Effect of Mixed Higher-Speed Traffic Introduction on Track Degradation Leveraging a Novel Degradation Cycle Identification Approach

Integrating higher-speed passenger service into existing freight rail corridors creates new challenges for managing track performance, maintenance, and long-term sustainability. This study evaluated how introducing higher-speed rail (HrSR) service, up to 177 km/h, affected track geometry degradation along a 64-km double-track freight corridor in the United States. A novel data-driven approach was developed to identify track degradation cycles using cross-correlation analysis of high-frequency geometry data. The method detects cycles not interrupted by maintenance events and calculates degradation rates through regression of vertical profile standard deviation, achieving <10% false positives when compared to maintenance records. A linear regression model examined how track structure, curvature, maximum allowable speed, and HrSR service introduction influenced degradation rates; cluster-robust covariance estimation was used to account for spatial and temporal dependence. Results showed that degradation rates decreased across all track types following the introduction of HrSR service (−33 to −60%), likely due to improved maintenance efficiency and tighter geometry standards under FRA Class 6 operation. However, legacy secondary main tracks and sidings continued to degrade faster than newer or mainline sections (+100% before HrSR, +50% after HrSR), emphasizing the lasting influence of historical maintenance and substructure conditions. Degradation rate increased with curvature, although tangent track degraded faster (+50%) than shallow curves but slower (−50%) than curves exceeding 1.5°, potentially due to interaction between dynamic axle loads and lateral loads. Lower-speed segments also exhibited higher degradation (+200%), suggesting effects of geometrically or structurally constrained locations rather than being a direct effect of train speed. The proposed methodology provides an objective and scalable framework for quantifying track degradation and identifying maintenance priorities. It enables asset managers to better monitor, sustain, and optimize mixed-use rail infrastructure by supporting data-driven decisions for renewal and maintenance planning, thereby improving long-term resilience and operational efficiency under evolving traffic and performance standards.