Abstract
Mobility as a Service (MaaS) is becoming a “fashionable” solution to increase transport users’ satisfaction and accessibility, by providing new services obtained by optimally integrating sustainable modes, but also guaranteeing mass transport and less sustainable modes, guaranteeing fast and lean access/egress to the mass transport. In this context, the understanding and prediction of travellers’ mode choices is crucial not only for the effective management of multimodal transport networks, but also successful implementation of new transport schemes. Traditional studies on mode choices typically treat travellers’ decision-making processes as planned behaviour. However, this approach is now challenged by the widely distributed, multi-sourced, and heterogeneous travel information made available in real time through information and communication technologies (ICT), especially in the presence of a variety of available mode options in dense urban areas. Some of the real-time factors that affect mode choices include availability of shared vehicles, real-time passenger information, unexpected disruptions, and weather. These real-time factors are insufficiently captured by existing mode choice models. This chapter aims to propose an introduction to MaaS, a literature review on mode choice paradigms, then it proposes a novel behavioural concept referred to as the hypermode. It will be illustrated a two-level mode choice decision architecture, which captures the influence of real-time events and travellers’ adaptive behaviour. A pilot survey shows the relevance of some real-time factors, and corroborates the hypothesized adaptive mode choice behaviour in both recurrent and occasional trip scenarios.
Highlights
Mobility as a Service (MaaS) can be considered as a tool to improve users’ mobility and, as stated by [1, 2] “MaaS provides an alternative way to move more people and goods in a way that is faster, cleaner, and less expensive than current options”
MaaS services have been implemented in several countries such as Germany (MOOVEL, Qixxit, BeMobility, HannoverMobil), Netherlands (Mobility Mixx, NS-Business card, Radiuz Total Mobility), Finland (WHIM, Tuup), Sweden (UBIGO), France (EMMA, Optymod), Austria (Smile, WienMobil lab), USA (SHIFT) and Singapore
The same first statement is uncertain, considering that it depends on MaaS diffusion, which in turn depends on the adopted business model, on its financial convenience and on the membership rate, which in turn depend on what kind of services are offered, their level of service and their price
Summary
MaaS can be considered as a tool to improve users’ mobility and, as stated by [1, 2] “MaaS provides an alternative way to move more people and goods in a way that is faster, cleaner, and less expensive than current options”. This research is an important undertaking as it identifies a set of new factors that influence mode choices, and presents a novel framework to study mode choice behaviour This behavioural paradigm may pose interesting challenges from a modelling perspective and may require an integrated modelling approach for both mode choice and traffic assignment to fully capture the adaptive behaviour. The latter statement stems from the observation that many real-time factors identified above have a dynamic and stochastic nature that is related to the evolution of the system (e.g. the dynamic network loading).
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