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In previous research, significant effects of weather conditions on car crashes have been found. However, most studies use monthly or yearly data and only few studies are available analyzing the impact of weather conditions on daily car crash counts. Furthermore, the studies that are available on a daily level do not explicitly model the data in a time-series context, hereby ignoring the temporal serial correlation that may be present in the data. In this paper, we introduce an Integer Autoregressive model for modelling count data with time interdependencies. The model is applied to daily car crash data, metereological data and traffic exposure data from the Netherlands aiming at examining the risk impact of weather conditions on the observed counts. The results show that several assumptions related to the effect of weather conditions on crash counts are found to be significant in the data and that if serial temporal correlation is not accounted for in the model, this may produce biased results.

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a b s t r a c tIn this paper we focus on an essential step in the construction process of a composite road safety performance indicator: the assignment of weights to the individual indicators. In the composite indicator literature, this subject has been discussed for a long time, and no agreement has been reached so far. The aim of this research is to provide insights in the most important weighting methods: factor analysis, analytic hierarchy process, budget allocation, data envelopment analysis and equal weighting. We will give the essential theoretical considerations, apply the methods on road safety data from various countries and discuss their advantages and disadvantages. This will facilitate the selection of a justifiable method. It is shown that the position of a country in the ranking is influenced by the method used. The weighting methods agree more for countries with a relatively bad road safety performance. Of the five techniques, the weights based on data envelopment analysis resulted in the highest correlation with the road safety ranking of 21 European countries based on the number of traffic fatalities per million inhabitants. This method is valuable for the development of a road safety index.

This paper focuses on the effect of weather conditions on daily traffic intensities (the number of cars passing a specific segment of a road). The main objective is a general examination of whether or not weather conditions uniformly alter daily traffic intensities in Belgium, or in other words whether or not the road usage on a particular location determines the size of the effects of various weather conditions. This general examination is a contribution which allows policy makers to assess the appropriateness of countrywide versus local traffic management strategies. In addition, a secondary goal of this paper is to validate findings in international literature within a Belgian context. To achieve these goals, the effects of weather conditions on both upstream (towards a specific location) and downstream (away from a specific location) traffic intensities of three traffic count locations, typified by a different road usage, are analyzed. The most interesting results of this study for policy makers are the heterogeneity of the weather effects between different traffic count locations, and the homogeneity of the weather effects on upstream and downstream traffic at a certain location. The results also indicated that snowfall, rainfall and wind speed clearly have a diminishing effect on traffic intensity, while maximum temperature has an increasing effect on traffic intensity.Further generalizations of the findings will be possible by studying weather effects on local roads and by shifting the scope towards travel behavior.

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Road safety performance indicators (SPI) have recently been proposed as a useful instrument in comparing countries on the performance of different risk aspects of their road safety system. In this respect, SPIs should be actionable, i.e. they should provide clear directions for policymakers about what action is needed and which priorities should be set in order to improve a country's road safety level in the most efficient way.This paper aims at contributing to this issue by proposing a computational model based on data envelopment analysis (DEA). Based on the model output, the good and bad aspects of road safety are identified for each country. Moreover, targets and priorities for policy actions can be set. As our data set contains 21 European countries for which a separate, best possible model is constructed, a number of country-specific policy actions can be recommended. Conclusions are drawn regarding the following performance indicators: alcohol, speed, protective systems, vehicle, infrastructure and trauma management. For each country that performs relatively poorly, a particular country will be assigned as a useful benchmark. Road safety performance information from other countries can help in this respect.Better insight into the road safety situation can be gained by studying the available data.In this context, a comparison between countries is often made based on crash data. The number of injury crashes and the number of casualties (divided into fatalities, serious injuries and slight injuries) per capita can be used to set up a ranking. In respect to the number of fatalities, Sweden, the United Kingdom and the Netherlands -being referred to as the SUN countries -are seen as an example for other European countries. Furthermore, in addition to the development of a set of useful crash related variables on the one hand and road safety performance indicators on the other hand, it would be interesting to create one road safety index (a combination of relevant road safety aspects into one index) enabling an overall comparison across entities (e.g. countries). The multidimensionality is summarised and the total road safety picture can be presented. The SafetyNet project stresses the importance of daytime running lights as an extra risk domain (in addition to the other six). However, this domain is not considered in this study as, in literature, the importance of this rather small aspect of road safety is less obvious. Additionally, road safety experts consider this as the least important risk domain of all (Hermans et al., 2008a). Some Northern countries constituted a daytime running lights' law a long time ago. Recently, there is no agreement regarding the obligation of daytime running lights on a larger (European) scale as the possible effects are unclear. Moreover, the availability and quality of the data is very poor compared to the other indicators. 2 Road safety outcomes can be decomposed in two main components, i.e. exposure and risk. To fairly compare countries road safety outcomes (e.g. the nu...