val noronha university of california, santa barbara transportation centerlines: case studies from...

Val NoronhaUniversity of California, Santa Barbara

Transportation Centerlines: Case

Studies from California and Iowa

RSPA/FDOT 2004-12-02 #2 © University of California. All rights reserved. N C R S T

Structure

U.S. DOT — NASAPartnership

Transportation Legislation “TEA-21”

Environment

Flows Hazards

Infrastructure

4 universities

4 TAPs

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Outline

What is a centerline?• Applications, requirements, status

Survey Methods• Remote sensing: eyeball, analytical• GPS: high-end, low-end• Photogrammetry

Tomorrow’s precision standards

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The Grapevine

Northbound and southbound carriageways of I-5 switch places for 6 km along the San Andreas Fault

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Where is the Gore point?

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Centerline Applications

Application Long Lat

Market research 250m 250m

Emergency reporting 25m 5m

Highway operations 15m 2m

Lane tolls 15m 2m

Snow plowing 15m .2m

ITS 2020 .2m .1m

Who’s paying?

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Centerline databases — families

Pro

duce

rsU

sers

ITS

State, localDOT

USGS1:24K

State, local

Engineering

DesignCAD

Mapquest

TIGER

GDTNavtech

CVO/Logistics

RS RS RS

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Facets of centerline data

Existence: omission/commission Geometric accuracy (2-D) Linear accuracy Attributes

• name and address range, speed limit• lane width, shoulder width …

Topology Data model

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Name Discrepancies

A B C D EE CAMINO CIELO RDCAMINO CIELO no record E CM CIELOSAN MARCOS PASSHIGHWAY 154 SAN MARCOS PASSSAN MARCOS PASSHWY 154 CA-154 blankMOUNTAIN DR MOUNTAIN MOUNTAIN W MOUNTAIN DRE MOUNTAIN DR PARK E MOUNTAIN DR

BELLA VISTA PARK LNBELLA VISTA DR

FOOTHILL RD CATHEDRAL OAKSHWY 192 CATHEDRAL OAKSCATHEDRAL OAKS RDCATHEDRAL OAKS RD FOOTHILL FOOTHILL RD

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Solutions—Geometry

Remote sensing GPS

• high-end dedicated• multiple vehicles?

Photogrammetry Mosaic engineering drawings

R E M O T E

S E N S I N G

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Hyperspectral process 1

MESMA(Dar)

Q-tree Vectorize

Additional steps: clean, conflate

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MESMA(Dar)

Thinning,Filters

Comparison

Additional steps: clean, conflate

Hyperspectral process 2

T H E G P S

C H A L L E N G E

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High end (“SOP”) GPS

ARAN Output: GPS

every 10 m — not topological centerlines

Cost: x 104

(service)Roadware Inc

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ARAN output

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Problems w/ Dedicated GPS

Need to get it right the first time Verification? IMU helps ($$$)

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The Consumer-GPS Challenge

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GPS Results — lane resolution

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The one to beat …

$150 at Staples

Convenience Price

T H E G R E A T

G P S - L I N E A R

COMPATIBILITY

C H A L L E N G E

?

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LX for professionals

DMI: accurate to 1 m

Linear referencing: “US101 — 20.25”

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A 7075C 7472E 7330DMI 7477GPS 7446

6400

6600

6800

7000

7200

7400

7600

A B C E DMI GPS

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A Myth about Linear vs 2D

Distance over the hill is greater: need to correct for elevation

Difference = 0.25% (on a 7% grade)

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What Really Happens on the Hill

Inaccurate (x,y) geometry shortchanges length up to 20%

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Linear Accuracy Tests — Setting

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Linear Accuracy Tests — Setting

~10 km road, rises 800m, average 8% grade

Numerous hairpins Some dense tree canopy

and partial GPS occlusion Remote sensing would

have difficulty with this

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Linear Accuracy Test — Results

$150-GPS length close to DMI• uphill: 1 ~ 1.5%• downhill: 0.5%

Uphill/downhill difference• Simulated: opposing lane geometry: 0.02-0.05%• GPS: 0.2% (higher downhill speed?)• DMI: 1.5% (affected by engine rev?)

Conclusion (provisional, more study needed)• GPS just as accurate (±0.5%), more consistent

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Will GPS replace LR?

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Big Picture

Applications Scale Convert Remote GPS Photo- Enggpaper sensing grammetry plans

Mkt research 1:50K • • • • •EMS 1:20K • • • • •Hwy ops 1:20K • • • • •Lane tolls 1:5K • • •Plowing 1:1K • • •ITS 2010 1:100 • •

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Manual Softcopy

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UNETRANS

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The Future

ITS• lane departure warning• autonomous control and platooning

Sub-decimeter accuracy Millions of sensors

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Tomorrow’s Centerlines

Car navigation system automatically updates local map while you gas up

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Conclusions

No single solution to all requirements

Showing promise• Remote sensing• GPS• Photogrammetry

Attribution, conflation, modeling … and data exchange

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