Train Arrival Times At Highway Railroad Grade Crossing

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Estimating Train Arrival Times at Highway-Railroad Grade Crossing Using Multiple Sensors. By Diego B. Franca, M.Sc., Kittelson and Associates, Inc and Elizabeth “Libby” Jones, PhD …

Estimating Train Arrival Times at Highway-Railroad Grade Crossing Using Multiple Sensors. By Diego B. Franca, M.Sc., Kittelson and Associates, Inc and Elizabeth “Libby” Jones, PhD
Assoc. Professor, Civil Engineering, University of Nebraska - Lincoln
Assoc. Director , Mid-America Transportation Center. Presented at 89th TRB Annual Meeting – Jan 2010.

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  • 1. By: Diego B. Franca, M.Sc. Kittelson and Associates, Inc Elizabeth “Libby” Jones, PhD Assoc. Professor, Civil Engineering, University of Nebraska - Lincoln Assoc. Director , Mid-America Transportation Center Presented at 89th TRB Annual Meeting – Jan 2010 Estimating Train Arrival Times at Highway-Railroad Grade Crossing Using Multiple Sensors
  • 2. Presentation Outline
    • Introduction
    • Problem Statement
    • Literature Review
    • Methodology
    • Data Collection
    • Data Analysis and Results
    • Final Considerations
  • 3. Introduction
    • Highway-Railroad Grade Crossings (HRGCs) Overview
    “The general area where a highway and a railroad’s right of way cross at the same level, within which are included the railroad tracks, highway, and traffic control devices for highway traffic traversing that area” (MUTCD, 2007)
  • 4. Introduction
    • HRGCs Concerns
      • Collisions at HRGCs represent the second largest cause of fatalities in railroads (trespassing is the first).
      • HRGCs near signalized intersections present a safety concern due to the potential queues of highway traffic that can back up across the tracks.
  • 5. Introduction
    • Traffic Signal Preemption at HRGCs
      • The MUTCD requires a 20-second minimum time for the railroad circuit to activate warning devices prior to arrival of a through train.
      • When highway-rail grade crossings are within 200 feet of a signalized intersection, preemption should be considered at that location .
  • 6. Problem Statement
    • Motivation
      • The minimum warning time of 20 seconds to the signalized intersection is not always adequate to safely clear stopped vehicles from the HRGC area.
      • The current traffic signal preemption strategies are viewed as a reactive action to trains approaching a nearby HRGC.
      • Advance notice of train arrival times at HRGCs can improve signal preemption strategies and reduce accidents.
  • 7. Problem Statement
    • General Hypothesis:
      • The hypothesis of this research is that second generation technologies can provide accurate advanced notice of train arrival at an HRGC near a signalized intersection of at least a cycle length prior to the arrival of a train at an HRGC.
  • 8. Problem Statement
    • Research Objective 1:
      • Define how the variability of speed measurements from two 2 nd generation technology sensors affects the train arrival time estimation at an HRGC in a multi-track environment.
    • Research Objective 2:
      • Determine how to best use the multiple sensor speed data to improve estimations of train arrival time at an HRGC.
  • 9. Literature Review
    • Train Detection Technologies:
      • First Generation: AC-DC, Motion-Sensitive, Constant Warning Time.
      • Second Generation: Doppler Radar, Video Detection, Infrared.
      • Third Generation: GPS, Transponders, Positive Train Control.
  • 10. Literature Review
    • NCHRP 271 - Traffic Signal Operations near Highway-Rail Grade Crossings ( Korve, 1999)
    • An Analysis of Low-Cost Active Warning Devices for Highway-Rail Grade Crossings (Roop et.al., 2005)
    • Non-Vital Advance Rail Preemption of Signalized Intersections near Highway-Rail Grade Crossings: Technical Report (Ruback, Balke and Engelbrecht, 2007)
    • Estimating Train Speeds Using Fused Data from Multiple Speed Detectors (Zhou, 2007)
  • 11. Methodology
    • 1 st Objective
      • Collect train speed data using Doppler radar and video image detection.
      • Manual speeds are needed so the performance of both sensors can be compared.
      • Two scenarios considered: multiple trains and single train on the tracks.
  • 12. Methodology
