Stochastic assignment games for Mobility-as-a-Service markets
Bingqing Liu, David Watling, Joseph Y. J. Chow

TL;DR
This paper models multimodal mobility markets using stochastic assignment games extended into a Stackelberg framework, enabling MaaS platforms to optimize fares considering user and operator behaviors.
Contribution
It introduces a novel bilevel stochastic assignment game model for MaaS markets, incorporating a Stackelberg structure and an iterative solution algorithm.
Findings
The model effectively predicts optimal fare strategies for MaaS platforms.
The iterative algorithms converge to solutions that approximate the bilevel problem.
Case studies demonstrate practical applicability in real-world mobility systems.
Abstract
We study the stochastic assignment game and extend it to model multimodal mobility markets with a regulator or a Mobility-as-a-Service (MaaS) platform. We start by presenting general forms of one-to-one and many-to-many stochastic assignment games. Optimality conditions are discussed. The core of stochastic assignment games is defined, with expected payoffs of sellers and buyers in stochastic assignment games as payoffs from a hypothetical "ideal matching" that represent sellers' and buyers' expectations under imperfect information. To apply stochastic assignment games to the urban mobility markets, we extend the general stochastic many-to-many assignment game into a stochastic Stackelberg game to model MaaS systems, where the platform is the leader, and users and operators are the followers. The platform sets fares to maximize revenue. Users and operator react to the fare settings to…
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Taxonomy
TopicsTransportation and Mobility Innovations · Transportation Planning and Optimization · Vehicle Routing Optimization Methods
