1. Safety-Based Deployment Assistance for Location of V2I Applications
Nancy Lefler, UNC Highway Safety Research Center
Based on work by Kim Eccles and Thanh Le, VHB for the Federal Highway Administration
Traffic Records Forum, 2017

2. Overview V2I Program Objectives Study Objectives Overall Approach
Data Sources and Background on Source
Applications
Red Light Violation Warning
Stop Sign Gap Assistance
Curve Speed Warning
Findings
Questions

3. Connected Vehicle Research
USDOT Initiative
Provide Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communications
Safety
Mobility
Environment
Agency Operations
Both V2V and V2I use wireless based communication technology

4. Objectives of V2I (From AASHTO Footprint)
Improving Safety – reducing crashes, injuries, and fatalities
Improving personal mobility and reducing environmental impacts
Improving freight efficiency – mobility and compliance/enforcement
Improving border crossing operations – for passengers and freight
Improving internal agency operations – reducing response times and costs
- Highlighted bullet is the focus of this effort

5. Vision for V2I (From AASHTO Footprint)
The vision for the infrastructure footprint anticipates a mature connected vehicle environment by 2040
Up to 80% (250,000) of traffic signal locations vehicle-to-infrastructure enabled
Up to 25,000 other roadside locations V2I enabled
Accurate, real-time, localized traveler information available on 90% or more of roadways
Next-generation, multimodal, information-driven, active traffic management deployed system-wide
Access the National Connected Vehicle Field Infrastructure Footprint Analysis (May 2014) on AASHTO’s website or through the following link:

6. Study Objective
Develop guidance on how to select locations for deployment of individual V2I applications.
Red Light Violation Warning (RLVW)
Curve Speed Warning (CSW)
Stop Sign Gap Assist (SSGA)
US DOT identified eight applications; these were selected for accelerated development
This is mentioned in all 3 reports however, the other 5 may need to be listed in case someone asks. The three found were
Spot Weather Impact Warning (SWIW)
Reduced Speed / Work Zone Warning (WZW)
Pedestrian in Signalized Crosswalk Warning (Transit – PSCW)
Reduced Speed Zone Warning with Lane Closure (RSZW/LC)

7. Approach
Feel free to swap this one out with another fish joke.

8. Data Sources Highway Safety Information System California Charlotte
Illinois
Maine
Minnesota
North Carolina
Ohio
Washington
Each one of the 3 applications used data that was provided by the HSIS

9. Purpose of the HSIS
To develop safety-related knowledge and to assist other researchers in developing this knowledge
Acquisition and distribution of safety data
Research
HSIS is not JUST a database!
The HSIS can be used to analyze a large number of safety problems. They can range from the more basic "problem identification" issues to identify the size and extent of a safety problem to modeling efforts that attempt to predict future accidents from roadway characteristics and traffic factors.

10. What Does the HSIS Do?
Provides quality data to road safety researchers (often in support of other national research programs, such as NCHRP)
Conducts research to support FHWA’s focus areas
Prepares safety assessments using data from HSIS, FARS, GES, HPMS, and other sources
Supports development and use of data collection and analytical tools for the study of highway safety
The system is designed to provide data to be used in research conducted in the general public interest, which is intended for publication in a scientific journal or other national publication.

11. HSIS Database Roadway Inventory Crash Information Traffic Volumes
Files can be linked for a wide spectrum of safety studies
Occupant Characteristics
Vehicle Characteristics
Other Information

12. HSIS Agencies

13. Pre-Crash Scenarios for Target Crashes
More than just crash type
Crash type and circumstances
Example: Angle crashes resulting from red light violation
Describe that finding these crashes is like fishing for a very specific type of fish. So, it’s not just a bass, it’s a wide-mouth striped bass.

14. V2I Safety Applications
Red Light Violation Warning (RLVW)
Curve Speed Warning (CSW)
Stop Sign Gap Assist (SSGA)
Continuing the fishing analogy, the applications are the lures that we want to use to catch the fish. Each lure is a bit different as it is intended to “catch” different types of fish.

