Ridesharing in the era of Mobility as a Service (MaaS): An Activity-based Approach with Multimodality and Intermodality

TL;DR

This paper introduces a comprehensive MaaS-based activity travel pattern generator and a dynamic rideshare model that integrate multimodal and intermodal transport options, improving ridesharing system performance analysis.

Contribution

It develops a novel mathematical framework for MaaS-based activity patterns and integrates it with ridesharing models, bridging a key gap in current transportation planning literature.

Findings

MaaS-based planning significantly impacts ridesharing system performance.

The proposed models enable detailed analysis of multimodal and intermodal travel behaviors.

Numerical examples demonstrate the effectiveness of the integrated approach.

Abstract

Mobility as a Service (MaaS), as an emerging concept, is quickly evolving and at the same time irreversibly reshaping travellers behaviour by facilitating their accessibility to different transport modes using shared economy concepts. Managing the complex multimodal and intermodal system of quickly emerging MaaS, requires a holistic modelling framework of transport network incorporating all the influential aspects and means involved. In this paper, we mathematically formulate a novel MaaS-based activity travel pattern (ATP) generator to facilitate ridesharing in a system in which drivers and passengers interact by sharing their full activity diary. The proposed formulation extends the definition of MaaS beyond an intermodal trip planner, by incorporating an inclusive set of travel attributes including the choices of activity, activity sequence, departure time, and mode, and the transitions among the modes in ATPs of all participants. Furthermore, this paper introduces a dynamic rideshare-oriented MaaS model in which the conventional rideshare modelling structure is synchronised with the proposed MaaS-based ATP generator in a unified structure. These models explicitly bridge the gaps in the missed-out connection between ATPs planning and rideshare models in the current literature. At last, numerical examples are provided to demonstrate the significant impacts of the MaaS-based planning on the ridesharing systems performance.