The study investigates the latent heterogeneity in travel behaviour among urban travellers, including ride-hailing service (RHS) users and non-users, by incorporating attitudes so as to reinforce conventional user-segmentation approaches. Simultaneously, prioritisation of ride-hailing specific attributes was carried out to assess how RHS will operate in a sustainable way. The study initially examines latent heterogeneity in travellers through a Latent Class Cluster Analysis (LCCA) model. Subsequently, it prioritises key RHS-specific attributes for each cluster using three established Multi Criteria Decision Making (MCDM) techniques. Three clusters were identified based on individuals' attitudes and covariates (socio-demographics, travel habits, and built environment attributes). The largest cluster is the Tech-savvy ride-hailing-ready individuals (48%) with higher technological literacy, showing maximum acceptance towards ride-hailing. The second largest cluster comprises the Traditional active-mobility individuals (28%) who display the least proclivity towards RHS, probably due to their technological inhibition coupled with greater attachment to traditional travel alternatives. Lastly, the PV-loving multimodal individuals (24%) are primarily vehicle owners but prefer RHS for occasional trips. The final ranking obtained from the analysis has revealed that travel time, reliability, and flexibility are the motivators, while travel cost and waiting time are the deterrents, as perceived by the users, that influence RHS in the Indian context.
