Abstract This paper presents an empirical investigation on demand for TNC services (e.g., Uber) in the Greater Toronto and Hamilton Areas (GTHA) through the application of an innovative discrete choice model. The proposed model combines Independent Availability Logit (IAL) and Constrained Multinomial Logit (CMNL) model formulation to reap the unique features of both. The proposed model is thus a Semi Compensatory Independent Availability Logit (SCIAL) model. For the empirical investigation, it uses a dataset of trip mode choices that suitable to represent ride-hailing service (e.g., Uber). Such trips are named as hailable trips in the dataset, which is drawn from a large scale household travel survey conducted in the region in 2016. To have a clear understanding of behavioural processes involved in the choice of travel mode of hailable trips, the proposed SCIAL model jointly models probabilistic choice set formation and conditional semi-compensatory choice. The empirical model does not reveal any evident competition between Uber and the private car, public transit, or non-motorized modes. It indicates that urban taxi is its main competitor, but there are notable differences in socio-demographic profiles of taxi and Uber users. For example, a taxi is preferred by older people, but younger people prefer uber, and there is no gender difference in such a pattern. In terms of the relationship between considering Uber as a feasible mode and choosing it for a trip, Uber has similarities to the car passenger mode. Merely accepting it as a feasible option has a significant influence on the final choice to use it. This indicates a potential new segment of the travel market, generated primarily for the advent of TNC service, e.g., Uber in Toronto.