An expert judgment model applied to estimating the safety effect of a bicycle facility

Accid Anal Prev. 2000 Jul;32(4):589-99. doi: 10.1016/s0001-4575(99)00090-1.


This paper presents a risk index model that can be used for assessing the safety effect of countermeasures. The model estimates risk in a multiplicative way, which makes it possible to analyze the impact of different factors separately. Expert judgments are incorporated through a Bayesian error model. The variance of the risk estimate is determined by Monte-Carlo simulation. The model was applied to assess the safety effect of a new design of a bicycle crossing. The intent was to gain safety by raising the crossings to reduce vehicle speeds and by making the crossings more visible by painting them in a bright color. Before the implementations, bicyclists were riding on bicycle crossings of conventional Swedish type, i.e. similar to crosswalks but delineated by white squares rather than solid lines or zebra markings. Automobile speeds were reduced as anticipated. However, it seems as if the positive effect of this was more or less canceled out by increased bicycle speeds. The safety per bicyclist was still improved by approximately 20%. This improvement was primarily caused by an increase in bicycle flow, since the data show that more bicyclists at a given location seem to benefit their safety. The increase in bicycle flow was probably caused by the new layout of the crossings since bicyclists perceived them as safer and causing less delay. Some future development work is suggested. Pros and cons with the used methodology are discussed. The most crucial parameter to be added is probably a model describing the interaction between motorists and bicyclists, for example, how risk is influenced by the lateral position of the bicyclist in relation to the motorist. It is concluded that the interaction seems to be optimal when both groups share the roadway.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Acceleration
  • Accidents, Traffic / prevention & control*
  • Accidents, Traffic / statistics & numerical data
  • Bayes Theorem
  • Bicycling / injuries*
  • Finland
  • Humans
  • Monte Carlo Method
  • Risk Assessment
  • Safety*
  • Sweden