1 / 32

Traffic Estimation with Space-Based Data

Traffic Estimation with Space-Based Data. Mark R. McCord NCRST-F The Ohio State University Workshop on Satellite Based Traffic Measurement Berlin, Germany 9-10 September 2002. Satellite Imagery for Vehicle Identification. High Resolution Required Cars 1m - 2m panchromatic

phuong
Télécharger la présentation

Traffic Estimation with Space-Based Data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Traffic Estimation withSpace-Based Data Mark R. McCord NCRST-F The Ohio State University Workshop on Satellite Based Traffic Measurement Berlin, Germany 9-10 September 2002

  2. Satellite Imagery forVehicle Identification High Resolution Required Cars 1m - 2m panchromatic Trucks 4m panchromatic

  3. High Resolution => Low orbits => Limited temporal sampling (dynamic traffic) => Long time scale, geographically extensive applications => Traffic Monitoring Average Annual Daily Traffic (AADT) Vehicle Kilometers Traveled (VKT)

  4. Improved AADT and VKTEstimation from High-Resolution Satellite Imagery Acknowledgments P. Goel, Z. Jiang, B. Coifman, Y. Yang,C. Merry, Past Students

  5. National, Regional Network Coverage AADT and VKT

  6. Average Annual Daily TrafficVehicle Kilometers Traveled AADT: Traffic on a highway segment AADTsΣ=1,365 V24s,  / 365 V24s,   24-hour volume, segment s, day  VKT: Travel over the network (avg daily) VKT = Σs=1,S Lengths * AADTs

  7. Estimating AADT on System (Permanent) Automatic Traffic Recorders V24s, ,  = 1, 2, …, 365, s  Spatr ~3% segments equipped with PATRs => Calculate AADTs s  Spatr => Estimate temporal variability (“expansion factors”) e.g., EF() = EFMD[m(),d()], m() = 1,2, …, 12 d() = 1, 2, …, 7

  8. Estimating AADT on System (cont.) Moveable ATRs (Coverage Counts) V24s, , V24s, +1,   {1, 2, …, 364},sSmatr ~33% segments per year => Estimate AADTs s  Smatr AADTests = f[V24s, , V24s, +1, EF(), EF(+1)] e.g. AADTests = [V24s, /EF()+V24s,+1/EF(+1)]/2

  9. Estimating AADT on System (cont.) Unsampled Segments in Year, Suns (S=Spatr  Smatr  Suns) AADTs  Suns = f[AADTs’,s’  SpatrSmatr], s  Suns e.g. AADTs  Suns = Average[AADTs’,s’  SpatrSmatr] AADTs  Suns = f[AADTs sampled in previous year, network growth factors]

  10. AccuracySampling, Estimation MethodologyCostLarge Labor and Equipment Expenses

  11. Satellite Imagery Potential Added Data Off-the-Road Spatial Perspective Access of Remote Areas Difficulty Unfamiliar (Density Based) Potential Error (“Short Interval” Observation)

  12. Original Image Binary Image

  13. Flowest(x,t+t) = Density(x+x,t)*Velocity(x+x,t) Flowest(x,t+t) [vph] t short (3-15 minutes) V24,ests, = f[Flowest(x,t+t; s,), EFh(h(t))] e.g., V24,ests,  = 24*Flowest(x,t+t; s,) / EFh(h(t)) EFh: hourly expansion factor

  14. V24,ests,  = f[Flowest(x,t+t; s,), EFh(h(t))] AADTimgs = f[V24,ests, , EFMD[m(),d()] ] EFMD: seasonal factor (month-of-year, day-of-week)

  15. Relative Error(AADT Image-based – AADTTrue) / AADTTrueAADTTrue  AADTGround-based

  16. Relative Errors, RE N = 18 N(RE > 0) = 12 N(RE < 0) = 6 Sample Mean = 0.03 Sample St. Dev. = 0.15 RELATIVELY UNBIASED

  17. Relative Errors, RE Sample St. Dev. (w. mean = 0) = 0.15 Maximum RE = 0.34 Lower RE with better AADTGr-based Equiv. Count Interval: 0.6 – 12.6 mins SURPRISING, PROMISING PERFORMANCE

  18. RE Decreases with Increased Simulated Time Interval

  19. NETWORK LEVEL ANALYSIS

  20. Computer Simulation Inputs • Traffic Patterns • AADT distribution, Link Lengths, EFM, EFD - Ground-Based Sampling •% Permanent ATR’s (PATR’s) • % Coverage Counts (MATR’s) • Satellite-Based Sampling* • Variability/Error/Random Terms** Outputs - AADT and VKT (VMT) Estimation Error •Ground-Based Data Only • Satellite- and Ground-Based Combination

  21. Satellite-Based Sampling*Physical Relations FCD[lat1,lat2] = 2(1-Fnpgt)*NPIX*RES*NORB *L[lat1,lat2;i, NORB])10-3)/EAR[lat1, lat2] (5) NORB = 8,681,665.8/ (R+H)1.5 [orbits/day] (9) H > 200 km => NORB < 16.3 [orbits/day] (10) H = (FL/WPI)(RES)(103) [km] (12) NORB>8,681,665/((FL/WPI)max(RES(103)+6371)1.5 [orb/day] (14) Vsg = 0.4633(NORB) [km/sec] (17) DBR = 3.706(NORB)(NPIX)(10-3)/(RES*COMP) [Mbits/sec] (18) (NPIX)( NORB) < 269.8(RES)(DBR*COMP)max (20)

  22. Satellite-Based Sampling*Maximal Coverage (P1) Max: Z1=NORB*NPIX*L[lat1,lat2;i,NORB] NORB,NPIX,i s.t. 90 < i < 180 8,681,665.8/((FL/WPI)max RES(103)+6371)1.5 < NORB < 16.3 0 < NPIX < NPIXmax (NPIX)(NORB) < 269.8(RES)(DBR*COMP)max

  23. Satellite-Based Sampling*: Daily Coverage vs. Resolution and Inclination Angle

  24. Variability/Error/Random Terms** • Ground-based sample: (gr) V24(gr)s, = AADTs*EFMM()-1 *EFDD()-1 * exp((gr) - (gr)2/2), (gr) ~ N(0, (gr)) (gr): Daily deviation from deterministic model • Satellite-based sample: (sat) V24(sat)s, = AADTs*EFMM()-1 *EFDD()-1 * exp((sat) - (sat)2/2), (sat) ~ N(0, (sat)) (sat): Error in Expanding Short-Duration Counts and Daily Variability

  25. Impact of SatelliteSupply —Equivalent Satellite Coverage (ESC)

  26. Extensions • More image- vs. ground-based comparisons • Expansion of short-interval flows • Improved hourly factors • Quantification of uncertainty in sub-hour expansion • Bayesian and model-based estimation • Spatial correlations • Satellite and air-based sampling strategies • Other Uses of Volume Data • Statewide truck OD estimation • Screening tool: growth factors, ground-based sample strategies • Implementation strategies • …

More Related