- An Automated System for Rail Transit Infrastructure Inspection
University of Massachusetts
University of Vermont
University of Alaska Fairbanks
Track inspection is critical for both freight and transit rail safety. Currently, rail transit agencies rely heavily on visual inspection, which is time and resource consuming and sometimes unreliable. To address this issue, this research introduces a system that integrates Ground Penetrating Radar (GPR), 3D laser, GIS, and GPS to automatically collect and georeference surface and subsurface rail data. Algorithms have been developed to interpret the collected data and to identify rail infrastructure surface and subsurface defects, such as broken ties, missing bolts, and fouled ballast. Also, a WebGIS-based decision support system with user-friendly interface is being developed to help rail transit employees without GPR and laser background to utilize the data. Preliminary results reveal that the newly proposed system is a promising and reliable alternative compared to visual inspection method. The developed system is also cost-effective and can be readily mounted on a hi-rail vehicle that virtually every rail transit agency owns. With the aging rail infrastructure, this automatic system is expected to substantially benefit the rail transit industry by improving track inspection efficiency, accuracy, and the safety of both rail transit systems and track workers.
Yuanchang Xie, Ph.D., P.E., is an assistant professor in the Department of Civil and Environmental Engineering at the University of Massachusetts (UMass) Lowell. Tian Xia is an associate professor with the School of Engineering, University of Vermont. Jenny Liu, Ph.D., P.E., Director of CESTiCC, is an associate professor in the Department of Civil and Environmental Engineering at the UAF.