Data-Driven Optimization and Planning of Multi-Component Track Responsive Maintenance with Defect Deterioration Modeling

Investigators: Qing He and Amjad Aref

Funding Source: USDOT/Federal Rail Administration

Abstract: Track geometrical defects and structural defects (e.g. rail cracks) are two leading causes for track failures with consequential train accidents. Train derailments can cost the rail stakeholders in terms of loss of revenue, property, environmental damage or even loss of life. Estimation of these costs and analysis of risks are important in deciding effective maintenance strategies. According to the 2013 national train accident statistics from Federal Railroad Administration (2013), track defects account for 33% of all train accidents, and it is the highest category among non-human causes.

The objective of this project is to develop an analytical solution, including both mathematical models and prototype tools, to assist multi-component data-driven track responsive maintenance (TRM). Specifically, the proposed work is composed of track deterioration modeling, track defect risk quantification, track defect rectification planning and optimization. The proposed project will serve as a theoretical and technical foundation to the state-of-practice in track inspection optimization research.