Highlight Vehicle trajectory data are applied to analyze the rear-end collision risks. Three important transition durations are defined and their rationalities are examined. Random-parameters accelerated failure time (AFT) survival models are developed. A typical case is taken to compare the transition durations with TTC and its variants. Transition durations could provide time point information and the variation of data.

    Abstract The time-to-collision (TTC) index and its extended variants have been widely utilized to assess rear-end collision risks, but the characteristics of the time-series data have not been fully explored, especially for the transition from safe to risky conditions. This study proposes a novel approach in rear-end collision risk analysis based on the concept of transition durations. The vehicle trajectory data were extracted and the TTC index was used to identify risky and safe conditions. Three important transition durations are defined and their rationalities for evaluating rear-end collision risks are examined by developing random-parameters accelerated failure time (AFT) survival models. Furthermore, a typical case from real trajectory data is taken to discuss the limitations of using TTC and its variants, and the advantage of the proposed transition durations. The results of random-parameters AFT models reveal contributing factors affecting the length of three durations and demonstrate the rationality of transition durations in rear-end collision risks analysis. It is indicated that the proposed method outperforms TTC and its variants in evaluating rear-end collision risks, because it could not only provide the information of time point but also the variation of time-series data.


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    Titel :

    Exploring transition durations of rear-end collisions based on vehicle trajectory data: A survival modeling approach


    Beteiligte:
    Li, Ye (Autor:in) / Wu, Dan (Autor:in) / Chen, Qinghong (Autor:in) / Lee, Jaeyoung (Autor:in) / Long, Kejun (Autor:in)


    Erscheinungsdatum :

    2021-06-18




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch