Performance Based Track Geometry Phase 2 (2015)

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C H A P T E R 1 Background Poor vehicle dynamic performance and poor ride quality frequently occur at track locations that do not exceed track geometry or safety standards, such as curve entry or exit, combinations of several track geometry deviations, special track work, and track misalignments that promote yaw instability or hunting. Poor ride quality may not be an indicator of unsafe operation, but may point to an area of track or a vehicle that needs maintenance to prevent further degradation. Conversely, track geometry locations that exceed some track geometry or safety limits may not cause poor ride quality or poor vehicle performance. To optimize transit system maintenance, methods need to be developed to identify vehicle conditions and locations in track that actually cause poor ride quality or vehicle performance. Track geometry measurements alone are not always an indicator of how a vehicle behaves. Predicting vehicle dynamic response will help address the following issues: • Prioritize track maintenance • Identify problem locations that do not exceed normal track geometry standards • Identify problems as they arise rather than waiting for scheduled maintenance • Identify car designs and car component wear issues that can contribute to poor vehicle performance and poor ride quality To improve and advance the current track geometry inspection practice and standards, Transportation Technology Center, Inc. (TTCI) has developed a track inspection method known as Performance Based Track Geometry (PBTG). Trained neural networks (NNs) in the PBTG system relate the complex dynamic relationships that exist between vehicles and track geometry to vehicle performance.1 NNs also identify track segments that may generate unwanted vehicle responses. A transit agency could use PBTG to optimize maintenance of the track and fleet, which allows monitoring of track conditions between scheduled track geometry measurements. Also, PBTG uses measured track geometry and the PBTG NN to predict vehicle performance on existing track. These predictions help to identify locations in the track likely to cause poor ride quality or other issues related to vehicle performance, which is the way PBTG is currently being applied by North American freight railroads. 1 Li, D., A. Meddah, K. Hass, and S. Kalay. March 2006. “Relating track geometry to vehicle performance using neural network approach.” Proc. IMECHE Vol. 200 Part F: J. Rail and Rapid Transit, 220 (F3), 273–282. 3

In support of the Transit Cooperative Research Program (TCRP) D7 Task 19 research program, TTCI completed Phase 2 of PBTG for the transit system research. The following tasks were completed during Phase 2: • Vehicle Characterization and On-track Testing – Port Authority Trans-Hudson (PATH) System • NUCARS®2 Modeling of PATH PA5 Car • Comparison of NUCARS simulations to on-track test results • PBTG NN for Dallas Area Rapid Transit (DART) and PATH system • Evaluation of the potential use of PBTG on Transit Systems (Proof of concept) 2 NUCARS® is a registered trademark of Transportation Technology Center, Inc., Pueblo, Colo. 4