Governed negotiation for human-centric disruption-aware railway dispatching in flatland. Methodology and pilot validation
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Autor:in (Körperschaft)
Publikationsdatum
2026
Typ der Arbeit
Studiengang
Typ
04B - Beitrag Konferenzschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
2026 1st International Conference on Human Centric Artificial Intelligence (ICHCAI)
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Verlag / Herausgebende Institution
IEEE
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Halden
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Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Major rail disruptions create dense conflicts that are still largely resolved manually under time pressure. We present a reproducible, non-learning-based dispatch methodology for human-supervised decision support that combines (i) short-horizon reservation negotiation, (ii) deterministic conflict arbitration with explicit anti-starvation guarantees, and (iii) a bounded governance layer with auditable parameter updates. The design supports bounded mixed-initiative control: dispatchers can inspect local rationales and steer policy parameters within explicit limits while preserving deterministic behavior and traceability. In a five-scenario Flatland pilot suite (paired seeds, bootstrap confidence intervals (CIs)), the stress-test BASELINE fails in all scenarios (strict deadlock 1.0 everywhere; throughput 0.0 in four scenarios and 0.015 in one), while NEGOTIATION and FULL resolve all scenarios (strict deadlock 0.0, throughput 1.0). On the four hard scenarios, NEGOTIATION_FIFO (First-In-First-Out arbitration) improves makespan with unchanged robustness, indicating that reservation plus anti-starvation is the dominant robustness driver and local ranking is a secondary efficiency lever. FULL-minus-NEGOTIATION paired deltas quantify the net effect of enabling governance under the configured policy, while governance intervention tables provide descriptive evidence of when and how updates are applied. Component-level governance causality (adaptive scheduling vs. patch policy vs. source) is not isolated. Ablations and a global Nstarve sweep provide mechanism evidence, including a monotonic efficiency-fairness trade-off. Evidence remains simulator-level and does not include real signaling, timetable constraints, user studies, or quantified human-factors impact.
Schlagwörter
Fachgebiet (DDC)
Veranstaltung
2026 1st International Conference on Human Centric Artificial Intelligence (ICHCAI)
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
27.05.2026
Enddatum der Konferenz
28.05.2026
Datum der letzten Prüfung
ISBN
979-8-3315-5118-6
979-8-3315-5119-3
979-8-3315-5119-3
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
peer-reviewed
Open Access-Status
Closed
Zitation
Ließner, R., Renold, M., Usher, J., Egli, A., Boos, D., Meyer, M., & Schneider, M. (2026). Governed negotiation for human-centric disruption-aware railway dispatching in flatland. Methodology and pilot validation. 2026 1st International Conference on Human Centric Artificial Intelligence (ICHCAI). 2026 1st International Conference on Human Centric Artificial Intelligence (ICHCAI). https://doi.org/10.1109/ichcai70183.2026.11607599