Multiple Sources of Safety Information From V2V and V2I: Redundancy, Decision Making, and Trust - Safety Message Design Report

Chapter 4. Summary and Conclusions

This report is part of the HFCV research program, whose goal is to minimize driver workload by eliminating CV device-related distractions. The research described in this document is part of an effort to develop initial design guidance for V2I safety messages provided using DIIs and DVIs. Emerging CV technologies and applications have the potential to significantly improve safety on U.S. roads; however, it will take many years for CV technology to reach sufficient penetration levels in the US vehicle fleet for those benefits to be realized. In the meantime, DII displays can effectively communicate safety messages even for vehicles that do not have integrated CV applications. Thus, DII-based approaches provide an important transition to the CV future, in addition to providing additional countermeasure options to address local safety problems. The current report provides a starting point for roadway engineers and other DOT personnel considering developing and implementing infrastructure-based safety measures that include DII components.

Although there is sufficient general and application-specific research related to using DII displays to communicate safety messages to drivers, this area of investigation is still in an early stage. The current effort drew upon on the best available research information to provide initial design guidance for developing effective DIIs. Existing HFCV research, in addition to previous research from related domains, was used to develop this preliminary design guidance. A total of 12 guidelines were developed using this approach.

A clear finding throughout the report was that although there was only a limited body of research covering specific CV applications, a substantial amount of research has been conducted on intelligent infrastructure-based safety applications in general. Therefore, for most of the guideline topics, there was sufficient existing research to develop useful design guidance, typically by adapting research findings obtained from non-CV safety systems that had key operational characteristics in common with the relevant CV applications. This approach was particularly effective for the general design guidance topics presented in the first part of the document (topics 1 through 7).

The application-specific guidelines (topics 8 through 12) also contain more specific design information that relates to individual applications; however, these guidelines should generally be considered preliminary design references at the time of this publication. While every attempt was made to use the most recent information about how these systems are intended to work, their full operational specifications are still being developed. Also, it should be noted that the basic strategy for guideline development in these topics employed an opportunistic approach with regards to the specific type of guidance provided in each topic. That is, the content for each topic was largely determined by focusing on the best available research information. This enabled the inclusion of at least some design information on each topic, and for all the key systems; however, this information was not as uniform across topics. It would be valuable to identify key information gaps that currently exist across the topics, so that they could be addressed in future research. Moreover, real world implementation and evaluation information about these technologies is largely missing.

Consequently, an important avenue for future research is to investigate driver behavior and responses to infrastructure-based CV applications under more realistic driving conditions. This research would be particularly relevant if DII messages were examined in combination with other in-vehicle safety information, since this will likely represent future operating environment in a CV world. These safety systems must strike a fine balance in terms of augmenting drivers’ understanding of their situation with relevant safety information, while at the same time avoiding annoying or distracting drivers with information that is unneeded, presented at the wrong time, or in conflict with other more important safety information. Examining how drivers manage infrastructure-based safety information in the context of the full range of on-road driving tasks, and the priorities that drivers assign to them, represents an important step for ensuring that these systems are developed and implemented in a way that maximizes the benefits to drivers.

In the meantime, the current document provides some of the most comprehensive and detailed design guidance for infrastructure-based CV systems. While individual guidelines discuss key design issues and the associated Human Factors considerations, they also serve as a useful starting point for users to find relevant research sources that provide more detailed information about specific topics or design issues. Importantly, the guidelines presented in this document compliment other Human Factors design references and address key information needs. In particular, the NCHRP Report 600: Human Factors Guidelines for Road Systems provides human factors design guidance for infrastructure, but it does not address connected infrastructure elements.⁽¹¹⁾ Similarly, the Human Factors for Connected Vehicles Driver-Vehicle Interface Design Principles focuses exclusively on in-vehicle safety applications, and do not cover infrastructure-based systems in detail. Thus, the current guideline document provides useful and complementary information to other recent State transportation department design documents.

References

1. SAE J2735. (2009). Dedicated short range communications (DSRC) message set dictionary, Warrendale, PA: SAE International.
   
   
2. United States Department of Transportation. (2014). Connected Vehicle Applications: Vehicle-to-Infrastructure (V2I) Communications for Safety, retrieved from http://www.its.dot.gov/safety/v2i_comm_safety.htm.
   
   
3. Green, P. (2002). “Motor Vehicle Driver Interfaces,” in J.A. Jacko and A. Sears (Eds.), The Human-Computer Interaction Handbook, Hillsdale, NJ: Erlbaum, 844–860.
   
