Bundling, pricing schemes and extra features preferences for mobility as a service: Sequential portfolio choice experiment

Abstract

Mobility as a Service (MaaS) represents an innovative solution expected to induce people abandoning their cars in favor of more sustainable ways of travelling. However, a precondition for the successful implementation and diffusion of MaaS is public acceptance. In this paper, the latent demand for MaaS is estimated using a choice model based on a stated preference survey, conducted in the Netherlands. Specifically, a sequential experimental design approach based on portfolio choice is developed. It consists of two steps aimed to investigate (i) individual intention to subscribe to MaaS and preferences for bundle configurations, (ii) willingness to pay for extra features of the service. This article reports results of the analysis of the first step of the experiment. Two different mixed logit models are estimated to capture unobserved heterogeneity in individual’s preferences. First, a binary mixed logit model is formulated to estimate the effect of service attributes, social influence, socio-demographics and transportation-related characteristics on the decision to subscribe. Then, a mixed logit model is formulated to analyze the effects of transportation mode pricing schemes, and cross effects between transportation modes and individual characteristics on the choice which transportation modes to include in the subscription. The estimation results indicate that, overall, respondents are not yet inclined to subscribe to this new service. The service attribute characteristics, especially the price of the monthly subscription, and the social influence variables have an important effect on the subscription intention. The results also show that public transportation is the most preferred transportation mode, revealing that it may play a key role in MaaS platforms. The decision to subscribe and the choice which transportation modes to include in the bundle, appear to be significantly related to socio-demographic profiles and individuals’ transportation-related characteristics. The findings can be used as a valuable source of information for transportation planners and policy makers.