Joint Estimation of Ride-Hailing Services (RHS) Adoption and Frequency: Assessing Impacts of Built Environment on Work and Discretionary Trips

Abstract

In recent years, ride-hailing services (RHS) (also known as on-demand ride services), such as Uber, Ola, Lyft, Didi, etc., have transformed the urban transportation environment. RHS promises to promote sustainable urban mobility as it combines the flexibility of personal vehicles and the shared nature of public transport. In developing countries like India, research on these emerging RHS is still in its infancy, and the role of the built environment (BE) in influencing RHS choice and usage has not been explored. The current study aims to do so, for the city of Kolkata, India which has the highest modal share of ride-hailing amongst million-plus cities in India. Revealed preference (RP) household surveys were conducted, and information on 841 ride-hailing user trips was collected and a semi-ordered bivariate probit model was developed to estimate RHS adoption and usage frequency simultaneously. Model results show that BE variables like destination accessibility, bus stop density, road density, and population density significantly influence the adoption of RHS and use frequency of RHS with varied intensity for work and discretionary trips. Users residing in neighbourhoods with higher accessibility and better public transit connectivity are the least likely to adopt RHS and are also likely to be infrequent users. On the other hand, individuals living in high-density neighbourhoods are more likely to adopt RHS. Also, with increasing distances between origin and destination, commuters tend to adopt and use RHS frequently.