As congestion levels increase in cities, it is important to analyze people’s choices of different services provided by transportation network companies (TNCs). Using machine learning techniques in conjunction with large TNC data, this paper focuses on uncovering complex relationships underlying ridesplitting market share. A real-world dataset provided by TNCs in Chicago is used in analyzing ridesourcing trips from November 2018 to December 2019 to understand trends in the city. Aggregated origin–destination trip-level characteristics, such as mean cost, mean time, and travel time reliability, are extracted and combined with origin–destination community-level characteristics. Three tree-based algorithms are then utilized to model the market share of ridesplitting trips. The most significant factors are extracted as well as their marginal effect on ridesplitting behavior, using partial dependency plots for interpretation of the machine learning model results. The results suggest that, overall, community-level factors are as or more important than trip-level characteristics. Additionally, the percentage of White people highly affects ridesplitting market share as well as the percentage of bachelor’s degree holders and households with two people residing in them. Travel time reliability and cost variability are also deemed more important than travel time and cost savings. Finally, the potential impact of taxes, crimes, cultural differences, and comfort is discussed in driving the market share, and suggestions are presented for future research and data collection attempts.


    Zugriff

    Download

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Understanding Ridesplitting Behavior with Interpretable Machine Learning Models Using Chicago Transportation Network Company Data


    Weitere Titelangaben:

    Transportation Research Record


    Beteiligte:
    Abkarian, Hoseb (Autor:in) / Chen, Ying (Autor:in) / Mahmassani, Hani S. (Autor:in)


    Erscheinungsdatum :

    2021-09-09




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Understanding ridesplitting behavior of on-demand ride services: An ensemble learning approach

    Chen, Xiqun (Michael) / Zahiri, Majid / Zhang, Shuaichao | Elsevier | 2016


    Scale effects in ridesplitting: A case study of the City of Chicago

    Liu, Hao / Devunuri, Saipraneeth / Lehe, Lewis et al. | Elsevier | 2023


    Analysis of multi-modal commute behavior with feeding and competing ridesplitting services

    Zhu, Zheng / Qin, Xiaoran / Ke, Jintao et al. | Elsevier | 2019


    Structure Analysis of Factors Influencing the Preference of Ridesplitting

    Xinghua Li / Feiyu Feng / Wei Wang et al. | DOAJ | 2021

    Freier Zugriff