15. Overall Methodology
From the bodies of water (HSIS data), can we develop tools/approaches to help agencies use these lures (i.e., install these applications) to catch the most fish (target crashes)?
What is the potential yearly haul (reduction in crashes and costs) for agency by using these lures?
What are the location characteristics where the most target fish are caught?

16. Red Light Violation Warning (RLVW) Goals
Helps vehicle drivers avoid crashes due to violation of a traffic signal
Effective at preventing signal violations
Acceptable to users
Deployable thru-out US
Warning is based on the approach speeds and distance to the signalized intersection. Uses a combination of broadcasting signal phase and timing, geometric intersection descriptions and GPS location correction information
The driver is expected to heed the warning and stop the vehicle to mitigate the risk of a crash.
Source: FHWA-Accelerated Vehicle-to-Infrastructure (V2I) Safety Applications

17. RLVW
Target crash for application: Red light running crashes at signalized intersections
California, Charlotte, and Minnesota Data
CA and MN were selected for detailed intersection analysis (provided in HSIS). Charlotte DOT provided intersection, roadway, crash data files with GIS shape files.
California – 10 most recent years of data (2002 – 2011)
Minnesota - 3 most recent years (2010 – 2012)
Charlotte – 3 most recent years (2011 – 2013)

18. RLVW Charlotte example 150’ buffer in GIS
713 Signalized intersections (3 and 4 legged)
~5,000 crashes per year

19. RLVW Considerations How would you find target crashes? Charlotte
PRIMARY_CAUSE: Disregarded traffic signals (4); Driver's Distraction (35,36,38)
DRTN: Direction of travel (Vehicles from two different directions)
CRASH_TYPE
Narratives for confirmation
The definition of target crashes for Charlotte was also expanded beyond “disregard traffic signals” as the primary cause to include: fail to yield right of way, inattention, driver distracted, driver distracted by electronic communication device, driver distracted by other inside the vehicle, and driver distracted by external distraction.

20. RLVW Considerations How would you find target crashes?
What’s an appropriate timeframe?
We determined 3 year average was best for this application
May be different for others
Six measures were explored. (1,2,3,5 year crash frequency, number of years in top 100 over 5 yr period,, proportion of target crashes to total crashes). 3 yr crash frequency closely replicated the PSI based method for identifying priority intersections. However this could be difficult for an agency without the resource and capability to perform this type of analysis.
Based on the analysis results, the 3 year frequency method holds the most promise providing a reliable method that achieves the desired characteristics

21. RLVW Considerations How would you find target crashes? Land use
What’s an appropriate timeframe?
Would you group intersections?
Land use
Number of lanes
Equipment?
These characteristics provide a lot of variance in the crash experience between the data sets from CA, MN, and CLT. Intersections were characterized into groups of similar intersections based on land use and number of approach lanes.
CA – Avg total number of intersections crashes for each group
MN – Grouped by nimber of approaches (3 or 4 legged), land use and number of lanes.
CLT – Intersection groups by avg number of total lanes. Land use was not used because all intersections in CLT were classified as urban.

22. RLVW Considerations How would you find target crashes?
What’s an appropriate timeframe?
Would you group intersections?
Quantify benefits?
Applied economic costs to crashes
Based on max severity
Assumed vehicle deployment, effectiveness, and infrastructure deployment rates

23. CA Signalized Intersections and RLVW Crashes
The BIG picture of the next three slides is that they are consistent in that if you target 10% of the intersections you can “capture” about 30% of the crashes or so. It’s fairly consistent across the three agencies. The more important point is the three slides that follow these three and show that if you consider the economic cost of those crashes, by targeting 10% of the crashes you get about 60% of the crash costs! This is because we are targeting some of the most severe crashes.
Relationship between cumulative number of signalized intersections and cumulative target crashes in California.
Graph presents the cumulative distribution of average annual total target crashes (total target crashes over three years divided by three) compared to the number of signalized intersections that experienced at least one target crash. 10 percent of these intersections are responsible for nearly 30 percent of the total target crashes in California and Minnesota. The number is a little higher, at about 32 percent in Charlotte. The percent is remarkably consistent for all three datasets.

