A Data-Driven Operations-Intelligence Framework for Lifecycle-Aware Maintenance Decision-Making Across Tramway Superstructure Variants

Authors

  • Vivien Révész-Jóvér Department of Transport Infrastructure and Water Resources Engineering, Széchenyi István University, Győr, Hungary Author https://orcid.org/0000-0003-4593-853X
  • Szabolcs Fischer Department of Transport Infrastructure and Water Resources Engineering, Széchenyi István University, Győr, Hungary Author https://orcid.org/0000-0001-7298-9960

DOI:

https://doi.org/10.31181/jopi41202673

Keywords:

Tramway infrastructure, Predictive maintenance, Mixed-effects model, EN 13848 track twist, Bootstrap confidence interval, Lifecycle prediction, Elastically supported continuous rail bedding (ESCRB) rail, Concrete-slab track

Abstract

Urban-rail operators must convert routine track-geometry inspections into operational maintenance decisions across heterogeneous superstructures, yet age-based schedules and per-rail mean defects obscure the underlying deterioration mechanism. This paper develops an operations-intelligence framework that turns repeated geometric measurements into an explainable, data-driven ranking of sections by maintenance urgency. The framework is applied to 374,610 measurement points from 18 Budapest tramway sections, sampled 6 to 8 times over 2.3 years. Six analytical layers combine descriptive statistics; both-rail unified metrics including the EN 13848 track twist; an EN 13848-style Track Quality Index; hierarchical mixed-effects models; bootstrap (B = 10,000) and Theil–Sen confidence intervals; and AIC-based linear-versus-exponential model selection. Degradation proceeds mainly through twist generation – right- and left-rail slopes correlate strongly negatively (r = −0.864, p < 0.0001, n = 18). Concrete-slab gauge depends strongly on age (R² = 0.72, n = 24); mixed-effects models attribute 60.3% of gauge variance to between-section identity; the linear model is selected in 94% of fits; and a 3.0 mm threshold flags four sections for intervention within ten years. The pipeline feeds directly into operational decision-making and maintenance management systems and is transferable across sectors, relying on periodic condition monitoring.

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Révész-Jóvér, V., & Fischer, S. (2026). Asymmetric Track Geometry Degradation of Tramway Superstructure Systems: A Statistical Reassessment from Repeated Handheld Measurements in Budapest. 5th Cognitive Mobility Conference (CogMob 2026), submitted manuscript under review.

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Published

2026-07-17

Issue

Vol. 4 No. 1 (2026): Journal of Operations Intelligence

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Articles

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Copyright (c) 2026 Vivien Révész-Jóvér, Szabolcs Fischer (Author)

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Révész-Jóvér, V., & Fischer, S. (2026). A Data-Driven Operations-Intelligence Framework for Lifecycle-Aware Maintenance Decision-Making Across Tramway Superstructure Variants. Journal of Operations Intelligence, 4(1), 42-64. https://doi.org/10.31181/jopi41202673