1. Crash Mitigation At Rural Unsignalized Intersections Providing Intersection Decision Support (IDS) for the Driver Inter-Regional Corridors: Hi-speed, hi-density roads crossing Low-speed, low-density roads

2. Agenda Attendees: Please send name, affiliation, phone and email address to: Rick Odgers: odgers@me.umn.edu u 1. Introductions (30 minutes) u 2. Background (20 minutes) u 3. MN Intersection Crash Data Analysis (30 minutes) u 4. Minnesota IDS Project status (20 minutes) u 5. Human Factors (20 minutes) u 6. Plan for the state pooled funded project (20 minutes) u 7. Feedback on plan and participation in deploying IDS in respective state (20 minutes) u 8. Sign-up for DII review panel (5 minutes) u 9. Plans for next meeting (10 minutes), week of April 20 u 10. Wrap-up (5 minutes)

3. National Motivation u 1.72 million annual intersection related crashes involved crossing or turning scenarios (1998 GES data) u Represents 27.3% of all 6.33 million police reported crashes v 41.6% occurred at signalized intersections v 58.4% occurred at unsignalized intersections (stop sign, no controls, other sign). v Crossing path crashes at uncontrolled intersections had the highest fatality rates u 8,474 of 37,409 (22.6%) of fatal crashes were intersection related ….. Traffic Safety Facts 2000

4. Intersection crashes u NHTSA [Carter, 1999] has indicated that 85% of intersection crashes were due to driver error, with the following breakdown: v 27% due to driver inattention v 44% due to faulty perception, and v 14% due to impaired vision.

5. Intersection Decision Support (IDS) u Emphasis on: v Cooperative Systems (infrastructure + in-vehicle) v Driver Decision Aids (gap, velocity) v Crossing-Path Collisions (Signalized and Unsignalized) 78.1% of Intersection Crashes (1998 GES) u Focus on driver error causal factors v For example: Provide the driver with information that will improve judgment of gap clearance and timing u Cooperative systems: v NHTSA sponsored work focus on in-vehicle systems v Infrastructure Consortium formed to focus on infrastructure

6. Crossing Path Crash Causal Factors at Intersections (1998 GES) From Najm W G, Koopmann J A, and Smith D L (2001) Analysis of Crossing Path Crash Countermeasure Systems. Proc. 17 th Intl Conference on Enhanced Safety of Vehicles LTAP/OD: Left turn across path/ opposite direction LTAP/LD: Left turn across path/ lateral direction LTIP: Left turn into path RTIP: Right turn into path SCP: Straight crossing paths Right angle Virtually no crashes in shaded cells. Left turn Right turn

7. The IDS Team: IVI Infrastructure Consortium u IVI Infrastructure Consortium Principals v California DOT (Caltrans) v Minnesota DOT v Virginia DOT v USDOT (FHWA) u Universities conducting the IDS research v U.C. Berkeley (California PATH, other units) v University of Minnesota (ITS Institute, other units) v Virginia Tech (VTTI)

8. Special Concentrations by State u Virginia team v Straight-crossing-path (SCP) crashes v Tests on ‘Smart Road’ intersection v Intentional and unintentional signal/stop violation v Focus: Warn violator (DVI and DII) u California team v Left Turn Across Path/ Opposite Direction (LTAP/OD) v Urban intersections v Tests on ‘Richfield Field Station’ intersection v Wireless communications for cooperative systems

9. Why Rural Intersections? u Crashes in rural areas are more severe than in urban areas v While 70% of all crashes in Minnesota occur in urban areas, 70% of fatal crashes occur in rural areas. u Along Minnesota’s Trunk Highway System, there are more rural through/stop intersections (3,920) than all categories of urban intersections (3,714) combined u During a three-year period (1998-2000), 62% of all intersection-related fatal crashes in Minnesota occurred at rural through/stop intersections

11. Minnesota Focus u Rural unsignalized intersections: v High-speed corridors v Through stop intersections u Traffic surveillance technologies (& on-site validation) u Gap detection/estimation (& on-site validation) u Human interface design u All intersection crash types occur at IRC intersections v Study will indicate which are prevalent v IRC intersection selected for tests & on-site validation based on crash analysis

12. Guiding Principles u Effective countermeasures depend on: v Capability to sense and predict the behavior of all vehicles “within” the intersection’s Region Of Interest (ROI) v Ability to predict with high probability at the appropriate time the gap positions v Ability to predict the time at which a vehicle CANNOT be released at a rural high speed intersection v A means to effectively communicate with driver(s) (and eventually vehicles) appropriate actions v Ability to cost effectively deploy needed technology to infrastructure (and eventually to vehicles)

13. Addressing Rural Intersection Safety Issues: u The primary problem at rural intersections involves a driver on the minor road selecting an unsafe gap in the major road traffic stream. u Consider study of 1604 rural intersections (2-lane roadways, Thru/STOP intersection control only, no medians) over 2+ year period.

