Optimizing MaaS for Commuters: Exploring Ride-Pooling Potentials

Abstract

Individual commuter traffic, characterized by low car occupancy and significant emissions, offers opportunities for ride-pooling as a sustainable alternative. This paper introduces a methodology that integrates a dynamic demand model using agent-based simulations with ride-pooling service and fleet optimization. The approach captures scenario-specific demand variations and enables detailed fleet simulations while exploring service parameters, including pricing, detour limits, and service constraints. It couples service parameters and service demand, while optimizing mobility services within a large search space. A case study applied in suburban and rural regions demonstrates the methodology’s potential to improve pooling efficiency and operational performance. While the results highlight promising opportunities, further enhancements, such as stop-based systems and intermodal integration, are needed to maximize scalability and pooling potential. This dynamic approach provides the foundation for a holistic, commuter-focused ride-pooling service optimization. It addresses demand responsiveness, fleet management, and the diverse objectives of commuters, providers, and transit agencies.