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User Preferences for Automated Shared Mobility Services: An Alternative-Specific Mixed Logit Regression Approach

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Abstract

Ridesharing transportation services as the leading transport systems in recent years have already invested in shared autonomous vehicles (SAVs) as the next generation of shared transportation systems. Recent years have seen a noticeably accelerating rate in utilizing advanced communications and mapping service technologies that opened new doors to the ridesharing operators to provide a better quality of service to the users; therefore, it is necessary to have a better insight in-to the preferences of the users for using SAVs. This study aims to identify individuals’ preferences for using SAVs and investigate related demographic characteristics and travel behavior attributes. An online Adaptive Choice-Based Conjoint (ACBC) survey will be designed and implemented between March to May 2020 in the United States. Then a series of mixed logit models (MXLs) were used to estimate participants’ preferences. The results show that females are more tended to use door-to-door services, younger riders are more interested in using SAVs and also accept longer travel times, and riders with high-income levels are willing to use SAVs with higher equipment and convenience door-to-door service and not share their trips with others. Furthermore, more educated riders are more likely to use door-to-door service, select a service with shorter travel time, and pay a little more money to insure their trip against possible delays.

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Abbreviations

ACBC:

Adaptive Choice-Based Conjoint

AV:

Autonomous Vehicle

CNL:

Cross-Nested Logit

DDA:

Descriptive Data Analysis

ESMS:

Electric Shared Mobility Services

HOV:

High-Occupancy Vehicle

IIA:

Independence of Irrelevant Alternatives

LCM:

Latent Class Model

MXL:

Mixed Logit Model

MNL:

Multinomial Logit Model

MDCP:

Multiple Discrete-Continuous Probit

RA:

Regression Analysis

SAV:

Shared Autonomous Vehicle

WTP:

Willingness-To-Pay

WTU:

Willingness-To-Use

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Acknowledgments

The authors would like to thank the Sawtooth Software, Inc. for providing an academic grant to conduct this study. The authors also appreciate the Urban Mobility & Equity Center at Morgan State University for their support. The findings of this research do not necessarily represent the views or positions of the authors’ affiliated institutions.

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Authors and Affiliations

  1. Department of Transportation and Urban Infrastructure Studies, Morgan State University, Baltimore, USA

    Alireza Ansariyar, Amirreza Nickkar & Young-Jae Lee

  2. Department of City and Regional Planning, Morgan State University, Baltimore, USA

    Hyeon-Shic Shin

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  1. Alireza Ansariyar
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Correspondence to Amirreza Nickkar.

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Ansariyar, A., Nickkar, A., Lee, YJ. et al. User Preferences for Automated Shared Mobility Services: An Alternative-Specific Mixed Logit Regression Approach. Int. J. ITS Res. 21, 331–348 (2023). https://doi.org/10.1007/s13177-023-00358-0

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