1. Data Collection for HDM Christopher R. Bennett EASTE

2. 2 Presentation Overview Principles of Data Collection Location Referencing Pavement Data Traffic Data Generic Specifications

3. Principles of Data Collection

4. 4 Good Data Critical for HDM Without good data cannot have sensible results If input data are wrong why worry about calibration?

5. 5 Categories of Data Inventory  Physical elements of system  Do not change markedly over time  Typically measured in ‘one off’ exercise and updated Condition  Change over time  Require regular (or irregular) monitoring

6. 6 What to Collect? Foundational question Decision often based on  Wish list (“nice to have”)  Existing or historical data collection processes Can lead to data collection becoming an end in itself Excessive or inefficient data collection could compromise project

7. 7 Cambodia: $600,000 for 11,000 km ($55/km)  Included procurement of equipment, vehicles, surveys Laos: $225,000 for 8,000 km ($28/km)  Surveys and map preparation New Zealand: $10-$15/km  Surveys only Comparative Costs

8. 8 Recommended Approach Collect only the data you need Collect data to the lowest level of detail sufficient to make an appropriate decision Collect data only when they are needed Use pilot studies to test the appropriateness of the approach

9. 9 Network Level Data Calibration Data Performance StructureCondition RideDistressFriction IQL-5 IQL-4 IQL-3 IQL-2 IQL-1 System Performance Monitoring Planning and Performance Evaluation Programme Analysis or Detailed Planning Project Level or Detailed Programme Project Detail or Research HIGH LEVEL DATA LOW LEVEL DATA Data Detail Very Important

10. 10 Survey Frequency Inventory Data  One off exercise  Updated/verified ~5 years Pavement Condition Data  Main roads 1-2 years  Minor roads ~2-5 years Bridge Condition Data  Regular surveys 1-2 years  Intensive surveys ~5 years Traffic Data  Permanent count stations (24/7/365)  Short-term count stations (~ 1 - 7 days)

11. Location Referencing

12. 12 The Most Important Issue Unless properly referenced, data will be of limited use Two elements:  The location  The address used to identify the location Three components:  Identification of a known point (eg km stone)  Direction (ie increasing/decreasing)  Distance measurement (ie displacement/ offset)

13. 13 One Location - Many Addresses

14. 14 Linear Referencing Most common Different methods  Kilometre point (e.g., 9.29)  Kilometre post (e.g., 9.29 with equations)  Reference point (e.g., xx + 0.29)  Reference post (e.g., xx + 0.29)

15. 15 Referencing a Network

16. 16 Examples of Referencing (NZ) Large Roundabout Divided Carriageway

17. 17 Examples of Referencing (NZ) Ramp

18. 18 Example of Referencing a Highway

19. 19 Referencing Business Process

20. 20 Spatial Referencing Latitude/Longitude Usually measured with GPS  Accuracy typically 95% +/- 10 m Improved through differential correction or post-processing  Survey issues will typically give accuracy +/- 1 m Recorded in WGS84 datum and so usually needs to be converted to local co-ordinate system

21. 21 GPS Validation

22. 22 Examples of GPS Data Processing Topological Corrections Correcting for incomplete survey Harmonizing different survey data

23. 23 Cambodia - Validation of Survey Lengths

24. 24 Manual Validation of Centreline

25. 25 GPS Data Projection - NZ Aerial/Satellite photos and ground survey data can have different projections Wrong Projections Correct Projections

26. 26 GPS Data Projection - Samoa

27. Pavement Data Collection - General

28. 28 Pavement Data Framework

29. 29 Measurement Equipment Types

30. 30 Multi-function Systems Measure multiple attributes in a single pass Most cost effective and reduces location referencing issues Two groups:  Portable systems: installed in any vehicle  Dedicated systems: custom instrumented vehicle Portable usually cheaper and more sustainable but sophisticated measurements require dedicated vehicle

31. 31 Typical Survey Progress - Cambodia

32. 32 Data Processing Surveys result in a large amount of data Unless processing is done during survey significant delays  Contracts require data submission within 30 days of survey Quality assurance is challenging but essential  Philippines: some data returned 4 times to contractor for correction ALL projects have underestimated difficulties in data processing

33. 33 Location Referencing Digital DMI (< $1 k) GPS (< $1 – 10 k) GPS with Inertial System (< $2 - 15 k)

34. 34 Geometry Combine GPS and precision gyroscopes/ inclinometers (> $50k) Precise 3-D measurements including cross-fall

35. 35 Video Logging

36. Roughness

37. 37 Roughness ‘Bumpiness’ of road Usually related to serviceability but also reflects structural deterioration Affects VOC, safety, comfort, speed Most commonly expressed as IRI IRI simulates response of ‘Quarter-car’ to road profile

38. 38 IRI Scale

39. 39 Types of Equipment

40. 40 Roughness Measurements Class I Class III

41. 41 Variability Between Class I Instruments 2.5 IRI (m/km) 3.5

42. 42 Comparison of Footprints

43. 43 Comparison of Toyota Landcruiser Roughness Calibrations Vehicle 1 Vehicle 2

44. Structural Capacity

45. 45 Structural Capacity Destructive techniques  Coring  DCP Non-destructive techniques  Deflection measurements

46. 46 Deflectometers Trailer FWD Vehicle FWDPortable

47. 47 Benkelman Beam

48. 48 Ground Penetrating Radar

49. Surface Distresses

50. 50 Surface Distresses Performed manually or with automated equipment Includes:  Cracking  Surface Defects  Deformations Great variation in measures used between countries

51. 51 Distress Measurements

52. 52 Video Distress Analysis

53. 53 Current Situation – Video Distress A number of successful commercial systems Some degree of human intervention required Systems usually expensive (> $200 k) and require dedicated vehicles with supplemental lighting Technology ‘evolving’

54. 54 Rut Depths Measured using discrete sensors (ultrasonic/laser) or line Data analyzed to simulate rut depth under a straight edge Systematic under- recording with discrete sensors

55. 55 Implications of Sampling With discrete sampling need to have sufficient sensors to capture key profile information Profiles Usually Sample at Discrete Points Rut Depth Error vs Number of Sensors

56. Selecting Equipment

57. 57 Selecting Equipment Used multi-criteria analysis based on survey and literature review Weightings changed based on road type/class

58. 58 Cost/Performance Matrix – All Roads

59. 59 Cost/Performance Matrix - Expressways

60. 60 Cost/Performance Matrix – Urban Roads

61. 61 Cost/Performance Matrix – Rural Roads

62. 62 Cost/Performance Matrix – Unsealed Roads

63. The end …