Webconference Agenda

1. Webconference Agenda • Welcome and Introductions • Participation Information • Overview of MMIRE • Summary of Previous Workshops • Variable and Data Collection Discussion

2. Today’s Presenters • Carol Tan, FHWA • Robert Pollack, FHWA • Kelly Hardy, Vanasse Hangen Brustlin • Forrest Council • Daniel Carter, UNC Highway Safety Research Center • Lincoln Cobb, FHWA

3. Participation Information • Phone lines will be muted (*1 to un-mute) • You can submit questions/comments electronically

4. Purpose of Webconference • Present MMIRE Concept • Discuss Feedback Received During Earlier Webconferences • Discuss Variables and Data Collection • Collection of difficult variables • Data collection technologies • Future variables • Open discussion of other issues

5. MMIRE Concept • Standardized Definitions…But Not a Standard • Dictionary of critical roadway data variables that are required to make more effective and efficient safety improvement decisions • Similar to Model Minimum Uniform Crash Criteria (MMUCC), MMIRE will be a guideline, not a requirement

6. MMIRE Development Effort • AASHTO Strategic Highway Safety Plan • Management: improve information and decision support systems • International Scan • White Paper

7. Proposed MMIRE Variables • Documents development process and includes working matrix • 180 data elements • www.mmire.org

8. Structure of MMIRE I. Roadway Segment Descriptors I.a. Segment Location/Linkage Variables I.b. Segment Roadway Classification I.c. Segment Cross Section I.c.1. Surface Descriptors I.c.2. Lane Descriptors I.c.3. Shoulder Descriptors I.c.4. Median Descriptors

9. Structure of MMIRE (cont.) I. Roadway Segment Descriptors (cont.) I.d. Roadside Descriptors I.e. Other Segment Descriptors I.f. Segment Traffic Flow Data I.g. Segment Traffic Operations/Control Data II. Segment Alignment II.a. Horizontal Curve Data II.b. Vertical Grade Data

10. Structure of MMIRE (cont.) III. Road Junctions III.a. At-Grade Intersection/Junctions III.a.1. At-Grade Intersection/Junction General Descriptors III.a.2. At-Grade Intersection/Junction Descriptors (Each Approach) III.b. Interchange and Ramp Descriptors III.b.1. General Interchange Descriptors III.b.2. Interchange Ramp Descriptors

11. What MMIRE Could Help You Do • Use the New Safety Tools • Interactive Highway Safety Design Model (IHSDM) • SafetyAnalyst • Highway Safety Manual • Enhanced Problem Identification • Enhanced Targeting of Specific Treatments

12. Current Effort • Compare Proposed Elements and Attributes to: • Other databases • Variables states are already collecting • Lead State Program to Pilot the Implementation of MMIRE • Refine Elements and Attributes: MMIRE Version 1.0

13. Earlier Workshops • Overview of Polling • Poll Results • How Poll Results Will Be Used • This will of course involve more polls!

14. Previous Workshop Polling • First, you voted on each potential MMIRE variable: • “Which elements are very important to your safety program/decisions?” • “Which elements will be very difficult to collect?” • Note: • That some segment elements were not included since most of you collect them – thus important and not as difficult • That we are discussing collection for allsegments and junctions, storage in a computerized file, and the ability to link to other safety files!

15. Previous Workshop Polling (cont.) • We then used the results to identify two groups • “Important and Difficult” variables (your inputs) • “Difficult (your input) and may also be important” • For the next 3 slides, tell us which variables you already collect systemwide and store electronically so that we can follow up with you.

16. Important and Difficult to Collect Roadway Segment and Alignment Variables: 22. Surface Friction 63. Roadside Clearzone Width 66. Driveway Information 72. Number of Stop-Controlled Intersections in Segment 93. Roadway Segment Lighting 99. 85th Percentile Speed (and/or Mean Speed) 104. Curve Superelevation or Superelevation Adequacy 110. Percent Gradient

17. Important and Difficult to Collect Intersections and Interchanges: 127. Number of Intersection Quadrants With Limited Sight Distance 133. Approach AADT (minor crossing roads) 150. Approach Left Turn Count/Percent 151. Approach Right Turn Count/Percent 167. Interchange Lighting 171. Ramp AADT 173. Feature at Beginning Ramp Terminal

18. Additional Difficult Variables Difficult to Collect and Needed by Tools or Future Programs: 52. Sidewalk Presence 73. Number of Uncontrolled Intersections in Segment 122. Intersection Skew Angle 132. Roundabout – Bicycle Facility Type 148. Crossing Pedestrian Count/Exposure on Approach 174. Ramp Descriptor at Beginning Ramp Terminal

19. Modifications to MMIRE • Results of Polls Will Be Used to Revise MMIRE • In Addition to Polls, Have Received Other Suggestions: • Add or Combine Variables • Add Attributes • Edit Definitions

20. Variables and Data Collection • Today, we will discuss: • Inputs from states who collect the “Difficult and Important” variables • Information from FHWA on new data collection system • Ideas from you on what might help

21. Collecting Difficult Variables • Surface Friction • Curvature • Superelevation • Speed

22. Data Collection Technologies • LIDAR • Ped/Bike Data • FHWA’s Digital Highway Measurement System

23. Data Collection Technologies • Pedestrian/Bicycle Data • Several emerging automated detection technologies for intersections and trails • Video imaging • Infrared • Microwave • Computerized stereovision • Still a developing technology but several cities have employed (Los Angeles, CA; Phoenix, AZ; Portland, OR) Illustration of Microwave Sensor Source: http://www.walkinginfo.org/pedsmart/nookit.htm

24. Data collection on, over, and under the road. Digital Highway Measurement System

25. DHMS - Summary • Instrumented van to collect data on, over, and under the road. • 6 years – first research cycle. • Looking forward • NIST identifying technology gaps, and potential advanced technology solutions • Next DHMS research cycle will focus on key high-reward technology gaps • Tech transfer – Collaboration / partnerships public and private sectors, and academia

26. What Does the DHMS Do? • DHMS is a high-speed 3-D mapping system. • 17 deployed functions • Many additional functions under consideration for 2nd R&D cycle.

