Joint Model of Application-Based Ride Hailing Adoption, Intensity of Use, and Intermediate Public Transport Consideration among Workers in Chennai City

Document Type

Journal Article

Publication Date


Subject Area

place - asia, ridership - demand, ridership - commuting, ridership - perceptions


Commuters, Commuting, Employees, Mobile applications, Mode choice, Probits, Public transit, Ridesourcing, Shared mobility, Work trips


The introduction of mobile application-based ride hailing services represents a convergence between technologies, supply of vehicles, and demand in near real time. There is growing interest in quantifying the demand for such services from regulatory, operational, and system evaluation perspectives. Several studies model the decision to adopt ride hailing and the extent of the use of ride hailing, either separately or by bundling them into a single choice dimension, disregarding potential endogeneity between these decisions. Unlike developed countries, the literature is sparser for ride hailing in developing countries, where the demand may differ considerably because of differences in vehicle ownership, and availability and patronage of many transit and intermediate public transport (IPT) modes (the shared modes carrying 40% shares in some cases). This study aims to bridge these gaps in the literature by investigating three interrelated choice dimensions among workers in Chennai city: consideration of IPT modes, the adoption of ride hailing services and the subsequent usage intensity of ride hailing services. The main factors influencing these decisions are identified by estimating a trivariate probit model. The results indicate that sociodemographic and locational characteristics and the availability of IPT modes influence ride hailing adoption, whereas work-related constraints and perception of other modes affect its frequency. Work and non-work characteristics affect both the dimensions of ride hailing. Further, endogeneity is observed between ride hailing and IPT adoption after controlling for these variables, whereas evidence of endogeneity is absent among other dimensions. Mainly, the model separates the effect of the exogenous influences on the usage frequency level from their effect on the adoption of ride hailing services.


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