Technologies for Generating Road Centerlines — an Applications View

1. Technologies for Generating Road Centerlines — an Applications View Val Noronha University of California, Santa Barbara

2. Background & Credits • NCRST-Infrastructure — 4 universities, federally funded under TEA-21. Mandate broadened to include Homeland Security • UCSB: Mike Goodchild, Rick Church, Qin Zhang, Dar Roberts, Meg Gardner, Chris Funk, Francisco Iovino • ISU: Reg Souleyrette, Shauna Hallmark, Zach Hans, David Veneziano • UW: Raad Saleh

3. Centerlines — Big Picture

4. W H Y C E N T E R L I N E S

6. The Applications View • Asset management — inventory, linear referencing • Homeland security • Other: ITS (current and future), precision snow plowing, market research/LBS, etc.

7. Homeland Security • Anticipate the unanticipated: need all information. Now! • Re-examine old cost/benefit frameworks: multiple-use expenditures, multiple-use data • Can’t afford massive re-tooling … what are the low-cost alternatives?

8. Centerlines in Homeland Security • Consequence analysis and logistics • Does the road come within 2 km of the burning pine growth? • Demographics overlay — how many to evacuate? • What targets/facilities in the vicinity (e.g. nukes, shelters) • Flood/drainage, avalanches

9. Homeland Security • Anticipate the unanticipated: need all information. Now! • Re-examine old cost/benefit frameworks: multiple-use expenditures, multiple-use data • Can’t afford massive re-tooling … what are the low-cost alternatives?

10. Asset Management • DMI troublesome, error prone as field technology. GPS a workable substitute? Depends on quality of centerline, GPS • What decisions ride on asset location? What accuracy is required? • Downtown core vs suburban vs rural • Well traveled vs remote • Type of object: bridge vs stop sign • Value of uncertainty metadata

11. Value of Current Data, Now Compatibility with engineering scale for its own sake is probably not cost-effective • 1:10,000 centerlines delivered this year, updated annually @ $50-100/km vs • 1:500 centerlines in 2010, updated decennially @ $1000/km

12. Multi-use “Economies of Scale” • 1:25,000 … urban centerlines • 1:10,000 … divided roads • 1: 2,000 … lane geometry • 1: 500 … engineering/design    Which multiple-use scenarios are most workable? Common/interchangeable topological scale?

13. Outline • Remote sensing • Photogrammetry/manual softcopy • Automated extraction: hyperspectral • GPS • Fleet probe • Linear accuracy

14. R E M O T E S E N S I N G

15. TIGER USGS 1:24K Design CAD GDT Navtech State, local State, local DOT Mapquest ITS Engineering CVO/Logistics Centerline databases — families RS RS RS Producers Users

16. Early Thinking • RS is not a monolithic solution. Many types of imagery  many approaches • No centerline solution is the “right” answer for all needs; each addresses a niche • scale • rural vs urban • data quality requirements

18. Manual Softcopy

21. Hyperspectral — Lessons Learnt • Largely automated process, may require user intervention in urban areas • Well suited to rural: extensive, little spectral confusion, no prior centerline coverages. Global road database a la GTOPO30? • With hyper tuning, multispectral imagery probably sufficient in rural areas • In practice add in ortho generation cost (Z)

22. T H E G P S C H A L L E N G E

23. The GPS Challenge

24. GPS Results — lane resolution

27. Prospects for GPS probes • Centerline geometry • Linear geometry • Some assets: stop signs, traffic signals, one-ways, turn prohibitions, speed “limit” • ITS industry thinking about 2010 scenarios; many DOT needs can be served in 2002

28. T H E G P S - L I N E A R C H A L L E N G E

29. GPS for Hwy Ops Hi end  Lo end compatibility

30. The Big Myth about Linear vs 2D  • Distance over the hill is greater: need to correct for elevation  Largely human perception. Actual difference = a quarter of 1% (on a 7% grade)

31. What Really Happens on the Hill Inaccurate (x,y) geometry shortchanges length up to 20%

32. What Really Happens on the Hill • Accurate centerline geometry required to relate (x,y) to linear measurement • How accurate? • Is $150 GPS accurate enough? • If not, can it be adjusted?

33. Generalization  GPS length high on straight sections (due to error) ... Curve fitting  and undersampled on curves (due to 1-2 Hz latency)

34. Linear Accuracy Tests — Setting

35. Linear Accuracy Tests — Setting • ~10 km road, rises 800m, average 8% grade • Numerous hairpins • Some dense tree canopy and partial GPS occlusion

36. 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

37. Miscellaneous Additional Results • Curve fitting and generalization add 0.1% to GPS-derived length • Difference between Trimble DGPS and uncorrected GPS from $150 unit: 0.1% • Effect of including z in calculation: 0.5-1.0% (note: z from GPS is more error-prone than x,y)

38. Recap • Homeland security and asset management require centerlines, inventory map base — now • Consumer grade GPS viable at 1:5,000 ~1:10,000, compatible with DMI/LR in ±1% range • RS appropriate in rural areas at 1:25K

39. What I’m not saying Linear referencing is dead, rendered obsolete by GPS GPS works better for everything everywhere (x,y) alone is sufficient Tools now available What I am saying Life without (x,y) is information-poor and untenable in long term DMI as field device is troublesome; GPS preferable for most location recording Need (Route_ID,x,y) at least in short term Points of Clarification

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