14. Addressing Rural Intersection Safety Issues: u Analyzed 768 right angle crashes on 409 different intersections. u Nearly 60% occur after vehicle on the minor roadway stops u Approximately 25% involved vehicle running through the STOP sign. Source: Howard Preston CH2MHill … i.e. problem is one of gap selection, NOT intersection recognition

15. Guidelines for Implementation of AASHTO Strategic Highway Safety Plan u NCHRP Report 500: Vol. 5 Unsignalized Intersections v Identifies objectives and strategies for dealing with unsignalized intersections v Objective 17.1.4 Assist drivers in judging gap sizes at Unsignalized Intersections v High speed at grade intersections

16. MN Pooled Fund Project: Towards a Multi-State Consensus Minnesota is leading a state pooled fund project for rural intersection IDS Multiple goals of state pooled fund: u Assistance/buy-in of DII design v Goal: nationally acceptable designs Performance Maintenance Acceptability u Increased data collection capability v Test intersections in participating states v Regional vs. national driver behavior

17. MN Pooled Fund Project: Towards a Multi-State Consensus Premise behind pooled fund project v States provide their perspective to rural intersection project v Nationally inter-operable systems will result Work distribution v Minnesota provides intersection instrumentation design guidance/assistance to states v States provide $ and resources to instrument test intersection in their state v States provide intersection instrumentation data to Minnesota for analysis Insures sufficient data for statistically valid results and conclusions Regional driver variability/coherence can be quantified v Minnesota analyzes states’ data, and provides results and feedback to participating states

18. Successful Demonstration, June 2003 u Turner Fairbanks Highway Research Center, McLean, VA u View simulation

19. IDS Program u Tasks v Crash Analysis v Enabling Research v Benefit:Cost Analysis v System Design

20. Task A: Crash Analysis u Analysis of present conditions and intersections …. Howard Preston, lead u Identification of Experimental Site: Minnesota Crash Data Analysis 3,784 Thru-STOP Isxns in MN Hwy System were evaluated Total > CR (% of total) 2-Lane - 3,388 | 104 (~ 3%) Expressway - 396 | 23 (~ 6%)

21. Location of Selected Intersection MN Hwy 52 & CSAH 9

22. Sight distance restricted on the W approach at CSAH 9 Note differences in N and S vertical alignments

23. Satellite Image

24. Prediction of Countermeasure Effects u Modeling of relationships between intersection characteristics and crash propensity … Gary Davis, Principal Investigator v Identify both atypically safe and unsafe intersections v Associate characteristics with both v Use info for deployment and benefit:cost analyses v Predict accident reduction based on potential deployment scenarios

25. STATISTICAL MODELING Gary Davis, Nebiyou Tilahun, Paula Mesa u Objectives v Predict Accident Reduction Effect of IDS Deployment on (All, Some) Stop-Controlled Rural Expressway Intersections in Minnesota v Determine if Older Drivers are Over-Represented at Stop-Controlled Rural Expressway Intersections in Minnesota v Assess Sensitivity of Accident Reduction Effect on Predicted Changes in Distribution of Driver Ages

26. STATISTICAL MODELING: Predict effect of IDS on accident reduction u Research Approach v Adapt Accident Prediction Methods Developed for FHWA's Interactive Highway Safety Design Module (IHSDM) to Stop- Controlled Rural Expressway Intersections in Minnesota; Use 'Standard' Extrapolation Methods to Forecast Changes in Traffic Volumes v Accident prediction model predicts number of total intersection related accidents per year (N int ), after application of accident modification factor (AMF, yet to be determined) to a base model prediction (N bi : predicted number of total intersection related accidents per year for nominal or base conditions). N int = AMF x N bi where N bi = exp (b 0 + b 1 ln ADT maj + b 2 ln ADT min ) and b 0, b 1, b 2 are to be determined for the intersection type under consideration

