Analyzing Users' Preferences between Personal and Pooled Rideshare Services Using a Mixed Logit Modeling Approach

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Abstract Ridesharing has become an increasingly popular transportation method over the past decade. Transportation network companies such as Uber and Lyft generally provide two types of rideshare services: personal rideshare, in which users ride alone or with individuals they know, and pooled rideshare, in which users ride with passengers they do not know but share similar routes. Pooled rideshare is capable of reducing energy consumption and traffic in the transportation system in comparison to personal rideshare. Despite the growth in trip volume, ridesharing usage is still low compared to other popular transportation methods in the U.S., particularly traveling in one's own personal vehicle. Furthermore, pooled rideshare usage is lower than personal rideshare. To understand riders' preferences, a national survey ( N = 2,884) was conducted in the U.S. to investigate users' choice behaviors in rideshare services examining personal versus pooled rideshare. Each survey respondent completed 20 stated choice scenarios where participants choose between a personal or pooled rideshare option. Based on the responses, a mixed logit model was developed to capture the choice behavior preferences of the participants. The model unveiled the impact of demographic and trip attribute variables on users' rideshare preferences. The discussion encompassed insights into demographic backgrounds and trip attributes, accompanied by a set of policy recommendations aimed at enhancing future pooled rideshare utilization.
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Analyzing Users' Preferences between Personal and Pooled Rideshare Services Using a Mixed Logit Modeling Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analyzing Users' Preferences between Personal and Pooled Rideshare Services Using a Mixed Logit Modeling Approach Haotian Su, Nazmul A. Khan, Krishna M. Gurumurthy, Joseph Paul, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6176409/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Jan, 2026 Read the published version in Transportation → Version 1 posted 16 You are reading this latest preprint version Abstract Ridesharing has become an increasingly popular transportation method over the past decade. Transportation network companies such as Uber and Lyft generally provide two types of rideshare services: personal rideshare, in which users ride alone or with individuals they know, and pooled rideshare, in which users ride with passengers they do not know but share similar routes. Pooled rideshare is capable of reducing energy consumption and traffic in the transportation system in comparison to personal rideshare. Despite the growth in trip volume, ridesharing usage is still low compared to other popular transportation methods in the U.S., particularly traveling in one's own personal vehicle. Furthermore, pooled rideshare usage is lower than personal rideshare. To understand riders' preferences, a national survey ( N = 2,884) was conducted in the U.S. to investigate users' choice behaviors in rideshare services examining personal versus pooled rideshare. Each survey respondent completed 20 stated choice scenarios where participants choose between a personal or pooled rideshare option. Based on the responses, a mixed logit model was developed to capture the choice behavior preferences of the participants. The model unveiled the impact of demographic and trip attribute variables on users' rideshare preferences. The discussion encompassed insights into demographic backgrounds and trip attributes, accompanied by a set of policy recommendations aimed at enhancing future pooled rideshare utilization. Rideshare services Transportation network companies Travel behavior Choice model Mixed logit model Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 09 Jan, 2026 Read the published version in Transportation → Version 1 posted Editorial decision: Revision requested 17 Jul, 2025 Reviews received at journal 17 Jul, 2025 Reviews received at journal 16 Jul, 2025 Reviewers agreed at journal 07 Jul, 2025 Reviewers agreed at journal 07 Jul, 2025 Reviewers agreed at journal 02 Jul, 2025 Reviews received at journal 22 Jun, 2025 Reviewers agreed at journal 22 May, 2025 Reviewers agreed at journal 16 May, 2025 Reviewers agreed at journal 16 May, 2025 Reviewers agreed at journal 15 May, 2025 Reviewers agreed at journal 14 May, 2025 Reviewers invited by journal 28 Apr, 2025 Editor assigned by journal 24 Apr, 2025 Submission checks completed at journal 07 Mar, 2025 First submitted to journal 07 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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