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

TL;DR

This paper introduces auction-based online mechanisms for mobility-as-a-service systems that enable users to bid for mode-agnostic resources, aiming to maximize social welfare while ensuring incentive compatibility.

Contribution

It presents novel incentive-compatible auction mechanisms for online MaaS resource allocation, accommodating user bids for flexible, mode-agnostic mobility services.

Findings

Mechanisms are incentive-compatible and promote truthful bidding.

Proposed algorithms achieve competitive performance bounds.

Numerical simulations demonstrate effectiveness and benefits of the mechanisms.

Abstract

In the context of `Everything-as-a-Service', the transportation sector has been evolving towards user-centric business models in which customized services and mode-agnostic mobility resources are priced in a unified framework. Yet, in the vast majority of studies on Mobility as a Service (MaaS) systems, mobility resource pricing is based on segmented travel modes, e.g. private vehicle, public transit and shared mobility services. This study attempts to address this research gap by introducing innovative auction-based online MaaS mechanisms where users can bid for any amount of mode-agnostic mobility resources based on their willingness to pay and preferences. We take the perspective of a MaaS regulator which aims to maximize social welfare by allocating mobility resources to users. We propose two mechanisms which allow users to either pay for the immediate use of mobility service (pay-as-you-go), or to subscribe to mobility service packages (pay-as-a-package). We cast the proposed auction-based mechanisms as online resource allocation problems where users compete for MaaS resources and bid for travel time per trip. We propose (integer-) linear programming formulations to accommodate user bids based on available mobility resources in an online optimization approach. We show that the proposed MaaS mechanisms are incentive-compatible, develop customized online algorithms and derive performance bounds based on competitive analysis. Extensive numerical simulations are conducted on large scale instances generated from realistic mobility data, which highlight the benefits of the proposed MaaS mechanisms and the effectiveness of the proposed online optimization approaches.