24. MN Signalized Intersections and RLVW Crashes
Relationship between cumulative number of signalized intersections and cumulative target crashes in Minnesota.
Graph presents the cumulative distribution of average annual total target crashes (total target crashes over three years divided by three) compared to the number of signalized intersections that experienced at least one target crash. 10 percent of these intersections are responsible for nearly 30 percent of the total target crashes in California and Minnesota. The number is a little higher, at about 32 percent in Charlotte. The percent is remarkably consistent for all three datasets.

25. CLT Signalized Intersections and RLVW Crashes
Relationship between cumulative number of signalized intersections and cumulative target crashes in Charlotte.
Graph presents the cumulative distribution of average annual total target crashes (total target crashes over three years divided by three) compared to the number of signalized intersections that experienced at least one target crash. 10 percent of these intersections are responsible for nearly 30 percent of the total target crashes in California and Minnesota. The number is a little higher, at about 32 percent in Charlotte. The percent is remarkably consistent for all three datasets.

26. CA Signalized Intersections and RLVW Crash Costs
Again, these next three slides show that by targeting just 10% of the intersections, you can “catch” the most severe crashes (represented here as crash costs)
Relationship between cumulative number of intersections and cumulative target crash cost in California.
Figure 8 presents the cumulative distribution of annualized target crash costs compared to the number of signalized intersections in California that experienced at least one target crash in the last three years. The impact of deploying at the top 10 percent of intersections is more poignantly expressed once severity is included. The top 10 percent of intersections represents over 60 percent of total target crash cost

27. MN Signalized Intersections and RLVW Crash Costs
Relationship between cumulative number of intersections and cumulative target crash cost in Minnesota.
The top 10 percent of intersections represent nearly 70 percent

28. CLT Signalized Intersections and RLVW Crash Costs
Relationship between cumulative number of intersections and cumulative target crash cost in Charlotte.
The top 10% represent slightly over 50 percent of total target crash cost in Charlotte

29. Curve Speed Warning (CSW) Goals
“Improve roadway curve safety and reduce ROR and rollover events by alerting drivers that their speed exceeds a safe threshold for current curve and roadway conditions and may cause loss of vehicle stability and/or control in the curve.”
Source: FHWA-Accelerated Vehicle-to-Infrastructure (V2I) Safety Applications

30. CSW
Potential to catch lots of different fish on curves, single and multiple
Washington and Ohio data
What variables would you use to target crashes?
You can pause at this point and ask agencies about what types of variables they might use in their database to capture these crashes.
WA – 10 most recent years of data
OH – 3 most recent years

31. CSW ELEMENT DESCRIPTION (WA) ELEMENT NAME
HORIZONTAL CURVE CEN ANGLE
CURV_ANG
HORIZONTAL CURVE MAX SUPER
CURV_MAX
STATE RTE TYPE ID
CURV_INV
LEGAL SPEED LIMIT
LEGAL_SP
HORIZONTAL CURVE CONTRACT NUMBER
CURV_NUM
HORIZONTAL CURVE DIRECTION
DIR_CURV
HORIZONTAL CURVE RADIUS
CURV_RAD
HORIZONTAL CURVE BEGIN MLPOST
BEGMP
HORIZONTAL CURVE END MLPOST
ENDMP
DEGREE OF CURVATURE
DEG_CURV
HORIZONTAL CURVE LGT (MI)
SEG_LNG
CURVE OVERLAP INDICATOR
OVERLAP
ROUTE NUMBER
RTE_NBR
Washington Curve File The slides are intended to show that there is some robust curve inventory in WA that was used for this analysis. I would jump through these two slides quickly.

