In Flanders (the Flemish speaking community of Belgium), approximately 1014 accident locations are currently considered as 'dangerous'. These 'dangerous' accident sites or so called black spots are selected by the Flemish government by means of their historic accident data for the period 1997-1999. Based on these data, each site where in the last 3 years 3 or more accidents have occurred, is selected. To improve the traffic safety on these locations, the Flemish government, will each year, starting in 2003 for a period of 5 years, invest 100 million EURO to redesign the infrastructure of the 800 black spots with the highest priority value. 77 of these accident sites are located on motorways, 61 on ring roads and 190 on passage roads. Accordingly, a relatively high number of dangerous accident sites are located on secondary and rural roads. However, this could be explained by the fact that in rural areas traffic accidents tend to lead to more serious injuries due to excessive speed levels. Finally, the impact of giving weight to the accidents instead of to all the injured occupants of the vehicles is investigated. The remainder of this paper is organized as follows. First a formal introduction to the hierarchical Bayesian model and to the statistical techniques used in this research is provided. This will be followed by a description of the dataset. Next, the results of the empirical study are presented. Analysis shows that a change in the traffic safety policy and the reflection of this choice in the injury weighting values used to identify and rank the most dangerous accident locations will not only have an important impact on the number of accident locations that will change when selecting and ranking black spots, it will also have an important effect on the type of accident locations (e.g. locations with high traffic volumes resulting in many small accidents) that are selected and accordingly on the resulting future traffic safety decisions. Government should therefore carefully decide which priorities should be stressed in the traffic safety policy. Accordingly, the appropriate weighting value combination can be chosen to rank and select the most dangerous accident locations. Furthermore, the use of Bayesian estimation values instead of historic count data to rank the accident locations can overcome the problem of random variation in accident counts. Finally, analysis shows that giving weight to the severity of the accident instead of to all the injured occupants of the vehicle will have a great impact on the selection and ranking of dangerous accident locations. No conclusions are made concerning which weighting criterion should be preferred. This was not the objective of this paper and will require additional data and in depth analyses of the accident locations. For the covering abstract see ITRD E136183.
Abstract