Highlights Road departures are analyzed in a driving simulator with failure-caused near departures. The method connects drivers’ behavior with the risk of road departure. The results exhibit the patterns prompted by the theory. The tested method provides insight into the research subject. The experiment time is reduced because departures do not have to be observed.

    Abstract Surrogate measures of safety attract revived interest thanks to the advancements in traffic observations techniques and the growing need for rapid safety evaluation. A new method of safety analysis based on failure-caused traffic conflicts and the Lomax distribution was recently proposed to estimate crash frequency more efficiently than with crash data. This paper has two objectives: (1) demonstrate the method applicability to near-departure data collected in a driving simulator, and (2) provide initial evidence of the method validity. Traffic failures and road users’ delayed responses to these failures is considered as the primary cause of both conflicts and crashes. Unlike early postulated exceedance distributions the proposed Lomax distribution of response delays was derived from the causal mechanism. From this perspective, the proposed method may use the entire range of the underlying distribution as long as the observed conflicts are failure-related. The fundamentals of the method are briefly explained with the emphasis on certain behavior of crash frequency estimates implied by the proposed theory. Then, an example application of the method to analyze the risk of road departures in a driving simulator is presented. The results are then inspected and the trend in the estimates derived from the theory is confirmed. This finding points to the method validity. Additional applications of the method are expected to further increase the confidence towards the method and to encourage its introduction to the safety engineering practice.


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    Title :

    Analyzing road near departures as failure-caused events


    Contributors:


    Publication date :

    2020-03-30




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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