   
4. Jackels, J. (2010). Cooperative Intersection Collision Avoidance System: Stop Sign Assist Field Operational Test, Presentation at the National RITS Conference, retrieved from: www.nritsconference.org/downloads/Presentations10/D2_Jackels.pdf.
   
   
5. Misener, J. Chan, C.Y., Dickey, S., Kim, Z.W., Kuhn, T., Lian, T., . . . Zhou, K. (2010). Cooperative Intersection Collision Avoidance System (CICAS): Signalized Left Turn Assist and Traffic Signal Adaptation (No. UCB-ITS-PRR-2010-20), retrieved from the University of California, Berkeley PATH Program Web site at http://www.path.berkeley.edu/PATH/Publications/PDF/PRR/2010/PRR-2010-20.pdf.
   
   
6. Neale, V.L., Perez, M.A., Doerzaph, Z.R., Lee, S.E., Stone, S., and Dingus, T.A. (2006). Intersection Decision Support: Evaluation of a Violation Warning System to Mitigate Straight Crossing Path Collisions (Report No. FHWA/VRTC 06-CR10), Charlottesville, VA: Virginia Transportation Research Council.
   
   
7. Federal Highway Administration. (2012). Manual on Uniform Traffic Control Devices, Washington, DC.
   
   
8. Bertini, R.L., Monsere, C.M., Nolan, C., Bosa, P., and El-Seoud, T.A. (2006). Field Evaluation of the Myrtle Creek Advanced Curve Warning System (SPR 352; FHWA-OR-RD-06-13), Retrieved from http://www.oregon.gov/ODOT/HWY/ITS/pdfs/benefitsofits/myrtle_creek_report_publish.pdf.
   
   
9. Lichty, M.G., Richard, C.M., Campbell, J.L., and Bacon, L.P. (2012). Guidelines for Disseminating Road Weather Advisory & Control Information (Report No. FHWA-JPO-12-046), Washington, DC: Federal Highway Administration.
   
   
10. Divekar, G., Richard, C.M., Jackson, J.E. (in press). Multiple Sources of Safety Information from V2V and V2I: Redundancy, Decision-making, and Trust, Washington, DC: National Highway Traffic Safety Administration.
    
    
11. Campbell, J.L., Lichty, M.G., Brown, J.L., Richard, C.M., Graving, J., Graham, J., O’Laughlin, M., … Harwood, D. (2012). NCHRP Report 600: Human Factors Guidelines for Road System, Second Edition, Washington, DC: Transportation Research Board.
    
    
12. Campbell, J.L., Richard, C.M., and Graham, J. (2008). Human Factors Guidelines for Road System. Collection A: Chapters 4, 5, 10, 11, 13, 22, 23, 26 (NCHRP Report 600A), Washington, DC: Transportation Research Board.
    
    
13. Campbell, J.L., Brown, J.L., Richard, C.M., and Graham, J. (2008). Human Factors Guidelines for Road Systems. Collection B: Chapters 6, 22 (Tutorial 3), 23 (Updated) (NCHRP Report 600B), Washington, DC: Transportation Research Board.
    
    
14. Campbell, J.L., Richard, C.M., Brown, J.L., and McCallum, M. (2007). Crash Warning System Interfaces: Human Factors Insights and Lessons Learned (Report No. DOT HS 810 697), Washington, DC: National Highway Traffic Safety Administration.
    
    
15. Doctor, M., Merritt, G., and Moler, S. (2009). “Designing Complex Interchanges,” Public Roads, 73(3), 3–11.
    
    
16. Lerner, N.D., Llaneras, R.E., McGee, H.W., and Alexander, G. (2003). NCHRP Report 488: Additional Investigations on Driver Information Overload, Washington, DC: Transportation Research Board.
    
    
17. Gugerty, L., McIntyre, S.E., Link, D., Zimmerman, K., Tolani, D., Huang, P., and Pokorny, R.A. (2014). “Effects of Intelligent Advanced Warnings on Drivers Negotiating the Dilemma Zone,” Human Factors, 56(6), 1,021–1,035.
    
    
18. Stephens, D.R., Timcho, T.J., Young, E., Klein, R.A., and Schroeder, J.L. (2014). Accelerated Vehicle-to-Infrastructure (V2I) Safety Applications Concept of Operations Document (Technical Report), Columbus, OH: Battelle Memorial Institute.
    
    
19. Becic, E., Manser, M.P., Creaser, J., and Donath, M. (2012). Cooperative Intersection Collision Avoidance System—Stop Sign Assist: Experiments to Validate the Use of an In-Vehicle Interface Design (Report No. CTS 12-09), Minneapolis, MN: University of Minnesota Department of Engineering.
    
