Modeling Individuals’ Willingness to Share Trips With Strangers in an Autonomous Vehicle Future

Lavieri, Patricia S.; Bhat, Chandra R. · 2018 · ROSA P / University of Texas at Austin. Data-Supported Transportation Operations & Planning Center (D-STOP)

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Summary

This study investigates the behavioral determinants of adopting dynamic ridesharing services in a future autonomous vehicle (AV) landscape, specifically focusing on individuals' willingness to share trips with strangers. The research is motivated by the potential of Mobility-as-a-Service (MaaS) systems to reduce vehicle miles traveled and congestion in low-density U.S. metropolitan areas, where public transit is often inadequate. While supply-side simulations suggest significant benefits from shared autonomous vehicles (SAVs), the authors note that historical data indicates low public acceptance of ridesharing due to barriers such as increased travel times and discomfort with sharing space with strangers. To address this gap, the study introduces the concept of "willingness to share" (WTS), defined as the monetary value an individual places on traveling alone versus sharing a ride, and compares it against the value of travel time (VTT). The authors employed a multivariate integrated choice and latent variable model, a special case of the Generalized Heterogeneous Data Model, to analyze data from an online survey of 1,607 commuters in the Dallas-Fort Worth-Arlington metropolitan area. The model jointly estimated three outcomes: current ride-hailing experience (categorized as no experience, private-only, or pooled experience) and future intentions to use SAVs for commute and leisure trips (binary choices between solo and shared rides). These outcomes were linked to three psychosocial latent constructs: privacy-sensitivity, time-sensitivity, and interest in the productive use of travel time (IPTT). The analysis utilized stated choice experiments where respondents evaluated scenarios varying in travel time, cost, fare discounts, and the number of additional passengers. The results indicate that users are less sensitive to the presence of strangers during commute trips compared to leisure trips. Crucially, the study found that the additional travel time required to pick up and drop off other passengers constitutes a greater barrier to adopting shared services than the mere presence of strangers. However, this barrier is mitigated for individuals with a high interest in using travel time productively, particularly among high-income earners who can utilize the extra time for work or leisure activities. The latent variable analysis confirmed that privacy concerns and time scarcity significantly influence choice behavior, with privacy-sensitivity negatively impacting the likelihood of choosing shared rides, especially as the number of additional passengers increases. The significance of this research lies in its nuanced understanding of the trade-offs consumers make between cost savings, time efficiency, and social comfort in an AV future. By quantifying WTS and VTT, the study provides policymakers and service providers with insights into which population segments are most likely to adopt dynamic ridesharing. The findings suggest that to increase adoption rates, SAV services must minimize additional travel time or enhance the productivity potential of that time, rather than relying solely on fare discounts to overcome privacy concerns. This behavioral perspective complements existing supply-side simulations, offering a more holistic view of the viability of MaaS systems in car-dominated urban environments.

Key finding

Travel time added to serve other passengers is a greater barrier to shared autonomous vehicle adoption than the presence of strangers, and commuters are less sensitive to sharing than leisure travelers.

Methodology

lab_experiment

Sample size: 1607

Provenance

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• acceptance adoption