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The aim of this project is to develop an improved basis for efficient socio-economic prioritising of road safety measures. Road fatalities and injuries are together with congestion the largest externalities connected to transport. The traditional way of predicting road accidents – and thus assessing road safety measures – has been to model accidents as a function of road type and traffic volume only. However, these variables cannot alone explain the trend in accidents over time and moreover, in traditional models the severity and accidents are completely decoupled. This project will overcome these shortcomings and combine the modelling approach with in-depth insight into road user behaviour. This project will use the aggregate and disaggregate parts of the so-called DRAG modelling approach to establish quantitative relations between accidents of various degrees of severity and road user (risk) behaviour, vehicle ownership, infrastructure and economic activity. Moreover, the project will estimate preference-based economic values of road safety measures. As a novelty, accident modelling will include both police recorded accidents and emergency room recorded accidents. In addition, modelling will include individual socio-economic and demographic data from the entire Danish population. Finally, a more qualified inclusion of human behaviour factors, i.e. road user sub group behaviour, in the models will be possible. Methods range from in-depth interviews to statistical modelling. The project is organised in five work packages (WPs), each with defined tasks and scope. Thus, data for WP3 will be documented and provided by WP1 and 2; modelling will take place in WP3, qualification of the models in WP2, development of a scientifically founded valuation method of accidents in WP4, and eventually transforming results into recommendations in WP5.
Financing sourceForskningsprojekter - Andre ministerier og styrelser
Research programmeForskningsprojekter - Andre ministerier og styrelser
Amount8,513,428.00 Danish Kroner
Project ID35254
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ID: 2257016