This paper presents a study that aims to investigate the role of experience in the mode choice of using ridesourcing services. Factors that impact users’ mode choice between traditional modes (i.e., private vehicles and public transit) and ridesourcing modes (i.e., exclusive and shared rides) were examined using stated preference (SP) survey data. This study focuses on understanding the differences between two groups (those that used and never used ridesourcing services) in terms of their attitudes and perceptions toward ridesourcing services and how these attitudes might influence their mode choice behavior. Two separate error component nested logit modes were applied for individuals with and without ridesourcing experiences. Model results supported our hypothesis that people with and without ridesourcing experience have different attitudes and perceptions toward these services. Individuals without ridesourcing experience displayed more concerns about using ridesourcing and were more attached to their vehicles than those with experience. In particular, trust issues with traveling with strangers, higher travel time concerns, and being a fan of private vehicles significantly reduced the probability of using ridesourcing services for those without ridesourcing experience. Car dependency and preferences for private vehicles could also be addressed by exposing travelers to ridesourcing services through promotion strategies and free-trial programs. The suggested policies and strategies could better address travelers’ needs and concerns, encourage them to switch from private vehicles to shared mobility, and promote more sustainable and environmentally friendly transportation.


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    Title :

    Propensity toward Ridesourcing: The Impacts of Previous Experience and Mode Dependency


    Additional title:

    J. Transp. Eng., Part A: Systems


    Contributors:
    Azimi, Ghazaleh (author) / Jin, Xia (author)


    Publication date :

    2022-04-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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