Incentive-compatible mechanisms for online resource allocation in mobility-as-a-service systems

Abstract

Mobility as a Service (MaaS) has recently received a significant attention from researchers, industry stakeholders, and the public sector. The vast majority of existing MaaS paradigms are articulated based on the traditional segmentation of travel modes, e.g. private vehicle, public transportation (bus, metro, light rail) and shared mobility (car/bike/ride-sharing, ride-sourcing). In the context of 'Everything-as-a-Service' (XaaS), service providers have evolved from productbased models towards less segmented representations in which resources are priced in a continuous fashion. Yet, such continuous resource allocation formulations are inexistent for MaaS systems. This study attempts to address this gap in the literature by introducing innovative MaaS mechanisms that allocate mobility resources to users without any form of travel mode segmentation. We introduce an online auction framework where travelers have the possibility to bid for continuous mobility resources based on their requirements and willingness to pay (WTP). We propose two MaaS mechanisms, named Pay-as-You-Go (PAYG) and Pay-as-a-Package (PAAP), which allow travelers to either pay for the immediate use of mobility services or to subscribe to mobility service packages for a more protracted usage. Both MaaS mechanisms are based on mixed-integer or linear programming formulations designed to maximize social welfare in the transport system. We show that the proposed PAYG and PAAP mechanisms are incentive-compatible thus promoting truthful user bidding behavior. We develop efficient online primal-dual algorithms to implement the proposed MaaS mechanisms and derive theoretical bounds on the worst-case performance of these algorithms. Moreover, we design a rolling horizon framework to incorporate booking flexibility and improve the social welfare obtained by the proposed online algorithms. Numerical results on extensive problem instances generated from realistic mobility data highlight the benefits of the proposed MaaS mechanisms, and quantify the trade-offs among the proposed approaches.

Modeling, equilibrium, and demand management for mobility and delivery services in Mobility‐as‐a‐Service ecosystems

“…Let 𝑑 denote the average distance between an independent OD pair, let 𝑡 𝑖 be the transport time of MaaS bundle 𝑖 between an OD pair, and the corresponding mobility or delivery resources 𝑧(𝑡 𝑖 ) are defined as 𝑧(𝑡 𝑖 ) ≜ 𝑑 2 𝑡 𝑖 , ∀𝑖 ∈  (Xi, Liu, et al, 2020), thus the mobility fare and delivery fare of selecting MaaS bundle 𝑖 can be calculated based on Equation (26).…”

Single-leader multi-follower games for the regulation of two-sided Mobility-as-a-Service markets

“…In this study, we build on the mathematical optimization formulation proposed by Xi et al (2020) for mobility resource allocation in MaaS systems, and adapt it in the context of two-sided MaaS markets. In this framework for MaaS systems, the average speed of different travel modes is assumed known and representative of the "commercial speed" of each mode.…”

“…To meet a traveler request, the MaaS operator can provide a diverse combination of mobility options across multiple travel modes, including public transport, ride-, car-or bike-sharing, taxi; hereby referred to as a MaaS bundle. Xi et al (2020) proposed an innovative paradigm for MaaS systems in which travelers can bid for any quantity of mobility resources in a mode-agnostic fashion with preference requirements, and the MaaS regulator allocates the mobility resources integrated from various transportation service providers (TSPs) to meet heterogeneous traveler requests. Building on this flexible and tractable MaaS framework, we consider a MaaS system under economic deregulation (Wong, Hensher, and Mulley 2020), which Xi et al: Single-leader multi-follower games for the regulation of two-sided Mobility-as-a-Service markets Article submitted to Transportation Science; manuscript no.…”