{
  "abstract": "We propose a causal machine learning framework to integrate dynamic tolling for mobility, equity, and climate objectives, resolving a long-standing policy trilemma. Using Double Machine Learning, we estimate zone-type-specific treatment effects, showing that commercial zones yield nearly twice the delay reduction of residential zones. Our adaptive quota mechanism expands residential coverage 34-fold while preserving 93% of total delay savings and sustaining high return on investment. The framework delivers climate co-benefits, reducing emissions by 743.5 metric tons of CO2 (valued at 141 USD) via congestion-sensitive pricing under moderate traffic conditions with sensitivity bounds from 557.6 to 929.3 tons. Crucially, we uncover a policy-invariant toll range where performance remains robust across demand scenarios, simplifying implementation. This study reveals critical, previously unidentified insights, such as a policy-invariant toll range, that can significantly simplify real-world implementation. Validated on synthetic data mirroring real-world networks, our methodological advances lay the foundation for future public deployment.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Journal of Intelligence and Information Systems 32 (2026) 383-412",
  "arxiv": "2610.33994",
  "author_total": 1,
  "authors": [
    {
      "name": "Christian Mulomba Mukendi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/56p5yxj8yx9fd.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.33994",
  "fulltext_url": null,
  "id": "56p5yxj8yx9fd",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.33994",
  "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",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "arxiv",
      "stat.OT"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.33994",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-25",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/56p5yxj8yx9fd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:49Z",
      "native_id": "2610.33994",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.33994"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Resolving the Mobility-Equity-Emissions Trilemma: A Causal Machine Learning Framework for Dynamic Tolling Policy",
  "title_text": "Resolving the Mobility-Equity-Emissions Trilemma: A Causal Machine Learning Framework for Dynamic Tolling Policy",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:49Z",
  "url": "https://research.draconitic.com/paper/56p5yxj8yx9fd",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.13088/jiis.2026.32.3.383",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}