This study analyzes various human factors considerations in estimating discounts for pooled rideshare trips. The discounts are utilized in an optimization-based rideshare assignment strategy (proactive strategy) and compared against each other, as well as a heuristic strategy attempting to replicate current real-world pooling rates. Simulations within Austin, Texas and Greenville, South Carolina, reveal the proactive strategy’s ability to increase average vehicle occupancy by 0.23 persons/mile in Austin and 0.52 persons/mile in Greenville. A significant ability to decrease trip rejections and increase profitability is also observed. Finally, the strengths of particular combinations of factors are discussed relative to their effectiveness in each region.


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    Titel :

    Proactive Assignment Strategy With Human Choice Models for Boosting Pooled Rideshare Service


    Beteiligte:
    Paul, Joseph (Autor:in) / Gurumurthy, Krishna Murthy (Autor:in) / Cokyasar, Taner (Autor:in) / Su, Haotian (Autor:in) / Khan, Nazmul (Autor:in) / Auld, Josh (Autor:in) / Jia, Yunyi (Autor:in)


    Erscheinungsdatum :

    01.02.2025


    Format / Umfang :

    13563250 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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