32. CSW ELEMENT DESCRIPTION (OH) ELEMENT NAME Ohio Curve File
DIRECTION OF CURVE
DIR_CURV
COUNTY
STATE ROUTE SUFFIX
RTE_SUFX
DESCRIPTION
DESC
DIVIDED HIGHWAY INDICATOR
DIVIDED
NUMBER OF LANES
NO_LANES
FUNCTIONAL CLASS
FUNC_CLS
BEGIN LOG POINT OF CURVE
BEGMP
SEGMENT LENGTH
SEG_LNG
END LOG POINT OF CURVE
ENDMP
STATE ROUTE NUMBER
RTE_NBR
COUNTY ROUTE
CNTY_RTE
DEGREE OF CURVE
DEG_CURV
Ohio Curve File

33. Run-Off-Road CSW EVENT1 = 9 (Run-Off-Road) TRF_CNTL1 = 9
CRASH TYPES – WASHINGTON
Run-Off-Road
EVENT1 = 9 (Run-Off-Road)
TRF_CNTL1 = 9
LOC_TYPE = 4,5,8
Example crash type using event code; traffic control type, and location

34. Rollover CSW Opposite Direction (M) CRASH TYPES – WASHINGTON
EVENT1 or 2 = 11 (Overturn)
TRF_CNTL1 = 9
LOC-TYPE = 4,5,8
Opposite Direction (M)
NUMVEHS > 1
ACCTYPE = 1-5,7,11-15, 17
COLTYPE1 = 24-30
TRF_CNTL1 = 9
LOC-TYPE = 4,5,8

35. Run-off-road CSW CRASH TYPES – OHIO LOC_TYPE=0,7 FRWY_IND=”N”
MISCACT=1,3,4,8,9,11,14
Event1,2=8, 9

36. Rollover CSW CRASH TYPES – OHIO Opposite Direction (M) LOC_TYPE=0,7
FRWY_IND=”N”
MISCACT=1,3,4,8,9,11,14
Event1,2=1
Opposite Direction (M)
LOC_TYPE=0,7
FRWY_IND=”N”
MISCACT=1,3,4,8,9,11,14
numvehs>1
event1,2=20
veh_n_from=Opposite direction

37. CSW Multi-Curve Segment
If two or more curves were closely located and their 250ft extended portions overlapped, these curves were combined to form a multi-curve segment
<500’

38. CSW
250’
250’
Target crashes are run off road, roll-over, and multi i vehicle opposite directions
250’
Include crashes located up to 250ft beyond both ends of each curve segment

39. CSW Considerations WA and OH How would you find target crashes?
Categorized as rollover, run-off-road, or multi-vehicle opposite direction
EVENT1, EVENT2,…: Sequence of events
LOC_TYPE: Not intersection-related
Those tree crash types where one of the involved vehicles lost control due to traveling a curve faster than a safe speed for the condition. The vehicle departs its travelled lane and then runs off road or rolls over or collides with another vehicle travelling in the opposing direction.

40. CSW Considerations How would you find target crashes?
What’s an appropriate timeframe?
Again, we determined 3 year average was best for this application
May be different for others
3 years was closest to the PSI (Spearmans rank correlation coefficient).

41. CSW Considerations How would you find target crashes?
What’s an appropriate timeframe?
Would you group curves?
Segment configuration (single or multi-curve)
Area type (rural vs. urban)
Terrain (level, rolling, and mountain)
Washington had 174 curve segments that experienced 3 or more target crashes in a 3 year period. These candidate segments were compared to those that experienced two or less target crashes in the same three-year period. There were 524 candidate curve segments in Ohio that experienced three or more crashes in a three-year period and similar comparisons were also made for Ohio data.

42. CSW Considerations How would you find target crashes?
What’s an appropriate timeframe?
Would you group intersections?
Quantify benefits?
Applied economic costs to crashes
Based on max severity
Assumed vehicle deployment, effectiveness, and infrastructure deployment rates

43. WA Curves and CSW Crashes
Graph presents the cumulative distribution of average annual total target crashes (total target crashes over three years divided by three) compared to the number of candidate curve segments that experienced at least one target crash in Washington, respectively. As shown on the graphs, 10 percent of these curve segments are responsible for nearly 30 percent of the total target crashes in Washington. The percent is consistent for the two datasets.

