Search

This paper presents the results of a stated choice study for estimating the Willingness-To-Pay of respondents in four European countries (Belgium, France, Germany and the Netherlands) to reduce the risk of fatal and serious injuries in road crashes. Respondents were confronted with hypothetical route choices that differ in respect of travel costs, travel time and crash risk. The survey was completed by 8,002 respondents, equally spread over the four participating countries and representative for each country with regards to gender, age and region. Possible biases caused by problematic choice behaviour such as inconsistent, irrational or lexicographic answers were addressed. The resulting values were estimated by means of a mixed logit model allowing to account for the panel nature of the data. The Value of a Statistical Life (VSL) was estimated at 6.2 Mill EUR, the Value of a Statistical Serious Injury (VSSI) at 950,000 EUR, and the Value of Time (VoT) at 16.1 EUR/h. Consequently, the relative value of avoiding a fatal injury is estimated to be around 7 times higher than the value of an avoided serious injury. The study revealed differences between countries with France showing values that are significantly lower than the average and Germany showing values that are significantly higher. The estimated VSL values are considerably higher than the values currently used in the four countries, but they are within the range of values found in similar stated choice studies. The results can be used as an input in a broad range of socioeconomic studies including cost-benefit analysis and assessments of socioeconomic costs of road crashes., (Copyright © 2022. Published by Elsevier Ltd.)

Automatic incident detection (AID) systems and variable speed limits (VSLs) can reduce crash probability and traffic congestion. Studies based on loop detector data have shown that AID systems decrease the variation in speeds between drivers. Despite the impact on driver behaviour characteristics, most mathematical models evaluating the effect of AID systems on traffic operations do not capture driver response realistically. This study examines the main factors related to driver speed compliance with a sequence of three VSLs triggered by an AID system. For this purpose, the variable speed limit database of the executive agency of the Dutch Ministry of Infrastructure and Water Management (Rijkswaterstaat) was integrated into the UDRIVE naturalistic driving database for passenger car data collected in the Netherlands. The video data were annotated to analyse driver glance behaviour and secondary task engagement. A logistic regression model was estimated to predict driver speed compliance after each VSL in the sequence. The results reveal that the factors predicting compliance to the VSLs differ based on which of the three VSLs the driver is subjected to. Low speeds and accelerations before the gantry, approaching a slower leader, high proportion of time with eyes-on-road and close consecutive gantries were associated with high compliance with the first VSL in the sequence (i.e., indicating a speed limit of 70 km/h with flashing attention lights). Low speeds and accelerations before the gantry, close consecutive gantries and a small number of lanes resulted in high compliance with the second VSL (i.e., a speed limit of 50 km/h with flashing attention lights). Low speeds before the gantry and close consecutive gantries were linked to high compliance with the third VSL (i.e., indicating a speed limit of 50 km/h). Although further investigations based on a larger sample are needed, these findings are relevant to the development of human-like driving assistance systems and of traffic simulations that assess the impact of AID systems on traffic operations realistically., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2012 Elsevier Ltd. All rights reserved.)