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 with space based data

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 for vehicle identification

Satellite Imagery forVehicle Identification

High Resolution Required

Cars 1m - 2m panchromatic

Trucks 4m panchromatic


Traffic estimation with space based data

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 vkt estimation from high resolution satellite imagery

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

National, Regional Network Coverage AADT and VKT


A verage a nnual d aily t raffic v ehicle k ilometers t raveled

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

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

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 cont1

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]


Accuracy sampling estimation methodology cost large labor and equipment expenses

AccuracySampling, Estimation MethodologyCostLarge Labor and Equipment Expenses


Satellite imagery

Satellite Imagery

Potential

Added Data

Off-the-Road

Spatial Perspective

Access of Remote Areas

Difficulty

Unfamiliar (Density Based)

Potential Error (“Short Interval” Observation)


Traffic estimation with space based data

Original

Image

Binary

Image


Traffic estimation with space based data

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


Traffic estimation with space based data

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 aadt true aadt true aadt true aadt ground based

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


Relative errors re

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 re1

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

RE Decreases with Increased Simulated Time Interval


Network level analysis

NETWORK LEVEL ANALYSIS


Computer simulation

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

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

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

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


Variability error random terms

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


Traffic estimation with space based data

Impact of SatelliteSupply

—Equivalent Satellite Coverage (ESC)


Extensions

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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