44. OH Curves and CSW Crashes
Graph presents the cumulative distribution of average annual total target crashes (total target crashes over three years divided by three) compared to the number of candidate curve segments that experienced at least one target crash in Ohio, respectively. As shown on the graphs, 10 percent of these curve segments are responsible for nearly 30 percent of the total target crashes in Ohio. The percent is consistent for the two datasets.

45. WA Curves and CSW Crash Costs
Graph resents the cumulative distribution of annualized target crash costs compared to the number of candidate curve segments in Washington that experienced at least one target crash in the last three years. The impact of deploying at the top 10 percent of curve segments is more poignantly expressed once severity is included. The top 10 percent of curve segments represent over 80 percent of total target crash cost.

46. OH Curves and CSW Crash Costs
Graph resents the cumulative distribution of annualized target crash costs compared to the number of candidate curve segments in OH that experienced at least one target crash in the last three years. The impact of deploying at the top 10 percent of curve segments is more poignantly expressed once severity is included. The top 10 percent of curve segments represent over 70 percent of total target crash cost.

47. Stop Sign Gap Assist (SSGA) Goals
“..Improve safety at stop sign controlled intersections by providing a cooperative decision support system to help drivers safely negotiate such intersections. The system will support the minor-road driver in identifying unsafe gaps in major road traffic at the intersection, assisting with either crossing or entering the major road traffic from a minor road.”
Concept uses infrastructure-based instrumentation on the major road to measure gaps in the major road traffic. Such a system can transmit this information to the stopped minor road vehicle to advise the driver of the length of the gap to assist in appropriate maneuver decisions.
Source: FHWA-Accelerated Vehicle-to-Infrastructure (V2I) Safety Applications

48. SSGA California and Minnesota data
Targeting poor gap acceptance crashes from minor
Use violation variable to exclude stop sign violations
CA – 10 yrs of data (2002 – 2011) for trend analysis / primary was
MN – (2010 – 2012)

49. SSGA Considerations California How would you find target crashes?
INT_RAMP: At intersection (<250’)
DIR_TRVL: Direction of travel (Vehicles from perpendicular directions)
MISCACT1,2: Vehicle movement preceding crash
In the California data files, several variables were used to identify target crashes at the candidate intersections. The variable INTS/Ramp ACC Location (INT_RAMP) was used to identify those crashes coded as occurred at or outside an intersection. These crashes were limited to those coded as occurring within 250 feet of the intersection.

50. SSGA Considerations How would you find target crashes?
What’s an appropriate timeframe?
We determined 3 year average was best for this application
May be different for others
Three year was closer to the Spearman’s Rank Correlation coefficient

51. SSGA Considerations How would you find target crashes?
What’s an appropriate timeframe?
Would you group intersections?
Number of approaches
Area type (urban or rural)
Number of approach lanes

52. SSGA Considerations How would you find target crashes?
What’s an appropriate timeframe?
Would you group intersections?
Quantify benefits?
Applied economic costs to crashes
Based on max severity
Assumed vehicle deployment, effectiveness, and infrastructure deployment rates

53. CA Stop-Controlled Intersections and SSGA Crashes
Graph presents the cumulative distribution of average annual total target crashes (total target crashes over three years divided by three) compared to the number of candidate intersections that experienced at least one target crash in California. As shown on the graphs, 10 percent of these intersections are responsible for almost 30% of the total crashes. The percent is consistent for the two datasets

54. MN Stop-Controlled Intersections and SSGA Crashes
Graph presents the cumulative distribution of average annual total target crashes (total target crashes over three years divided by three) compared to the number of candidate intersections that experienced at least one target crash in MN. As shown on the graphs, 10 percent of these intersections are responsible for nearly 26% of the total crashes. The percent is consistent for the two datasets

55. CA Stop-Controlled Intersections and SSGA Crash Cost

56. MN Stop-Controlled Intersections and SSGA Crash Cost

57. Next Steps
Publish reports

58. Kim Eccles, VHB keccles@vhb. com Carol Tan, PhD, FHWA carol. tan@dot
Kim Eccles, VHB Carol Tan, PhD, FHWA Carl Andersen, FHWA