This study was designed to apply the method proposed in
two earlier studies conducted for the Ring road I in
Helsinki to identify conditions when the risk of a
traffic incident is elevated and develop the method
further to be able to analyse the riskiness over whole
We draw the following conclusions from this study:
1. The method developed earlier can be extended to all
main roads without meeting computational difficulties
when working with Mathematica. The key design principle
was to build the Palm distributions per road segment and
storing them in one file per road.
2. Because relatively few segments are measured
continuously, we could not use actual traffic densities
per hour. Instead, these were replaced by estimates using
traffic variation factors. The results were throughout
credible and the identified risky conditions were similar
to the findings of the Ring road I study.
3. The analysis revealed road segments with very high
risk levels. These top-20 lists should be studied by
domain experts. Note that there may also be errors caused
by the volume estimation method.
4. The overall road condition indicators "yellow" and
"red" turned out to correspond roughly to risk levels 1.5
and 2.5, respectively. Our method could be applied to
tune the use of the colour indicators.
|Publication status||Published - 2015|
|MoE publication type||D4 Published development or research report or study|
|Series||VTT Research Report|
- incident risk
- road weather conditions
- palm distribution