27. STATISTICAL MODELING: Predict effect of IDS on accidents involving older drivers u Adapt Induced Exposure Methods to Estimate Relative Risk to Older Drivers at Stop-Controlled Rural Expressway Intersections in Minnesota v Assumes that one can determine the at-fault and innocent drivers in 2 vehicle accidents. From the crash data base, use the proportion of older innocent drivers at each intersection to estimate the relative exposure of older drivers. The relative risk for older drivers can then be estimated. v Results in rank ordering of intersections by risk to older drivers u Will try to adapt Exogenous Sampling Methods for Choice-Based Samples to Develop Age-Specific Accident Base models, ie N bi for specific range of ages u Use Census Bureau's "Projections of the Population by Age, Sex and Race for the United States“ to predict age-specific accident frequencies. u Apply AMF to age-specific accident frequencies to estimate age- specific accident reductions.

28. References: u Vogt, A, and Bared, J., (1998) Accident Models for Two-Lane Rural Roads: Segments and Intersections, Report FHWA-RD-98-133, Federal Highway Administration, Washington, DC. u Harwood, D., Council, F., Hauer, E., Hughes, W., and Vogt, A. (2000) Prediction of the Expected Safety Performance of Rural Two- Lane Highways, Report FHWA-RD-99-207, Federal Highway Administration, Washington, DC. u Davis, G. and Yang, S. (2001) Bayesian identification of high-risk intersections for older drivers via Gibbs sampling, Transportation Research Record, 1746, 84-89.

29. Task B: Enabling Research u Surveillance, Alec Gorjestani, Principal Investigator u Sensors – Determine location and speed of high speed road vehicles Determine type of vehicle on low speed road (signal timing) Sensor placement, intersection design, etc. u Communications Transmit data from sensors to IDS main processor Wire / Fiber Optic / Wireless options u Computational systems Determine location, speed, and size of traffic gaps u Performance issues: Redundancy, reliability, range, power, cost, estimation vs. sensor coverage, etc.

30. Enabling Research u Test Intersection v Once Candidate Intersection selected, design test infrastructure IDS System sensors, power, processors, and associated cabinets within Mn/DOT R-O-W alongside road, in advance of cross roads Test and validation system consisting of cameras, and supporting structures (masts, power cabinets, etc.).

31. Sensors  Must provide at least 10 second warning at intersection with vehicles traveling at 60 mph, need information from at least 880 feet out (10 x 88 ft) at the Driver Infrastructure Interface (DII) controller  As vehicle speeds vary, gap sizes may change.  Must track all gaps (‘safe’ and ‘unsafe’) as they approach the Isxn.  Need to determine location of sensors to provide adequate advance preview.

32. Surveillance System - Overview u System designed to record the location and velocity of every vehicle at or approaching the intersection u Surveillance system consists of an array of sensors v Radar, Lidar (LIght Detection And Ranging) v Vision – visible and infrared, image processing u Sensor data transmitted to central processor v Sensor data filtered and fused v Intersection vehicle state matrix v Gaps in traffic calculated v Warnings can be generated for Driver Infrastructure Interface (DII)

33. Eaton VORAD EVT-300 Radar (24.725 GHz) u Provides range, range rate, azimuth to 7 targets u Maximum range = 350 ft; 500 ft. when stationary u Maximum range rate = 120 mph u Beam geometry = 12 degrees azimuth u Elevation angle = 5 degrees u Strengths v Long range, very accurate range rate, weather insensitive u Weaknesses v Targets ‘drop out’ at low relative speed v Performance better when close to road u Ideal for Hwy 52 main line traffic

34. Surveillance System – Radar u Radar sensors placed along roadside to detect high speed vehicles entering the intersection

35. Surveillance System - Lidar u SICK LMS 221 v 2D scan, 180 degree v Range: 30 meters for 10% reflectivity target u Strengths v Very accurate range: +/- 10 mm v Good angle resolution: 0.25 degree v Can obtain vehicle profile for classification u Weaknesses v No range rate provided, must calculate from successive scans v Low maximum range v Performance in snow unknown u Ideal for County Road 9 v Slow moving or stopped vehicles

