Performance-Based Track Geometry, Phase 3 (2023)
Chapter: Chapter 1 Background
CHAPTER 1
Background
Poor vehicle dynamic performance and poor RQ frequently occur at track locations that do not exceed pre-set track geometry standards, such as curve entry or exit, combinations of several track geometry deviations, special trackwork, and track misalignments that promote lateral instability or hunting. Poor RQ may not be an indicator of unsafe operation but may point to a vehicle or to an area of track that needs maintenance to prevent further geometry degradation. Conversely, track geometry locations that exceed some track geometry or safety limits may not cause poor RQ or poor vehicle performance. To optimize transit system track maintenance, methods need to be developed to identify track conditions and locations that cause poor RQ or vehicle performance.
Track geometry measurements alone are not always an adequate indicator of how a vehicle behaves. Predicting an adverse vehicle dynamic response will help address:
- Prioritizing track maintenance
- Identifying problem track locations that do not exceed normal track geometry standards
- Identifying track issues as they arise rather than waiting for scheduled maintenance
- Identifying car operating speeds, designs, and car component wear issues that can contribute to poor vehicle performance and poor RQ
To improve and advance the current track geometry inspection practices and standards, MxV Rail (formerly Transportation Technology Center, Inc.) has developed a track inspection method known as PBTG. In the PBTG system, trained NNs relate the complex dynamic relationships that exist between vehicles and track geometry to vehicle performance.3 The PBTG system also 1) identifies track segments that may generate unwanted vehicle responses and 2) generates track geometry maintenance reports, recommending remedial actions and slow order speeds. A transit agency could use PBTG to optimize track maintenance, which allows monitoring of track conditions between scheduled track geometry measurements. PBTG also uses measured track geometry and the PBTG NN to predict vehicle performance on existing track. These predictions help identify track locations that are likely to cause poor RQ and that may require maintenance attention.
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3 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.
