On-demand Mobility-as-a-Service platform assignment games with guaranteed stable outcomes

• URL: http://arxiv.org/abs/2305.00818v2
• Date: Fri, 21 Jun 2024 07:35:25 GMT
• Title: On-demand Mobility-as-a-Service platform assignment games with guaranteed stable outcomes
• Authors: Bingqing Liu, Joseph Y. J. Chow,
• Abstract summary: Mobility-as-a-Service (M) systems are two-sided markets, with two mutually exclusive sets of agents, i.e., travelers/users and operators. This study proposes a MaaS platform equilibrium model based on many-to-many assignment games incorporating both fixed-route transit services and mobility-on-demand (MOD) services.
• Score: 3.10770247120758
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Mobility-as-a-Service (MaaS) systems are two-sided markets, with two mutually exclusive sets of agents, i.e., travelers/users and operators, forming a mobility ecosystem in which multiple operators compete or cooperate to serve customers under a governing platform provider. This study proposes a MaaS platform equilibrium model based on many-to-many assignment games incorporating both fixed-route transit services and mobility-on-demand (MOD) services. The matching problem is formulated as a convex multicommodity flow network design problem under congestion that captures the cost of accessing MOD services. The local stability conditions reflect a generalization of Wardrop's principles that include operators' decisions. Due to the presence of congestion, the problem may result in non-stable designs, and a subsidy mechanism from the platform is proposed to guarantee local stability. A new exact solution algorithm to the matching problem is proposed based on a branch and bound framework with a Frank-Wolfe algorithm integrated with Lagrangian relaxation and subgradient optimization, which guarantees the optimality of the matching problem but not stability. A heuristic which integrates stability conditions and subsidy design is proposed, which reaches either an optimal MaaS platform equilibrium solution with global stability, or a feasible locally stable solution that may require subsidy. For the heuristic, a worst-case bound and condition for obtaining an exact solution are both identified. An expanded Sioux Falls network test with 82 nodes and 748 links derives generalizable insights about the model for coopetitive interdependencies between operators sharing the platform, handling congestion effects in MOD services, effects of local stability on investment impacts, and illustrating inequities that may arise under heterogeneous populations.

Related papers

• Platform-Aware Mission Planning [50.56223680851687]
  We introduce the problem of Platform-Aware Mission Planning (PAMP), addressing it in the setting of temporal durative actions. The first baseline approach amalgamates the mission and platform levels, while the second is based on an abstraction-refinement loop. We prove the soundness and completeness of the proposed approaches and validate them experimentally.
  arXiv  Detail & Related papers  (2025-01-16T16:20:37Z)
• Multiple Mean-Payoff Optimization under Local Stability Constraints [2.95348334737984]
  The long-run average payoff per transition (mean payoff) is the main tool for specifying the performance and dependability properties of discrete systems. The problem of constructing a controller (strategy) simultaneously optimizing several mean payoffs has been deeply studied for and game-theoretic models. In this paper, we design and evaluate the first efficient and scalable solution to this problem applicable to Markov decision processes.
  arXiv  Detail & Related papers  (2024-12-17T22:53:08Z)
• NodeOP: Optimizing Node Management for Decentralized Networks [8.225105658045843]
  We present NodeOP, a novel framework designed to optimize the management of General Node Operators in decentralized networks. By integrating Agent-Based Modeling (ABM) with a Tendermint Byzantine Fault Tolerance (BFT)-based consensus mechanism, NodeOP addresses key challenges in task allocation, consensus formation, and system stability.
  arXiv  Detail & Related papers  (2024-10-22T06:00:04Z)
• Reconfigurable Intelligent Surface (RIS)-Assisted Entanglement Distribution in FSO Quantum Networks [62.87033427172205]
  Quantum networks (QNs) relying on free-space optical (FSO) quantum channels can support quantum applications in environments where establishing an optical fiber infrastructure is challenging and costly. A reconfigurable intelligent surface (RIS)-assisted FSO-based QN is proposed as a cost-efficient framework providing a virtual line-of-sight between users for entanglement distribution.
  arXiv  Detail & Related papers  (2024-01-19T17:16:40Z)
• Safe Model-Based Multi-Agent Mean-Field Reinforcement Learning [48.667697255912614]
  Mean-field reinforcement learning addresses the policy of a representative agent interacting with the infinite population of identical agents. We propose Safe-M$3$-UCRL, the first model-based mean-field reinforcement learning algorithm that attains safe policies even in the case of unknown transitions. Our algorithm effectively meets the demand in critical areas while ensuring service accessibility in regions with low demand.
  arXiv  Detail & Related papers  (2023-06-29T15:57:07Z)
• Multi-agent Attention Actor-Critic Algorithm for Load Balancing in Cellular Networks [33.72503214603868]
  In cellular networks, User Equipment (UE) handoff from one Base Station to another, giving rise to the load balancing problem among the BSs. This paper formulates the load balancing problem as a Markov game and proposes a Robust Multi-agent Attention Actor-Critic (Robust-MA3C) algorithm.
  arXiv  Detail & Related papers  (2023-03-14T15:51:33Z)
• Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems [31.23491481430466]
  We consider the sequential decision-making problem of making proactive request assignment and rejection decisions for a profit-maximizing operator of an autonomous mobility on demand system. We formalize this problem as a Markov decision process and propose a novel combination of multi-agent Soft Actor-Critic and weighted bipartite matching to obtain an anticipative control policy.
  arXiv  Detail & Related papers  (2022-12-14T16:19:51Z)
• Switchable Representation Learning Framework with Self-compatibility [50.48336074436792]
  We propose a Switchable representation learning Framework with Self-Compatibility (SFSC) SFSC generates a series of compatible sub-models with different capacities through one training process. SFSC achieves state-of-the-art performance on the evaluated datasets.
  arXiv  Detail & Related papers  (2022-06-16T16:46:32Z)
• Low-Latency Federated Learning over Wireless Channels with Differential Privacy [142.5983499872664]
  In federated learning (FL), model training is distributed over clients and local models are aggregated by a central server. In this paper, we aim to minimize FL training delay over wireless channels, constrained by overall training performance as well as each client's differential privacy (DP) requirement.
  arXiv  Detail & Related papers  (2021-06-20T13:51:18Z)
• Pointwise Feasibility of Gaussian Process-based Safety-Critical Control under Model Uncertainty [77.18483084440182]
  Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs) are popular tools for enforcing safety and stability of a controlled system, respectively. We present a Gaussian Process (GP)-based approach to tackle the problem of model uncertainty in safety-critical controllers that use CBFs and CLFs.
  arXiv  Detail & Related papers  (2021-06-13T23:08:49Z)
• A Modular and Transferable Reinforcement Learning Framework for the Fleet Rebalancing Problem [2.299872239734834]
  We propose a modular framework for fleet rebalancing based on model-free reinforcement learning (RL) We formulate RL state and action spaces as distributions over a grid of the operating area, making the framework scalable. Numerical experiments, using real-world trip and network data, demonstrate that this approach has several distinct advantages over baseline methods.
  arXiv  Detail & Related papers  (2021-05-27T16:32:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.