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.

该研究通过整合态度来调查城市旅行者（包括网约车服务 （RHS） 用户和非用户）出行行为的潜在异质性，以加强传统的用户细分方法。同时，对网约车的特定属性进行了优先排序，以评估 RHS 将如何以可持续的方式运营。该研究最初通过潜在类别聚类分析 （LCCA） 模型检查旅行者的潜在异质性。随后，它使用三种已建立的多标准决策 （MCDM） 技术为每个集群确定关键 RHS 特定属性的优先级。根据个人的态度和协变量 （社会人口统计学、旅行习惯和建筑环境属性） 确定了三个集群。最大的群体是精通技术、准备打车的人 （48%），他们具有较高的技术素养，对网约车的接受度最高。第二大集群包括传统主动行动者 （28%），他们对 RHS 的倾向性最低，这可能是由于他们的技术抑制加上对传统旅行选择的更依恋。最后，喜欢 PV 的多式联运者 （24%） 主要是车主，但更喜欢偶尔出行时使用 RHS。从分析中获得的最终排名表明，旅行时间、可靠性和灵活性是动机，而用户认为旅行成本和等待时间是影响印度 RHS 的威慑因素。