{
  "abstract": "Achieving a socially desirable operating point for a multimodal transportation system is challenging when Autonomous Mobility-on-Demand (AMoD) and Public Transit (PT) operators pursue selfish objectives alongside endogenous passenger choices. Existing equilibrium-based regulation models typically search over municipal policies to predict the induced operator equilibrium, creating strong behavioral assumptions, equilibrium-selection issues, and difficult bilevel optimization problems. This paper proposes an implementation-based alternative. Rather than asking which municipal action induces the best equilibrium, we ask: given a target operating profile, what minimum realized transfer makes unilateral deviation unattractive for each operator? Using k-implementation theory, this payment decomposes into two unilateral deviation gains: one for the AMoD operator and one for PT. Calculating this payment requires computing three distinct objects: the social target, the AMoD best response, and the PT best response. This is nontrivial because each represents a large-scale network optimization problem complicated by endogenous mode choice and congestion. To address this, we develop tailored mathematical formulations and algorithms for each oracle. For the social target, we derive a decomposition and entropy-regularized mixed-integer convex formulation balancing social optimality and implementability. For the AMoD oracle, we derive an exact reformulation, a convex relaxation providing a global upper bound, and a sequential convex approximation for feasible lower bounds. For the PT oracle, we develop a mixed-integer convex relaxation and characterize its exactness condition. A NYC case study shows the framework computes tight implementation-payment bounds and reveals how the dominant source of incentive misalignment shifts with congestion.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.18687",
  "author_total": 3,
  "authors": [
    {
      "name": "Xinling Li"
    },
    {
      "name": "Runyu Zhang"
    },
    {
      "name": "Gioele Zardini"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dttztj4tdfpm5.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.18687",
  "fulltext_url": null,
  "id": "dttztj4tdfpm5",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.18687",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "math.OC"
    ],
    [
      "openalex",
      "3313|Transportation and Mobility Innovations"
    ]
  ],
  "openalex": "W7161714375",
  "pdf_url": "https://arxiv.org/pdf/2605.18687",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dttztj4tdfpm5.ris",
  "sources": [
    {
      "first_seen": "2026-11-10T06:21:07Z",
      "native_id": "2605.18687",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.18687"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Implementation-Based Incentive Design for Autonomous Mobility-on-Demand and Transit Systems",
  "title_text": "Implementation-Based Incentive Design for Autonomous Mobility-on-Demand and Transit Systems",
  "type": "preprint",
  "updated_at": "2026-11-10T06:21:07Z",
  "url": "https://research.draconitic.com/paper/dttztj4tdfpm5",
  "venue": "arXiv",
  "versions": []
}