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- W4315589039 abstract "Transit agencies have the opportunity to outsource certain services to well-established platform-based Mobility on Demand (MOD) providers. Such alliances can improve service quality, coverage, and ridership; reduce public sector costs and vehicular emissions; and integrate the passenger experience. To amplify the effectiveness of such alliances, we develop a fare-setting model that jointly optimizes discounted fares across a multimodal network. We capture commuters' travel choices with a discrete choice model, resulting in a large-scale, mixed-integer, non-convex optimization problem. To solve this challenging problem, we develop a two-stage decomposition with the pricing decisions in the first stage and a mixed-integer linear optimization problem optimizing fare discounts and the induced passenger behaviors in the second stage. To solve the decomposition, we develop a new solution approach combining tailored coordinate descent, parsimonious second-stage evaluations, and interpolations using special ordered sets. This approach, enhanced by acceleration techniques based on slanted traversal, randomization and warm-start, significantly improves system-wide practical outcomes over algorithmic benchmarks. Different alliance priorities result in qualitatively different fare designs: flat fares decrease the total vehicle-miles traveled, while geographically-informed discounts improve passenger happiness. The model responds appropriately to equity-oriented and passenger-centric priorities, improving system utilization and lowering prices for low-income residents and long-distance commuters. Finally, our revenue allocation mechanism improves outcomes for both operators, thus incentivizing profit-oriented MOD operators to adopt transit priorities." @default.
- W4315589039 created "2023-01-11" @default.
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- W4315589039 date "2023-01-09" @default.
- W4315589039 modified "2023-09-25" @default.
- W4315589039 title "Multimodal Transportation Alliance Design with Endogenous Demand: Large-Scale Optimization for Rapid Gains" @default.
- W4315589039 doi "https://doi.org/10.48550/arxiv.2301.03414" @default.
- W4315589039 hasPublicationYear "2023" @default.
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