Highlights This study conducted a laboratory experiment in a mixed traffic with autonomous vehicles (AVs) and manually-driven vehicles (MVs) to uncover travel mode preferences. The result showed that more than half of the travelers chose AVs. The result came close to Nash equilibrium, but did not reach social optimum. Those who were provided more information will consider the cost difference between the two modes, which resulted in losses of both individual and social benefits. Subsidies that eliminated the cost inequality can make the system more efficient. The effect of information on social welfare depended on whether providing subsidy or not. Learning model simulation showed that the main factors affecting travelers' decisions were inertia, experiences, and inequality.

    Abstract An increase in autonomous vehicles (AVs) would result in a decline in traffic congestion; however, the travel cost associated with AVs is always higher than that of manually-driven vehicles (MV). This situation is interpreted as a so-called multi-player social dilemma. This study designed an economic experiment to investigate the effect of AVs on mode choice in mixed traffic flows. Participants were informed about the cost function in both modes and were asked to choose the travel mode for more than 60 rounds. In full information (FI) treatment, participants received information about the travel costs of the AV and MV modes at the end of each round. In the partial information (PI) treatment, participants received information only about the travel cost of the mode they chose. We found that participants were sensitive to cost differences in the FI treatments. Based on inequality aversion models, we proposed a perceived cost that could better explain the experimental equilibrium. A monetary reward was provided to encourage participants to take AVs and solve social dilemmas. The results demonstrated that the reward mechanism reduces traffic congestion and increases social benefits, especially in the FI treatment. Finally, a learning model that considers inertia and perceived cost is proposed to explain the decision-making process of the participants during the experiment. The findings have implications for traffic forecasting in the mixed flow of MVs and AVs and provide insights and policy suggestions for AV management.


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    Titel :

    Mode choice between autonomous vehicles and manually-driven vehicles: An experimental study of information and reward


    Beteiligte:
    Zhang, Qianran (Autor:in) / Ma, Shoufeng (Autor:in) / Tian, Junfang (Autor:in) / Rose, John M. (Autor:in) / Jia, Ning (Autor:in)


    Erscheinungsdatum :

    2022-01-13


    Format / Umfang :

    16 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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