Traffic estimation with space based data
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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

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Traffic Estimation with Space-Based Data

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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


Satellite Imagery forVehicle Identification

High Resolution Required

Cars 1m - 2m panchromatic

Trucks 4m panchromatic


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)


Improved AADT and VKTEstimation from High-Resolution Satellite Imagery

Acknowledgments

P. Goel, Z. Jiang, B. Coifman,

Y. Yang,C. Merry, Past Students


National, Regional Network Coverage AADT and VKT


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


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


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


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]


AccuracySampling, Estimation MethodologyCostLarge Labor and Equipment Expenses


Satellite Imagery

Potential

Added Data

Off-the-Road

Spatial Perspective

Access of Remote Areas

Difficulty

Unfamiliar (Density Based)

Potential Error (“Short Interval” Observation)


Original

Image

Binary

Image


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


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)


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


Relative Errors, RE

N = 18

N(RE > 0) = 12

N(RE < 0) = 6

Sample Mean = 0.03

Sample St. Dev. = 0.15

RELATIVELY UNBIASED


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


RE Decreases with Increased Simulated Time Interval


NETWORK LEVEL ANALYSIS


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


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)


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


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


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


Impact of SatelliteSupply

—Equivalent Satellite Coverage (ESC)


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


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