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54 Data from a previous track geometry run were used for the tunnel. The tunnel has direct fixation track; and therefore, it was assumed that track geometry changes in the time since the previous run were negligible. 8.4 Ride Quality and Track Geometry Comparison A major objective of this research was to determine if there was a correlation between ride quality and track geometry. Places on the Red Line that had ride quality issues were identified from the ride quality test performed. The southbound section of track between Cedars and 8th These results indicated it should be possible to identify the effect of track geometry deviations on vehicle ride quality response during Phase II of this project. However, there is still some work required to improve the vehicle model to correctly predict this response. Identifying the influence of the following factors on vehicle response will be important to accurately model and determine track geometry triggers: & Corinth stations was uncomfortable with a ride quality index of 1.056. This section of track contained lateral alignment deviations with a wavelength of 94 feet, which corresponds to a frequency of 1 Hz at the speed the train was running. This resulted in a vehicle yaw response of 1 Hz, clearly indicating a correlation between track geometry and vehicle response. It is important to note that although these track geometry deviations did not exceed any safety criteria, they clearly affected passenger ride quality. In order to identify the track geometry issues that affect ride quality, it is imperative to take track geometry measurement at the same time as ride quality measurements. ⢠Wheel/rail interface, including profile shapes and contact geometry ⢠Vehicle speed ⢠Understanding and identifying rigid body vibration modes of the vehicle This work will be continued in Phase II. 9.0 WHAT IS NEXT: PHASE II The results of Phase I indicate it should be possible to identify the effect of track geometry deviations on vehicle ride quality response during Phase II of the project. However, there is still some work required to improve the vehicle model to correctly predict this response. Identifying the influence of the following factors on vehicle response is important to accurately model and determine track geometry triggers: ⢠Wheel/rail interface including profile shapes and contact geometry ⢠Vehicle speed ⢠Understanding and identifying rigid body vibration modes of the vehicle After all the issues have been investigated, the track geometry and ride quality data collected during Phase I at DART will be used to train neural networks to predict ride quality. The validated DART vehicle NUCARS model will be used to run simulations at different speeds to generate additional neural network training data. The neural networks
