Assessing the Factors Impacting Transport Usage of Mobility App Users in the National Capital Territory of Delhi, India

Abstract

Smartphone-based mobility apps have created a smartphone-enabled ecosystem of mobility services in developed countries and are slowly picking up pace in the Global South. Against this backdrop, this study used Latent Class Cluster Analysis to empirically investigate the impacts of mobility apps on transport usage patterns in Delhi by classifying users into three latent clusters based on socioeconomic characteristics, smartphone app usage, attitudes, and transport usage. Cluster 1 consisted of users with low app usage, and higher usage of public transport and intermediate public transport; Cluster 2 consisted of multimodal users with high app usage; and Cluster 3 consisted of users with moderate app usage and heavy reliance on private vehicles. Furthermore, the detailed characteristics of each latent class and factors affecting the individual’s probability of being classified into these clusters are discussed. It was found that younger users with higher education, more smartphone experience, medium-to-high household income and lower vehicle ownership had a very high probability of being classified as a multimodal traveler. Furthermore, the attitudes and preferences of users belonging to these clusters towards their choice of transport are discussed, along with a brief policy discussion for encouraging new app-based mobility services such as MaaS.