Frits Bijleveld

Effects of incentive programs to stimulate safety belt use: A meta-analysis

The effects of campaigns using tangible incentives (rewards) to promote safety belt usage have been evaluated by means of a meta-analytic approach. Two coders extracted a total number of 136 short-term and 114 long-term effect sizes and coded many other variables from 34 journal articles and research reports. The results show a mean short-term increase in use rates of 20.6 percentage points; the mean long-term effect was 13.7 percentage points. Large scale studies report smaller effect sizes than small scale studies; when studies were weighted by the (estimated) number of observations, the weighted mean effect sizes were 12.0 and 9.6 percentage points for the short and long term, respectively. The main factors that influence the magnitude of the reported short-term effect of the programs were the initial baseline rate (which was highly correlated with the presence or absence of a safety belt usage law), the type of population involved, whether incentives were delivered immediately or delayed, and whether incentives were based on group or individual behaviour. Together these four variables accounted for 64% of the variance. Other variables, such as the duration of the intervention, the probability of receiving a reward, and the value of the reward were not related to the short-term effect sizes. The relationship between moderating variables and long-term effects was less clear.

On statistical inference in time series analysis of the evolution of road safety

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.

Do Calculated Conflicts in Microsimulation Model Predict Number of Crashes

A microsimulation model and its calculations are described, and the results that are subsequently used to determine indicators for traffic safety are presented. The method demonstrates which changes occur at the level of traffic flow (number of vehicles per section of road) and at the vehicle level (vehicles choosing different routes). The best-known safety indicator in this type of model is the conflict situation, in which two vehicles approach each other and, if no action is taken, a crash will occur. These conflict situations are detected in the simulation model. This method does not necessarily relate directly to any actual observed conflicts or recorded crashes. The quantitative relationship is examined between detected conflicts at junctions in the model and recorded crashes at the same locations in the real world. The methods chosen for detecting conflicts and for selecting crashes are explained. A microsimulation model was constructed for a regional road network. The conflicts in this network were detected, and the recorded crashes were selected. The results show a quantitative relationship between the number of conflicts at priority junctions and the number of passing motor vehicles on one hand and the number of observed crashes on the other hand. When crashes and conflicts are divided into crash categories, junctions with signals clearly show substantial differences between the relative numbers of frontal crashes and frontal conflicts.

Exposure data and risk indicators for safety performance assessment in Europe

The objective of this paper is the analysis of the state-of-the-art in risk indicators and exposure data for safety performance assessment in Europe, in terms of data availability, collection methodologies and use. More specifically, the concepts of exposure and risk are explored, as well as the theoretical properties of various exposure measures used in road safety research (e.g. vehicle- and person-kilometres of travel, vehicle fleet, road length, driver population, time spent in traffic, etc.). Moreover, the existing methods for collecting disaggregate exposure data for risk estimates at national level are presented and assessed, including survey methods (e.g. travel surveys, traffic counts) and databases (e.g. national registers). A detailed analysis of the availability and quality of existing risk exposure data is also carried out. More specifically, the results of a questionnaire survey in the European countries are presented, with detailed information on exposure measures available, their possible disaggregations (i.e. variables and values), their conformity to standard definitions and the characteristics of their national collection methods. Finally, the potential of international risk comparisons is investigated, mainly through the International Data Files with exposure data (e.g. Eurostat, IRTAD, ECMT, UNECE, IRF, etc.). The results of this review confirm that comparing risk rates at international level may be a complex task, as the availability and quality of exposure estimates in European countries varies significantly. The lack of a common framework for the collection and exploitation of exposure data limits significantly the comparability of the national data. On the other hand, the International Data Files containing exposure data provide useful statistics and estimates in a systematic way and are currently the only sources allowing international comparisons of road safety performance under certain conditions.

Latent risk and trend models for the evolution of annual fatality numbers in 30 European countries.

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.

Analysing the development of road safety using demographic data

The purpose of this paper is to show that time series analyses of road safety and risk can be improved by using demographic data. We demonstrate that the distance travelled by drivers or riders of a certain age reflects the fluctuations over the years of the number of people of that age within the population. We further demonstrate that the change over time of per capita distance travelled, i.e. distance travelled per person, is often less subject to stochastic fluctuations, and therefore more smooth than the total distance travelled for drivers of that age. This smoothness is used to obtain forecasts of distance travelled, or to average out year-to-year fluctuations of data of distance travelled. Analysis of such data stratified by age group, gender or both reveals that, for most travel modes, per capita distance travelled is to a large extent constant or slowly changing over time. The consequences for the evaluation of risk, i.e. casualties per distance travelled, with and without the use of population data, are explored. Dutch data are used to illustrate the model concept. It is shown that the analyses and forecasts of distance travelled could gain substantially by incorporating demographic data, as compared to an analysis with data of distance travelled alone. The paper further shows that, for an analysis of risk and therefore for traffic safety forecasts in the absence of any data of distance travelled, stratified analysis of mortality, i.e. casualties per inhabitant, may be a reasonable alternative.

Setting Road Safety Targets in Cambodia: A Methodological Demonstration Using the Latent Risk Time Series Model

We present the methodology used for estimating forecasts for the number of road traffic fatalities in 2011–2020 in Cambodia based on observed developments in Cambodian road traffic fatalities and motor vehicle ownership in the years 1995–2009. Using the latent risk time series model baseline forecasts for the fatality risk were estimated for the years 2010–2020. These baseline forecasts were then used to obtain estimates for the future number of fatalities based on three scenarios for the future Cambodian growth in motor vehicle ownership: a low, a middle, and a high growth scenario. The middle growth scenario results in an expected death toll of approximately 3,200 in 2020. In 2010, it was therefore decided in Cambodia to set the target at a 50% reduction of this number or 1,600 fatalities in 2020. If it is possible to achieve this target by taking additional actions to improve road safety, then a total of 7,350 lives could be saved in Cambodia over the whole 2011–2020 period.

Continuous Time State Space Modelling with an Application to High-Frequency Road Traffic Data

We review Kalman filter and related smoothing methods for the continuous time state space model. The attractive property of continuous time state space models is that time gaps between consecutive observations in a time series are allowed to vary throughout the process. We discuss some essential details of the continuous time state space methodology and review the similarities and the differences between the continuous time and discrete time approaches. An application in the modelling of road traffic data is presented in order to illustrate the relevance of continuous time state space modelling in practice.

Forecasting German crash numbers: the effect of meteorological variables

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.