27. How Does It Work? Establishing the reference system -- • Vehicle position established accurately and precisely. • DHM measurements taken relative to vehicle. • Measurements transformed to ground reference frame. • Control line established fixed to ground.

28. ) ))))))))))))) ) ))))))))))))))) Ground reference frame Vehicle Position Accurately Established in Ground Reference • Foundation of DHMS accuracy and precision is highly accurate vehicle position. • Initial position established using High-Accuracy National Differential Global Positioning System (HA NDGPS) • Airline inertial navigation unit (INU) maintains accurate position when GPS signals weak.

29. Objects Located Relative To The Vehicle Reference frame fixed to vehicle Sensors establish the positions and orientations of objects of interest with respect to coordinate system fixed to the vehicle.

30. Applications • Roadside Features and Inventory: • Shoulder characteristics • Roadside profile • Locate and identify signs, barriers, luminaire supports, etc. • Vertical clearance

31. Edge Drop-Off Transverse distance (in) Flat Drop-Off Edge of pavement Sloped Drop-Off Elevation (in) Rutted Drop-Off An edge drop-off can consist of a rutted shoulder next to the lane edge, a pot-hole in the gravel or soil, or a flat or sloped shoulder below the lane edge.

32. Pavement Shoulder, Curbs, Sidewalks, Guardrails Elevation (ft) W Beam face Transverse distance (ft) Guardrails are recognized by their vertical faces positioned to the side of the road at ground level (for tapered guardrails) and above at a consistent height. Flat and W faced guardrails are identified along with their position.

33. Vertical Clearance Tunnel scan, three lanes wide, single lane data shown. Position of scanning laser, on top of vehicle. V-Clear Vertical Clearance varies by lane. The data from the scanning laser above the DHM Van is extracted, filtered, and examined for overhead obstructions.

34. Applications • Roadway Geometry: • Horizontal and vertical alignment • Cross slope / super-elevation • Pavement width

35. Horizontal Alignment Horizontal alignment is defined as the geometric information required to describe the horizontal changes in the alignment of the road.

36. Vertical Alignment Vertical alignment is defined as the geometric information required to describe the vertical changes in the alignment of the road. Estimating Vertical Alignment Using Measured Profile

37. Applications • Inventory & Asset Management: • As-built plans • Pavement condition (surface, sub-surface) • Signs • Safety hardware • Utilities • Pavement markings • Shoulder dimensions, profiles

38. Roadside Feature Location & Identification Image processing of stereoscopic digital images fused with pattern recognition applied to scanning laser data allows for location and identification of roadside features, including signs, guardrails, driveways, and intersections.

39. Pavement Markings Double line Single line Reflective signal strength Scanning laser angular position (deg) Pavement markings are located with respect to the vehicle by noting points of high reflective signal strength within a transverse scan by the scanning laser.

40. Lane Width Lane Width (LW) = difference in pavement marking offset distances derived from fusion of pavement markings, vehicle trajectory, and DMI data. Offset from vehicle centerline (in) Lane Width Painted island for left turn lane Project stationing (100 in)

41. Applications • Pavement Condition: • Surface • Roughness • Macrotexture • Joint faults • Sub-surface – Ground penetrating radar creates 3D map

42. Pavement Surface Roughness Lower IRI indicates more recent pavement

43. Step-Frequency Ground Penetrating Radar Data collection advantages Synchronized antenna array Operational flexibility Data processing advantages 3D sub-surface imaging Robust data processing Wide bandwidth covered Regulatory advantages Programmable frequency spectrum notching. Stable output characteristics Sub-surface feature detection, measurement, and imaging. Output is a series of longitudinal sections (profiles 1-3, below).

44. Elevation Plot of GPR Output (Longitudinal Section, At Constant Offset From Control Line) Plan views of GPR results can be generated by combining elevation plots and cross sections.

45. 3-D Laser & Optical Scanning State of the industry • Astonishing pace of development. • Most features now off-the-shelf. • Integration of features incomplete. Extensive post-processing needed. • Holy Grail = Automatic object extraction from laser point cloud or optical images.

46. 3-D Laser & Optical Scanning Automatic object extraction • Being done, but . . . . • Requires object libraries. • Limited sign libraries exist, but no other highway features. • 40% to 70% accuracy, over short ranges (~40m), ideal conditions – e.g. all signs square to road, no branches obscuring corner of sign. • Very high performance – 3-5 years out.

47. 3-D Laser & Optical Scanning Next prize -- Not just what the object is, and where it is, but how well can it still perform.

48. 3-D Laser & Optical Scanning Sources – • ASTM E-57: Committee on 3D Imaging Systems • http://www.astm.org/COMMIT/SUBCOMMIT/E57.htm • Spar Point Research LLC • Unofficial industry association/clearing house. • Deliver a major conference each year. • http://www.sparllc.com/

49. DHMS: For More Information Contact: Lincoln Cobb (Lincoln.Cobb@dot.gov) Carol Tan (Carol.Tan@dot.gov)

50. Future Variables • If technology were not an issue, what variables would you like to collect? • Examples: • Pedestrian/bike counts • Retroreflectivity • Barrier example