Track


Dallas tests innovative track geometry system


A performance-based track geometry system incorporating trained neural networks has been developed by TTCI. It is already being used by four railways and is now being tested on the Dallas light rail network, as senior engineer Charity Duran Ketchum explains.


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OOR vehicle dynamic performance and ride quality may occur at track locations that do not exceed track geometry or safety standards, such as curve entry or exit, special trackwork, 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 track geometry or safety standards do not often cause poor ride quality or poor vehicle performance. To improve and advance current track geometry inspection practices and maintenance procedures, Transportation Technology Center Inc (TTCI) developed a track inspection method known as performance-based track geometry (PBTG). Trained neural networks within PBTG relate the


IRJ August 2012


complex dynamic relationships that exist between vehicles and track geometry to vehicle performance. They also identify track segments that may generate unwanted vehicle responses. PBTG is now in use with three North American railways and an overseas one, and is also suitable for use by a transit system to optimise track and fleet maintenance. Onboard accelerometers and a PBTG neural network identify track locations that need work without direct measurement of the track geometry. This allows monitoring of track condition between scheduled track geometry measurements.


PBTG can also identify cars whose performance is beginning to deteriorate. If all cars in the fleet are equipped with PBTG accelerometers, it is possible to build a database of information to monitor the condition of both the cars and the track over time. PBTG also uses measured track


geometry and the PBTG neural network to predict vehicle performance on the track. This helps identify locations in the track likely to cause poor ride quality or other issues related to vehicle performance, which is how North American freight railways are currently applying PBTG. To support the Transit Cooperative


Research Programme, TTCI conducted research to develop methods for evaluating track geometry that account for transit vehicle performance and passenger ride quality using a combination of modelling techniques with PBTG and Nucars. These studies will help to determine improvements in track geometry and track maintenance practices to be developed in Phase II of the project.


Nucars simulations and data collected on transit systems are being used to train PBTG neural networks to evaluate the model’s ability to predict ride quality. The goal is to ascertain the


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