jurisdiction and from its legal licensing age to drive motor vehicles. SiE was operationalized as the number of road fatalities per 10(5) inhabitants/all ages together, but excluding the 10 to 17 year olds. Data on road fatalities during the years 2001 through 2008 were obtained from the OECD International Road Traffic Accident Database (IRTAD) and from the USA NHTSA's Fatality Analysis Reporting System (FARS) database for 29 early and 10 late licensing jurisdictions. Linear mixed models were fitted with annual 'Adolescent road mortality per capita' for 2001 through 2008 as the dependent variable, and time-dependent 'SiE' and time-independent 'Licensing system' as predictor variables. To control for different levels of motorisation, the time-dependent variable 'Annual per capita vehicle distance travelled' was used as a covariate. Licensing system of a jurisdiction was entered as a categorical predictor variable with late licensing countries as a baseline group. The study found support for the protective effects of SiE on adolescent safety. If SiE increased by one unit, the mortality rate of 10 to 17 year olds increased by 0.487 units. No support was found for a protective effect of late licensing for this age group. Thus, compared to young adolescents who are allowed to drive motor vehicles in early licensing jurisdictions, late licensing does not provide extra protection for pre-license adolescents. This finding is probably the result of the high risks associated with alternative transport modes, such as moped riding and bicycling. Also, the fact that the study only included risks to young adolescents themselves and did not include the risks they might pose to other road users and passengers may have contributed to this finding, because such risks are greater when driving a motor vehicle than riding a moped or a bicycle. Therefore, to advance our understanding of the impact of licensing systems, more study is needed into the benefits of early or late licensing, thereby considering these wider effects as well.

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In this paper a unified methodology is presented for the modelling of the evolution of road safety in 30 European countries. For each country, annual data of the best available exposure indicator and of the number of fatalities were simultaneously analysed with the bivariate latent risk time series model. This model is based on the assumption that the amount of exposure and the number of fatalities are intrinsically related. It captures the dynamic evolution in the fatalities as the product of the dynamic evolution in two latent trends: the trend in the fatality risk and the trend in the exposure to that risk. Before applying the latent risk model to the different countries it was first investigated and tested whether the exposure indicator at hand and the fatalities in each country were in fact related at all. If they were, the latent risk model was applied to that country; if not, a univariate local linear trend model was applied to the fatalities series only, unless the latent risk time series model was found to yield better forecasts than the univariate local linear trend model. In either case, the temporal structure of the unobserved components of the optimal model was established, and structural breaks in the trends related to external events were identified and captured by adding intervention variables to the appropriate components of the model. As a final step, for each country the optimally modelled developments were projected into the future, thus yielding forecasts for the number of fatalities up to and including 2020.

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A practical approach was developed to assess and compare the effects of five short road safety education (RSE) programmes for young adolescents that does not rely on injury or crash data but uses self reported behaviour. Questionnaires were administered just before and about one month after participation in the RSE programmes, both to youngsters who had participated in a RSE programme, the intervention group, and to a comparable reference group of youngsters who had not, the reference group. For each RSE programme, the answers to the questionnaires in the pre- and post-test were checked for internal consistency and then condensed into a single safety score using categorical principal components analysis. Next, an analysis of covariance was performed on the obtained safety scores in order to compare the post-test scores of the intervention and reference groups, corrected for their corresponding pre-test scores. It was found that three out of five RSE programmes resulted in significantly improved self-reported safety behaviour. However, the proportions of participants that changed their behaviour relative to the reference group were small, ranging from 3% to 20%. Comparisons among programme types showed cognitive approaches not to differ in effect from programmes that used fear-appeal approaches. The method used provides a useful tool to assess and compare the effects of different education programmes on self-reported behaviour.

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Data collected for building a road safety observatory usually include observations made sequentially through time. Examples of such data, called time series data, include annual (or monthly) number of road traffic accidents, traffic fatalities or vehicle kilometers driven in a country, as well as the corresponding values of safety performance indicators (e.g., data on speeding, seat belt use, alcohol use, etc.). Some commonly used statistical techniques imply assumptions that are often violated by the special properties of time series data, namely serial dependency among disturbances associated with the observations. The first objective of this paper is to demonstrate the impact of such violations to the applicability of standard methods of statistical inference, which leads to an under or overestimation of the standard error and consequently may produce erroneous inferences. Moreover, having established the adverse consequences of ignoring serial dependency issues, the paper aims to describe rigorous statistical techniques used to overcome them. In particular, appropriate time series analysis techniques of varying complexity are employed to describe the development over time, relating the accident-occurrences to explanatory factors such as exposure measures or safety performance indicators, and forecasting the development into the near future. Traditional regression models (whether they are linear, generalized linear or nonlinear) are shown not to naturally capture the inherent dependencies in time series data. Dedicated time series analysis techniques, such as the ARMA-type and DRAG approaches are discussed next, followed by structural time series models, which are a subclass of state space methods. The paper concludes with general recommendations and practice guidelines for the use of time series models in road safety research.

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K-means cluster analysis is known for its tendency to produce spherical and equally sized clusters. To assess the magnitude of these effects, a simulation study was conducted, in which populations were created with varying departures from sphericity and group sizes. An analysis of the recovery of clusters in the samples taken from these populations showed a significant effect of lack of sphericity and group size. This effect was, however, not as large as expected, with still a recovery index of more than 0.5 in the "worst case scenario." An interaction effect between the two data aspects was also found. The decreasing trend in the recovery of clusters for increasing departures from sphericity is different for equal and unequal group sizes.