Introducing roadside hazard severity indicator based on evidential reasoning approach

Safety experts have, in recent years, been attentive to roadside accident severity and occurrence. Hitherto, to prioritize road segment hazardousness, there have been little efforts to quantify a well defined indicator. In this regard, the existing indicators are usually very plain and the overall configuration of roadside is rated by experts with an exact number describing its condition. Hence, the uncertainties which come with the subjective judgments cannot be regarded as of any substance. This research contribution therefore presents a procedure to assess the road safety (roadside safety indicator) by means of the evidential reasoning (ER) approach. The betterment of ER as opposed to the available procedures for roadside safety assessment is that the proposed approach makes allowance for the uncertainties which may arise from individual judgments. Additionally, when there is a dearth of evidence concerning factors which affect roadside hazardousness severity to collate several roadside segments, this procedure will offer the benefits of utilizing the maximum/minimum utility function. With the aid of the drawn indicator, organizations and agencies responsible for ensuring road safety can reach more flexible decisions to set in-place uncertain planning and road segments priorities. This indicator can also be utilized as a variable to include roadside conditions in crash severity prediction models. A field case study has also been carried out in an attempt to follow and validate the proposed approach which is based on the run-off accident history for a sample road segments. The crash data confirm the suggested indicator.

► We offer a modeling to more carefully assess roadside hazard instead of the customary methods.
► We consider inspector’s uncertainty coming with one’s self judgment for rating.
► We introduce a three- level hierarchy for roadside hazard evaluation.
► We use evidential reasoning approach as a method for better modeling the uncertainty.
► This method presents a flexible assessment and relative ranking of roadside segments.