    
20. Saad, F. (2002). “Ergonomics of the Driver’s Interface with the Road Environment: The Contribution of Psychological Research,” In R. Fuller and J.A. Santos (Eds.), Human Factors for Highway Engineers, Oxford, UK: Elsevier, 23–41.
    
    
21. Lees, M.N. and Lee, J.D. (2007). “The Influence of Distraction and Driving Context on Driver Response to Imperfect Collision Warning Systems,” Ergonomics, 50, 1,264–1,286.
    
    
22. Campbell, J.L., Brown, J.L., Graving, J.S., Richard, C.M., Lichty, M.G., Sanquist, T., Bacon, L.P., … Morgan, J.F. (in press). Human Factors Design Principles for the Driver Vehicle Interface (DVI), Version 1.2, Washington, DC: National Highway Traffic Safety Administration.
    
    
23. Nowakowski, C., Cody, D., and O’Connell, J. (2008). “Comparison of Infrastructure and In-Vehicle Driver Interfaces for Left-Turn Warnings,” Transportation Research Record 2069, Transportation Research Board, Washington, DC, 33–40.
    
    
24. Bliss, J.P. and Acton, S.A. (2003). “Alarm Mistrust in Automobiles: How Collision Alarm Reliability Affects Driving,” Applied Ergonomics, 34, 499–509.
    
    
25. McIntyre, S., Link, D., Zimmerman, K., Tolani, D., Huang, P., and Pokorny, R. (2012). “Lane Specific Dilemma Zone Warnings at Signalized Intersections,” Proceedings of the Human Factors & Ergonomics Society, 56, 2,221–2,225.
    
    
26. Richard, C.M., Campbell, J.L., and Brown, J.L. (2008). “A Procedure for Developing Warning Message Prioritization Rules in Integrated Collision Warning Systems for Heavy Trucks,” Proceedings of the Human Factors & Ergonomics Society Annual Meeting, 52, 1860–1864.
    
    
27. Doerzaph, Z., Sullivan, J., Bowman, D., and Angell, L. (2013). Connected Vehicle Integration Research and Design Guidelines Development: Integration Architecture, Task 5, Report to the National Highway Traffic Safety Administration, Blacksburg, VA: Virginia Tech Transportation Institute.
    
    
28. Inman, V.W. and Davis, G.W. (2010). “Effects of In-Vehicle and Infrastructure-Based Collision Warnings to Nonviolating Drivers at Signalized Intersections,” Transportation Research Record 2189, Transportation Research Board, Washington, DC, 17–25.
    
    
29. Ng, A.W.Y. and Chan, A.H.S. (2007). “The Guessability of Traffic Signs: Effects of Prospective-User Factors and Sign Design Features,” Accident Analysis & Prevention, 39, 1,245–1,257.
    
    
30. Shinar, D., Dewar, R.E., Summala, H., and Zakowska, L. (2003). “Traffic Sign Symbol Comprehension: A Cross-Cultural Study,” Ergonomics, 46(15), 1,549–1,565.
    
    
31. Dudek, C.L. (2004). Changeable Message Sign Operation and Messaging Handbook. (Report No. FHWA-OP-03-070), Washington, DC: Federal Highway Administration.
    
    
32. Ben-Bassat, T. and Shinar, D. (2006). “Ergonomic Guidelines for Traffic Sign Design Increase Sign Comprehension,” Human Factors, 48(1), 182–195.
    
    
33. Campbell, J.L., Richman, J.B., Carney, C., and Lee, J.D. (2004). In-Vehicle Display Icons and Other Information Elements, Volume I: Guidelines (Report No. FHWA-RD-03-065), Washington, DC: Federal Highway Administration, retrieved from https://www.fhwa.dot.gov/publications/research/safety/03065/index.cfm.
    
    
34. Dudek, C.L. and Ullman, G.L. (2006). Dynamic Message Sign Message Design and Display Manual (Report No. FHWA/TX-04/0-4023-P3), Washington, DC: Federal Highway Administration.
    
    
35. Shinar, D. and Vogelzang, M. (2013). “Comprehension of Traffic Signs with Symbolic Versus Text Displays,” Transportation Research Part F: Traffic Psychology and Behaviour, 18, 72–82.
    
    
36. Dewar, R.E. and Ells, J.G. (1974). “A Comparison of Three Methods for Evaluating Traffic Signs,” Transportation Research Record, 503, Transportation Research Board, Washington, DC, 38–47.
    
    
37. Ells, J.G. and Dewar, R.E. (1979). “Rapid Comprehension of Verbal and Symbolic Traffic Sign Messages,” Human Factors 21(2), 161–168.
    