36. Surveillance System - Lidar Horizontal AlignmentVertical Alignment

37. Surveillance System – Vision u Visible light and IR cameras v Visible light camera – records visible light to images v IR camera – records temperature of radiating bodies as image u Unable to place hardware in median v Likely to get hit by vehicle or snowplow blade v Must monitor slow moving vehicles in median from distance u Strengths v Can be placed further from road (zoom lens) v Multiple vehicles detected at once u Weaknesses v Image processing more complex v Inconsistent lighting problematic (visible light)

38. Surveillance System - Vision u IR image of Cadillac Escalade u Hot engine stands out against background

39. Surveillance System – Communication Sensor data to central processing unit u Use wired communication to test wireless’ suitability for surveillance system v Needed to trench for power anyway, therefore will install wired network; enable us to compare various wireless schemes against a wired network; cost vs. performance tradeoffs u Wired Ethernet network v Fast – 100 Mbit/sec; Reliable v Disadvantage: Must run cables long distances, need repeaters u Wireless v Slower – 11 Mbit/sec for 802.11b v Not as reliable, interference, retransmission v Advantage: No need to run cables

40. Surveillance System – Evaluation u System must be evaluated v Collected data must be accurate v System must be reliable u Individual sensor accuracy tested using DGPS equipped probe vehicles u Surveillance system accuracy also tested with probe vehicles u Cameras will later be used to validate radar system’s reliability v Cameras placed on major legs v Images compared with radar data, will count misses

41. Radar Validation

43. Determination of the Radar Yaw Angle (w.r.t. North)

44. u Experiments used to determine optimal location and angle for radar u Locate radar 12 ft laterally from road u Height: 15 inches u Sensor orientation angle: 4.85 – 5.05 degrees Radar Station for Validation

45. Surveillance System – Data Collection u Intersection Data AcQuisition System (iDAQ) v Stores all engineering data Vehicle states – X, Y, Speed, Class, Gap v Stores images from cameras MPEG4 image capture board 4 channels v Engineering and video data time synchronized v Removable hard drive v R/WIS station within ½ mile of Hwy 52 and CSAH 9.

49. Radar Installation with Respect to Road u The radar is 12 feet from the nearest drivable surface. u If ditch slope is considered, radar is between 3 and 4.5 feet from the ground.

50. Contract Issues u U of MN will be bidding job u Request for bids ready very soon u Feb: Bidding process u March: Select Contractor u April: Work out contract u May: Complete the job; bring intersection on- line.

51. u Misperceptions of gap size and/or location u Speed misjudgments u Driver vigilance, situation awareness u Learned Inattention u Age related effects u Communication to drivers u Outlier behavior Human Factors Issues

52. Enabling Research u Human factors…Nic Ward, Principal Investigator u Applying human-centered approach to problem of intersection crashes because the state of the driver and the actions of the driver are the most common factors identified in police crash reports. u Consider the number of fatal crashes in MN between 1998 and 2000 that had one of the following crash factors: - Driver (the state of the driver, eg impairment) - Driving (the actions of the driver, eg speeding) - Vehicle (faults or failures with vehicle and its components) - Environment (factors in the environment such as weather and road conditions).

53. Human-Centered

54. V.E.S.T.R

55. Task C: Benefit:Cost Analysis Principal Investigator – David Levinson u Three phased approach: v Do Nothing (baseline values) Costs associated with intersection crashes v Install Traditional Signals (or re-grade) Costs: Hardware, design, power, decreased traffic flow rates, etc. Benefits: (possibly) fewer crashes, less severe crashes, perceived value by motoring public v Implementing an IDS System Costs: Hardware, design, power, public education, etc. Benefits: decreased crash rates, severity, maintenance of traffic flow rates, surrogate economic benefits

56. Benefit:Cost Analysis u To include: v System Optimization Examine reliability, redundancy, sensor performance, sensor coverage vs. gap estimation, cost:performance sensitivity v Deployment Modeling Identification of candidate intersections based on crash numbers, crash severity, and other characteristics (based on Gary Davis’ work). Development of deployment strategies and models based on cost vs. performance vs. estimated effectiveness at rural IRC intersections

57. Benefit Cost Analysis: Concept

58. Benefit Cost Analysis: Framework

59. Task D: System Requirements & Specification Definition u Functional Requirements u System Requirements u System Specifications u Experimental MUTCD Approval v Driver interface likely to fall outside the normal devices found within the MUTCD. Will need to work to gain MUTCD approval as soon as candidate interface is determined