Performance Based Track Geometry Phase 2 (2015)

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Figure 31. Lateral Frequency Response ......................................................................................... 29 Figure 32. Vertical Frequency Response ........................................................................................ 29 Figure 33. Measured Vertical Accelerations Compared with Predicted Vertical Accelerations .... 31 Figure 34. Measured Lateral Accelerations Compared to Predicted Lateral Accelerations ........... 32 Figure 35. Measured Vertical Acceleration Frequency Content Compared with Predicted Frequency Content ............................................................................................................ 33 Figure 36. Measured Lateral Acceleration Frequency Content Compared with Predicted Frequency Content ............................................................................................................ 33 Figure 37. NN Schematic ............................................................................................................... 34 Figure 38. DART – Alignment of Ride Quality Data and Track Geometry Data .......................... 36 Figure 39. DART – Neural Net Training Data for Point-by-Point Approach ................................ 37 Figure 40. DART – Point-by-Point Approach deployed on LBJ to Spring Valley Station Data ... 37 Figure 41. DART – Segment-based Approach deployed on LBJ to Spring Valley Station Data .. 38 Figure 42. PATH Training and Validation Data at Front Carbody Acceleration ........................... 39 Figure 43. PATH Training and Validation Data at Center Carbody Acceleration ......................... 40 Figure 44. PATH Training and Validation Data at Driver Cab Carbody Acceleration.................. 40 iii

List of Tables Table 1. PATH Design Specifications .............................................................................................. 7 Table 2. Measured Rigid Body Mode Frequencies Compared to Model ......................................... 9 Table 3. Longitudinal Suspension Characteristics ......................................................................... 11 Table 4. Lateral Suspension Characteristics ................................................................................... 12 Table 5. Secondary Suspension Vertical Stiffness ......................................................................... 14 Table 6. Primary Suspension Stiffness ........................................................................................... 15 Table 7. Ride Quality Test Accelerometer Locations .................................................................... 16 Table 8. Passenger Ride Quality Index .......................................................................................... 19 Table 9. VDV Values that exceeded Fairly Uncomfortable Ride Quality Index ........................... 20 Table 10. VDV Values that exceeded Fairly Uncomfortable Ride Quality Index ......................... 25 Table 11. Frequency Response of Vehicle and Corresponding Wavelength in Track ................... 42 iv

Summary In support of the Transit Cooperative Research Program (TCRP) D7 Task Order 19, Transportation Technology Center, Inc. completed Phase 2 of Performance Based Track Geometry (PBTG). The following tasks were completed during Phase 2: • Vehicle Characterization and On-track Testing with Port Authority Trans-Hudson (PATH) System • NUCARS® Modeling of PATH PA5 Car • Comparison of NUCARS simulations to on-track test results • PBTG Neural Network (NN) development for Dallas Area Rapid Transit (DART) and PATH systems • Evaluation of the potential use of PBTG on transit systems (Proof of concept) Vehicle Characterization and On-track Testing with PATH System NUCARS Modeling of PATH PA5 Car PATH PA5 car was selected and fully characterized and ride quality tests were performed. The NUCARS model of the PATH PA5 car was updated with information collected during vehicle characterization testing. Then, the NUCARS model was validated using test results from on-track testing on the PATH system. The simulation correctly predicted the frequency content and trends of lateral and vertical accelerations measured in the driver’s cab during on-track testing. There was some deviation in the magnitudes. The differences may be due to the following: • Dampers of the PA5 car were not measured. Damping rates from the manufacturer for new dampers were used. • Representative rail profiles were used in the simulations. Actual wheel/rail interaction is influenced by the shapes of the wheels and rails. Comparison of NUCARS Simulations to On-track Test Results On-track tests showed a strong correlation between passenger ride quality and track geometry on the PATH system. Further work needs to be done to identify what track anomalies or structures correspond to the wavelengths identified. The influence of entry/exit spiral (or lack of) will need to be investigated. The roll response due to curves can affect ride quality and will need to be studied in Phase 3. PBTG NN Development for DART and PATH Systems Phase 2 results show that transit systems with a wide range of track geometry deviations and corresponding high vehicle dynamic response are well suited for using NNs to develop PBTG, whereas transit systems with very few track geometry deviations and relatively low vehicle dynamic response are not. NNs were developed for both DART and PATH systems and showed the following: • DART NNs predicted vehicle response to track geometry with confidence intervals of 0.1 percent and 0.25 percent based on point-by-point response and segment-based approach, respectively. • The poor predictive performance on DART is due to what is known as overfitting. The track geometry had small deviations; therefore, there were no high dynamic events in the ride 1

quality data. The data used to train the NNs was seen as “patternless” noise; therefore, the NNs did not recognize a trend in the data. • PATH NNs predicted vehicle response to track geometry with an 81 percent confidence interval using the segment-based approach. The data collected on PATH had significant dynamic events recorded in response to a wide range of track geometry deviations. These results show significant improvement compared to DART NNs, because PATH NNs recognized trends in the data. Evaluation of the Potential Use of PBTG on Transit Systems (Proof of concept) The NUCARS model was used to generate information to build the NNs for PBTG proof of concept. Research from Phase 1 and Phase 2 shows there is potential for PBTG to be used on some transit systems to optimize maintenance and ride quality. Further research is needed to determine the viability of a PBTG system and to establish guidelines for implementation. The following tasks are recommended for Phase 3 research: • Build a complete set of NNs for PATH using the data and NUCARS model of the PA5 Car. • Use PBTG developed for PATH to predict areas in track that cause adverse ride quality. Once these areas are identified, a track inspection should be done to determine if the prediction is accurate and if it is in fact a deviation that can be maintained (slab track versus ballasted track, etc.). • Maintain and correct deviations in the identified locations. Measure both track geometry and ride quality to determine if significant improvement is achieved. • If significant improvement is achieved, modify the PBTG system that was developed for freight railroads to meet the transit agency needs. • Write guidelines for implementation of PBTG for transit systems. 2