Automatic vehicles are likely to operate in mixed traffic conditions, affecting cyclists’ existing behavioral decisions. However, the intention and decision-making mechanism of cyclists’ risk-taking behavior on shared roads are not clearly understood. The theory of planned behavior(TPB) has been proven to be effective in the study of travel behavior. Based on the theory of TPB, the Extended Theory of Planned Behavior (ETPB) research framework of risk-taking behavior intention was constructed by introducing safety perception, psychological motivation and technology cognition variables. Safety perception variables took into account the public’s perception of safety in the shared road environment with AVs, while psychological motivation mainly took into account riders’ past riding habits, risk perception bias and riding emotion factors. Secondly, questionnaire survey was used to obtain riders’ personal basic information and multi-situation risk-taking behavior willingness data, and corresponding structural equation model was constructed to explain the influence of variables on willingness. Finally, clustering people by means of K-means clustering algorithm and analyzing the psychological and behavioral characteristics of all kinds of people, so as to facilitate the selection of traffic management strategies. The results show that the extended TPB model can better explain the risk-taking behaviors of cyclists on the self-driving shared road. The influence of attitude and safety perception on the willingness to take risks is more prominent, and technology cognition plays an important indirect role. This study expands the research on the psychology of cyclists’ risk-taking behavior, which could help to promote the integration of autonomous vehicles into transportation systems and better management strategies.


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    Titel :

    A study on cyclists’ risk-taking behavior intention on shared roads with autonomous vehicles


    Beteiligte:
    Fu, Siyi (Autor:in) / Zhou, Hongmei (Autor:in) / Li, Yue (Autor:in) / Wang, Wenwen (Autor:in)


    Erscheinungsdatum :

    2023-08-04


    Format / Umfang :

    974442 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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