An expert judgment model applied to estimating the safety effect of a bicycle facility
Section snippets
Purpose and methods
The primary purpose of this study has been to develop a model for estimating the changes in risk of modifications to the geometrical layout of streets. The model consists of three submodels: a risk index model, an expert assessment model and a risk attribute model. The model is here applied to the safety of bicyclists. A reason for this application is that bicyclists have more injury accidents per kilometer traveled than almost all other road-user categories (Gårder, 1994). On the other hand,
Illustrative example — raised bicycle crossing
In this example, we apply the model using automobile speed and bicycle speed as attributes. The model describes the combined effect of these attributes on collision risk.
The experts were asked to draw curves representing the relative risk of collision between a bicyclist and a right-turning car when the initial speed of the car and the speed of the bicyclist were varied. The experts were also asked to assess the relative injury risk of the bicyclist for different initial speeds of the
Effect on bicycle volumes and the safety effect of changes in this volume
Bicycle flows increased on one of the streets by 75% on one side and by 79% on the other side, and by 100% on the second of the two streets where extensive measurements were taken before and after the reconstruction. Measurements of bicycle flows on the two control sections indicate a general growth in bicycle flows of around 20%. The extra increase on our experimental sections would then be at least 50%. This is probably the result of the ‘better’ layout.
An analysis of the relationship between
Concluding remarks
The experts’ assessments of risk were made separately for each attribute. The exponential model was used to approximate the assessments of the experts. The experts were asked to give their opinions in the form of relative risk curves, from which the parameters of the exponential distribution were estimated. Based on the experts’ estimates, we determined the posterior distribution of the parameter by using a Bayesian multiplicative error model. Although there are obvious difficulties in the
Acknowledgements
The authors would like to express their sincere appreciation to all those who contributed in various ways to the development of this paper, including Mr Rein Schandersson, SweRoad, for contributing his knowledge about ‘knowledge engineering’, and Mr Per Näsman, Prof. Torbjörn Thedéen, Prof. Åke Claesson and Mr Björn Bergman, Royal Institute of Technology, Stockholm, Sweden and Mr Lennart Andersson, City of Gothenburg, Sweden for aiding in the planning and execution of this study. We would also
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