Rideshare services have the potential to positively impact traffic congestion, safety, emissions, and energy consumption, as they minimize mobility obstacles and transportation inequity for the disabled, rural residents, and other transportation disadvantaged groups. Prior research has extensively explored the benefits of this mobility trend; however, it has not fully addressed the importance of trip characteristics to ride ratings as an indicator of customer satisfaction. This study uses a treed regression analysis on rideshare data collected from Via, an operation in Arlington, Texas, from 2021 to 2022, to gain a better understanding of rider satisfaction. A predictive model was developed to identify the ride characteristics that have the greatest impact on ratings, and the analysis showed that three primary factors positively impact a trip’s rating: having a ride pass, travelling early in the morning, and the origination and destination points. The findings also demonstrated the interplay of various trip characteristics and how they may concurrently maximize the likelihood of a ride receiving a high rating. This study will benefit rideshare service providers and transportation professionals by providing them with insights into the factors that will improve customers’ rideshare experiences.


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    Titel :

    Predicting Rideshare Ratings with Trip Characteristics


    Beteiligte:

    Kongress:

    International Conference on Transportation and Development 2025 ; 2025 ; Glendale, Arizona



    Erscheinungsdatum :

    05.06.2025




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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