    
38. Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y.C., de Visser, E.J., and Parasuraman, R. (2011). “A Meta-Analysis of Factors Affecting Trust in Human-Robot Interaction,” Human Factors, 53, 517–527.
    
    
39. Lee, J.D. and See, K.A. (2004). “Trust in Automation: Designing for Appropriate Reliance.” Human Factors, 46, 50–80.
    
    
40. Muir, B.M. and Moray, N. (1996). “Trust in Automation. Part II. Experimental Studies of Trust and Human Intervention in a Process Control Simulation,” Ergonomics, 39, 429–460.
    
    
41. Lee, J.D. and Moray, N. (1994). “Trust, Self-Confidence, and Operators’ Adaptation to Automation,” International Journal of Human Computer Studies, 40, 153–184.
    
    
42. Kantowitz, B.H., Hanowski, R.J., and Kantowitz, S.C. (1997). “Driver Acceptance of Unreliable Traffic Information in Familiar and Unfamiliar Settings,” Human Factors, 39, 164–176.
    
    
43. Gordon, K.M., Anderson, S.H., Gribble, B., and Johnson, M. (2001). Evaluation of the FLASH (Flashing Light Animal Sensing Host) System in Nugget Canyon, Wyoming (Report No FHWA-WY-01/03F), Washington, DC: Federal Highway Administration.
    
    
44. Higgins, L., Balke, K., and Chrysler, S.T. (2012). “Driver Responses to Signing Treatments for Flooded Roads,” Transportation Research Record 2321, Transportation Research Board, Washington, DC, 98–107.
    
    
45. Parasuraman, R., Hancock, P.A., and Olofinboba, O. (1997). “Alarm Effectiveness in Driver-Centered Collision-Warning Systems,” Ergonomics, 40, 390–399.
    
    
46. Parasuraman, R. and Miller, C.A. (2004). “Trust and Etiquette in High-Criticality Automated Systems,” Communications of the ACM, 47(4), 51–55.
    
    
47. Weinstock A., Oron-Gilad T., and Parmet Y. (2012). “The Effect of System Aesthetics on Trust, Cooperation, Satisfaction and Annoyance in an Imperfect Automated System,” Work, 41(1), 258–265.
    
    
48. Madhavan, P. and Wiegmann, D.A. (2007). “Effects of Information Source, Pedigree, and Reliability on Operator Interaction with Decision Support Systems,” Human Factors, 49, 773–785.
    
    
49. Creaser, J., Manser, M., Rakauskas, M., and Donath, M. (2010). Sign Comprehension, Considering Rotation and Location, Using Random Gap Simulation for Cooperative Intersection Collision Avoidance System—Stop Sign Assist: CICAS-SSA Report #4 (Report No. CTS 10-34), Minneapolis, MN: University of Minnesota Human FIRST Program.
    
    
50. Ragland, D.R., Arroyo, S., Shladover, S.E., Misener, J.A., and Chan, C. (2006). “Gap Acceptance for Vehicles Turning Left Across On-Coming Traffic: Implications for Intersection Decision Support Design (Paper 06-2696),” Transportation Research Board 85th Annual Meeting Compendium of Papers (CD ROM]).
    
    
51. Devarasetty, P.C., Zhang, Y., and Fitzpatrick, K. (2012). “Differentiating Between Left-Turn Gap and Lag Acceptance at Unsignalized Intersections as a Function of Site Characteristics,” Journal of Transportation Engineering, 138(5), 580–588.
    
    
52. Gorjestani, A., Menon, A., Cheng, P.M., Newstrom, B., Shankwitz, C., and Donath, M. (2010). Macroscopic Review of Driver Gap Acceptance and Rejection Behavior at Rural Thru-Stop Intersections in the U.S.—Data Collection Results in Eight States: CICAS-SSA Report #3, Minneapolis, MN: University of Minnesota Intelligent Vehicles Laboratory.
    
    
53. Radwan A.E. and Sinha, K.C. (1980). “Gap Acceptance and Delay at Stop Controlled Intersections on Multi-Lane Divided Highways,” ITE Journal, 50(3), 38–44.
    
    
54. Zhody, I., Sadek, S., and Rakha, H.A. (2010). “Empirical Analysis of Effects of Wait Time and Rain Intensity on Driver Left-Turn Gap Acceptance,” Transportation Research Record 2173, Transportation Research Board, Washington, DC, 1–10.
    
    
55. Hamed, M., Easa, S., and Batayneh, R. (1997). “Disaggregate Gap-Acceptance Model for Unsignalized T-Intersections,” Journal of Transportation Engineering, 123(1), 36–42.
    