    • 1 st Objective
      • Train arrival time estimation methodology from the collected train speed data.
      • Required steps to estimate train arrival times: train length and train acceleration estimation.
      • Actual train arrival time at the studied HRGC needed to compare the estimations from radar and video.
  • 13. Methodology
    • 2 nd Objective
      • Fuse train speed data from Doppler radar and video image detection.
      • Estimate train arrival times using methodology for the 2 nd objective.
      • Ideally, the fused data should improve the estimates from radar and video detection.
  • 14. Data Collection Site
    • UNL HRGC TEST BED
  • 15. Adams Street HRGC Location
    • Installed equipment helps monitor highway traffic.
    • Vehicles are constantly caught stopping at the tracks.
    N 35th St . Adams St . Data Collection Site N
  • 16. Upstream Salt Creek Location
    • Located 1.85 miles upstream of the HRGC location;
    • City of Lincoln Public Works installation support;
    • Connected to the UNL-ITS Lab via internet.
    Data Collection Site
  • 17. Data Collection
    • 1 st Objective
      • Video and radar speed data collected at Salt Creek location. Single train and multiple-train scenarios considered.
      • Manual speeds measured by computing railcar length and time interval on recorded videos.
  • 18. Data Collection
    • 1 st Objective
      • Actual train arrival times needed to compare estimations from radar and video.
      • Camera time stamps from Salt Creek and HRGC locations used to compute actual train arrival times.
  • 19. Data Collection
    • 2 nd Objective
      • Data collected for 1 st objectives used.
      • Speed data fused using the Kalman filter model and train arrival time estimations obtained from the new speed estimates.
  • 20. Data Analysis & Results
    • 1st Objective
      • Multiple-Train Scenario
    RADAR PAIRED T-TEST T-Statistic P-value 95% Confidence Interval for Mean Difference (sec) 1.58 0.14 (-0.61, 3.68) VIDEO (AUTOSCOPE) PAIRED T-TEST T-Statistic P-value 95% Confidence Interval for Mean Difference (sec) 0.58 0.571 (-2.47, 4.27)
  • 21. Data Analysis & Results
    • 1st Objective
      • Single Train Scenario
    RADAR PAIRED T-TEST T-Statistic P-value 95% Confidence Interval for Mean Difference (sec) 3.15 0.007 (0.98, 5.06) VIDEO (AUTOSCOPE) PAIRED T-TEST T-Statistic P-value 95% Confidence Interval for Mean Difference (sec) 0.09 0.929 (-4.30, 4.69)
  • 22. Data Analysis & Results
    • 2 nd Objective
      • Multiple-Train Scenario
    • Kalman filter presented the narrowest 95% C.I.
    KALMAN FILTER PAIRED T-TEST T-Statistic P-value 95% Confidence Interval for Mean Difference (sec) 1.79 0.099 (-0.32, 3.25)
  • 23. Data Analysis & Results
    • 2 nd Objective
      • Single Train Scenario
    • Radar presented the narrowest 95% C.I., but Kalman filter 95% C.I. includes the zero mean difference.
    KALMAN FILTER PAIRED T-TEST T-Statistic P-value 95% Confidence Interval for Mean Difference (sec) 1.44 0.171 (-0.86, 4.44)
  • 24. Data Analysis & Results
    • Train Arrival Time Estimation Comparison
    MULTIPLE-TRAIN SCENARIO   Count Percentage Difference from Actual Arrival Time Radar Estimation Autoscope Estimation Kalman Filter Estimation Radar Estimation Autoscope Estimation Kalman Filter Estimation Within +/- 2 seconds 6 4 8 46% 31% 62% Within +/- 5 seconds 9 7 12 69% 54% 92% within +/- 10 seconds 13 13 13 100% 100% 100% SINGLE TRAIN SCENARIO   Count Percentage Difference from Actual Arrival Time Radar Estimation Autoscope Estimation Kalman Filter Estimation Radar Estimation Autoscope Estimation Kalman Filter Estimation Within +/- 2 seconds 6 6 7 38% 38% 44% Within +/- 5 seconds 12 7 13 75% 44% 81% within +/- 10 seconds 15 12 15 94% 75% 94%
  • 25. Final Remarks
    • Lessons learned from Doppler radar and video performances will help to develop algorithms to predict train arrival times at HRGCs and improve traffic signal preemption (and safety as well).
    • Data fusion process showed to be reliable and it could improve train arrival time estimations in a real time system application.
    • In the event of failure of either sensor, the Kalman filter could still be used to provide consistent measurements.
  • 26. Credits
    • Federal Railroad Administration (FRA)
    • Nebraska Department of Roads (NDOR)
    • City of Lincoln