2. What is NCRST Funded under TEA-21, 1998
US DOT: Research & Special Programs Administration
Philosophy: rapid evolution of research into commercial products, practice
User consultation and outreach
Partnerships: industry, international
CLEM #2

3. The NCRSTs Environment — Mississippi State
Chuck O’Hara
Infrastructure — UC Santa Barbara
Raad Saleh (Wisconsin)
Flows — Ohio State
Charles Toth
Disasters — New Mexico
Demin Xiong (Oak Ridge)
9 Technology Application Partners
Chris Chiesa (Veridian)
CLEM #3

4. NCRST Interest in Centerlines
3 of 4 consortia have centerline extraction projects
2 of 9 TAPs are focused on centerline extraction
CLEM #4

5. UCSB Interest in Centerlines
Tobler, Church, Goodchild, etc
Funded by Caltrans, USDOT-FHWA since early 1990s: towards IVHS/ITS models
Modeling of geometric error
GPS, wireless communication
Map rectification
NCRST: remote sensing
ESRI: essential data model for transportation
CLEM #5

6. Consortium Members Iowa Wisconsin California Florida GPCI NM
Souleyrette
Hallmark
Andrle
California
Goodchild
Church
[Estes]
Roberts
Manjunath
Gerges
Noronha
Wisconsin
Vonderohe
Scarpace
Adams
Florida
Shrestha
Degner
Tuell
GPCI NM
Fletcher
CLEM #6

7. Why CLEM2001? Centerlines widely studied
Diverse approaches, each successful in particular domain
To become common practice:
understand niche of each method
faster, cheaper, more accurate
rural vs urban areas
exposed vs canopy, etc
consolidate techniques
CLEM #7

8. Some Centerline Applications
Precision snow plowing ± 0.2 m
ITS messaging … mayday ± m
Toll by road/lane use ± 2 m
Highway asset management ± 15 m
Elections: right topology
Market research: who uses BrandX toothpaste: ± 500 m
CLEM #8

9. Some Criteria Cost Timeliness Accuracy Scope
Errant counties fail to report
Disaster response
Accuracy
Scope
Neighborhood vs city vs global
CLEM #9

10. Easy Street
New neighborhood
Little or no foliage overhang
Vehicles in garage/driveway

11. Not so easy
Repairs and surface coats
Paint stripes
Shadows
Parked vehicles
Foliage overhangs

12. Conclusion
One solution is not necessarily better than the others across all criteria — each has its niche
CLEM2001 is an opportunity to learn from each other
CLEM #12

13. Agenda Structure — Monday
View from the data producers
Don Cooke, GDT — accuracy
Bob LaMacchia, Census — beyond TIGER
Introductory survey of techniques
Raad Saleh
CLEM #13

14. Agenda Structure — Monday
Image analysis techniques
Ed Granzow, Iguana
Demin Xiong, Oak Ridge
Dar Roberts, UCSB
Chris Funk, UCSB
Chris Chiesa, Veridian
Peter Gipps, Quantm
CLEM #14

15. Agenda Structure — Tuesday
GPS/ITS techniques
Christopher Bennett, Montgomery Watson
Charles Toth, Ohio State
Russ Shields, Ygomi
GPS/Photogrammetry demo
Ted Jones/Gay Hamilton Smith, Florida DOT/HSA Consulting
CLEM #15

16. Agenda Structure — Tuesday
Data modeling
Kai Han, U/Manitoba
Terry Bills, GIS/Trans
Kevin Curtin, UCSB
What have we learned, where next (CLEM200x)?
Mike Goodchild, UCSB
CLEM #16