Examining the influence of attitudinal factors on the use of ride-hailing services in Toronto

Patrick Loa and Khandker Nurul Habib

Transportation Research Part A: Policy and Practice, 2021, vol. 146, issue C, 13-28

Abstract: The continued growth of ride-hailing usage creates the need for policymakers to understand the factors that affect the adoption and utilization of ride-hailing services. Attitudinal and perceptual factors are of particular importance, both because ride-hailing services are still evolving, and a relatively small number of studies have examined the role of these factors. This paper utilizes data from a web-based survey to understand the role that latent attitudinal factors play in adopting and using ride-hailing services in Toronto. Specifically, two binary logistic regression models are used to understand the factors that influence the adoption of exclusive and shared ride-hailing services. Besides, a zero-inflated ordered probit (ZIOP) model is estimated to investigate the factors that affect the frequency with which a person uses ride-hailing. The empirical investigation reveals that the perception of ride-hailing services tends to differ between individuals with ride-hailing experience and those without, which is expected given the relative novelty of ride-hailing. The logistic regression models reveal that, although common attributes affect the likelihood that a person has adopted a ride-hailing service, the influence of these factors varies based on the specific type of service. This underscores the value of distinguishing between exclusive and shared ride-hailing services. The ZIOP model shows that attitudinal factors regarding qualitative trip characteristics, the inclination towards using ride-hailing services in certain situations, and the consideration of parking requirements affect the frequency with which a person uses ride-hailing. Also, transit pass ownership was found to influence the frequency with which a person uses ride-hailing positively. The results of this study aim to provide insights into the adoption and utilization of ride-hailing, which can help inform policies that aim to encourage the use of shared ride-hailing as an alternative to exclusive ride-hailing services.

Keywords: Ride-hailing; Zero-inflated ordered probit model; Latent attitudinal factors; Shared ride-hailing; Ride-hailing adoption; Transportation network company (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (13)

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DOI: 10.1016/j.tra.2021.02.002

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