Presentation is loading. Please wait.

Presentation is loading. Please wait.

Val Noronha University of California, Santa Barbara Transportation Centerlines: Case Studies from California and Iowa.

Similar presentations


Presentation on theme: "Val Noronha University of California, Santa Barbara Transportation Centerlines: Case Studies from California and Iowa."— Presentation transcript:

1 Val Noronha University of California, Santa Barbara Transportation Centerlines: Case Studies from California and Iowa

2 N C R S T RSPA/FDOT #2 © University of California. All rights reserved. Structure U.S. DOT — NASA Partnership Transportation Legislation “TEA-21” EnvironmentFlowsHazardsInfrastructure  4 universities 4 TAPs

3 N C R S T RSPA/FDOT #3 © University of California. All rights reserved. Outline  What is a centerline? Applications, requirements, status  Survey Methods Remote sensing: eyeball, analytical GPS: high-end, low-end Photogrammetry  Tomorrow’s precision standards

4 N C R S T RSPA/FDOT #4 © University of California. All rights reserved.

5 N C R S T RSPA/FDOT #5 © University of California. All rights reserved.

6 N C R S T RSPA/FDOT #6 © University of California. All rights reserved. The Grapevine Northbound and southbound carriageways of I-5 switch places for 6 km along the San Andreas Fault

7 N C R S T RSPA/FDOT #7 © University of California. All rights reserved.

8 N C R S T RSPA/FDOT #8 © University of California. All rights reserved. Where is the Gore point?

9 N C R S T RSPA/FDOT #9 © University of California. All rights reserved. Centerline Applications Who’s paying?

10

11

12 N C R S T RSPA/FDOT #12 © University of California. All rights reserved. Centerline databases — families Producers Users ITS State, local DOT USGS 1:24K State, local Engineering Design CAD Mapquest TIGER GDT Navtech CVO/Logistics RS

13 N C R S T RSPA/FDOT #13 © University of California. All rights reserved. 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

14 N C R S T RSPA/FDOT #14 © University of California. All rights reserved.

15 N C R S T RSPA/FDOT #15 © University of California. All rights reserved.

16 N C R S T RSPA/FDOT #16 © University of California. All rights reserved.

17 N C R S T RSPA/FDOT #17 © University of California. All rights reserved.

18 N C R S T RSPA/FDOT #18 © University of California. All rights reserved.

19 N C R S T RSPA/FDOT #19 © University of California. All rights reserved.

20 N C R S T RSPA/FDOT #20 © University of California. All rights reserved.

21 N C R S T RSPA/FDOT #21 © University of California. All rights reserved.

22 N C R S T RSPA/FDOT #22 © University of California. All rights reserved.

23 N C R S T RSPA/FDOT #23 © University of California. All rights reserved.

24 N C R S T RSPA/FDOT #24 © University of California. All rights reserved.

25 N C R S T RSPA/FDOT #25 © University of California. All rights reserved.

26 N C R S T RSPA/FDOT #26 © University of California. All rights reserved.

27 N C R S T RSPA/FDOT #27 © University of California. All rights reserved. Name Discrepancies

28 N C R S T RSPA/FDOT #28 © University of California. All rights reserved. Solutions—Geometry  Remote sensing  GPS high-end dedicated multiple vehicles?  Photogrammetry  Mosaic engineering drawings

29 R E M O T E S E N S I N G

30

31 N C R S T RSPA/FDOT #31 © University of California. All rights reserved. Hyperspectral process 1 MESMA (Dar) Q-treeVectorize Additional steps: clean, conflate

32 N C R S T RSPA/FDOT #32 © University of California. All rights reserved. MESMA (Dar) Thinning, Filters Comparison Additional steps: clean, conflate Hyperspectral process 2

33 T H E G P S C H A L L E N G E

34 N C R S T RSPA/FDOT #34 © University of California. All rights reserved. High end (“SOP”) GPS  ARAN  Output: GPS every 10 m — not topological centerlines  Cost: x 10 4 (service) Roadware Inc

35 N C R S T RSPA/FDOT #35 © University of California. All rights reserved.

36 N C R S T RSPA/FDOT #36 © University of California. All rights reserved. ARAN output

37 N C R S T RSPA/FDOT #37 © University of California. All rights reserved. Problems w/ Dedicated GPS  Need to get it right the first time  Verification?  IMU helps ($$$)

38 N C R S T RSPA/FDOT #38 © University of California. All rights reserved. The Consumer-GPS Challenge

39 N C R S T RSPA/FDOT #39 © University of California. All rights reserved. GPS Results — lane resolution

40 N C R S T RSPA/FDOT #40 © University of California. All rights reserved. The one to beat … $150 at Staples  Convenience  Price

41 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 ?

42 N C R S T RSPA/FDOT #42 © University of California. All rights reserved. LX for professionals  DMI: accurate to 1 m  Linear referencing: “US101 — 20.25”

43 N C R S T RSPA/FDOT #43 © University of California. All rights reserved. A 7075 C 7472 E 7330 DMI 7477 GPS 7446

44 N C R S T RSPA/FDOT #44 © University of California. All rights reserved. A Myth about Linear vs 2D  Distance over the hill is greater: need to correct for elevation Difference = 0.25% (on a 7% grade)  

45 N C R S T RSPA/FDOT #45 © University of California. All rights reserved. What Really Happens on the Hill Inaccurate (x,y) geometry shortchanges length up to 20%

46 N C R S T RSPA/FDOT #46 © University of California. All rights reserved. Linear Accuracy Tests — Setting

47 N C R S T RSPA/FDOT #47 © University of California. All rights reserved. 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

48 N C R S T RSPA/FDOT #48 © University of California. All rights reserved. Linear Accuracy Test — Results  $150-GPS length close to DMI uphill: 1 ~ 1.5% downhill: 0.5%  Uphill/downhill difference Simulated: opposing lane geometry: % 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

49 N C R S T RSPA/FDOT #49 © University of California. All rights reserved.

50 N C R S T RSPA/FDOT #50 © University of California. All rights reserved. Will GPS replace LR?

51 N C R S T RSPA/FDOT #51 © University of California. All rights reserved. Big Picture

52 N C R S T RSPA/FDOT #52 © University of California. All rights reserved. Manual Softcopy

53 N C R S T RSPA/FDOT #53 © University of California. All rights reserved. UNETRANS

54

55 N C R S T RSPA/FDOT #55 © University of California. All rights reserved. The Future  ITS lane departure warning autonomous control and platooning  Sub-decimeter accuracy  Millions of sensors

56 N C R S T RSPA/FDOT #56 © University of California. All rights reserved. Tomorrow’s Centerlines Car navigation system automatically updates local map while you gas up

57 N C R S T RSPA/FDOT #57 © University of California. All rights reserved. Conclusions  No single solution to all requirements  Showing promise Remote sensing GPS Photogrammetry  Attribution, conflation, modeling … and data exchange

58

59 1


Download ppt "Val Noronha University of California, Santa Barbara Transportation Centerlines: Case Studies from California and Iowa."

Similar presentations


Ads by Google