    
56. Tupper, S., Knodler, M., and Hurwitz, D. (2011). Connecting Gap Acceptance Behavior with Crash Experience, paper presented at the 3d International Conference on Road Safety and Simulation Conference Compendium, retrieved from http://onlinepubs.trb.org/onlinepubs/conferences/2011/RSS/1/Tupper,S.pdf.
    
    
57. Yan, X. and Radwan, E. (2008). “Influence of Restricted Sight Distance on Permitted Left-Turn Operation at Signalized Intersections,” Journal of Transportation Engineering, 134(2), 68–76.
    
    
58. Kittleson, W.K. and Vandehey, M.A. (1991). “Delay Effects on Driver Gap Acceptance Characteristics and Two-Way Stop Controlled Intersections,” Transportation Research Record 1320, Transportation Research Board, Washington, DC, 160–167.
    
    
59. Gerlough, D.L. and Huber, M.J. (1976). Traffic Flow Theory—A Monograph (Special Report 165), Washington, DC: Transportation Research Board.
    
    
60. Ashalatha, R. and Chandra, S. (2011). “Critical Gap through Clearing Behavior of Drivers at Unsignalized Intersections,” KSCE Journal of Civil Engineering, 15(8), 1,427–1,434.
    
    
61. Pollatschek, M.A., Polus, A., and Livneh, M. (2002). “A Decision Model for Gap Acceptance and Capacity at Intersections,” Transportation Research Part B, 36, 649–663.
    
    
62. Polus, A., Craus, J., and Reshetnik, I. (1996). “Non-Stationary Gap Acceptance Assuming Drivers’ Learning and Impatience,” Traffic Engineering & Control, 37(6), 395–402.
    
    
63. Brilon, W., Koenig, R., and Troutbeck, R.J.(1999). “Useful Estimation Procedures for Critical Gaps,” Transportation Research Part A, 33(3), 161–186.
    
    
64. Yan, X., Radwan, E., and Gou, D. (2007). “Effects of Major-Road Vehicle Speed and Driver Age and Gender on Left-Turn Gap Acceptance,” Accident Analysis and Prevention, 39(4), 843–852.
    
    
65. Lerner, N., Huey, R.W., McGee, H.W., and Sullivan, A. (1995). Older Driver Perception-Reaction Time for Intersection Sight Distance and Object Detection (Report No. FHWA-RD-93-168), Washington, DC: Federal Highway Administration.
    
    
66. Harwood, D.W., Mason, J.M., and Brydia, R.E. (1999). “Design Policies for Sight Distance at Stop-Controlled Intersections Based on Gap Acceptance,” Transportation Research Part A, 33, 199–216.
    
    
67. Hanscom, F.R. (2001). Evaluation of the Prince William County Collision Countermeasure System (Report No. FHWA/VTRC 01-CR5). Washington, DC: Federal Highway Administration, retrieved from http://www.virginiadot.org/vtrc/main/online_reports/pdf/01-cr5.pdf.
    
    
68. Lyles, R.W. (1980). “Evaluation of Signs for Hazardous Rural Intersections.” Transportation Research Record 782, Transportation Research Board, Washington, DC, 22–30.
    
    
69. Rakauskas, M., Creaser, J., Manser, M., Graving, J., and Donath, M. (2009). Validation Study—On-Road Evaluation of the Stop Sign Assist Decision Support Sign: CICAS-SSA Report #5, Minneapolis, MN: University of Minnesota Human FIRST Program.
    
    
70. Golembiewski, G.A. and Chandler, B. (2011). Intersection Safety: A Manual for Local Rural Road Owners (Report No. FHWA-SA-11-08), Washington, DC: Federal Highway Administration, retrieved from http://safety.fhwa.dot.gov/local_rural/training/fhwasa1108/index.cfm.
    
    
71. Hayes, C. and Drew, D. (2011). In-vehicle Decision Support to Reduce Crashes at Rural Thru-Stop Intersections, Minneapolis, MN: University of Minnesota Intelligent Transportation Systems Institute Center for Transportation Studies.
    
    
72. Laberge, J.C., Creaser, J.I., Rakauskas, M.E., and Ward, N.J. (2006). “Design of an Intersection Decision Support (IDS) Interface to Reduce Crashes at Rural Stop-Controlled Intersections,” Transportation Research Part C, 14, 39–56.
    
    
73. Maze, T.H., Hochstein, J.L., Souleyrette, R.R., Preston, H., and Storm, R. (2010). NCHRP Report 650: Median Intersection Design for Rural High-Speed Divided Highways, Washington, DC: Transportation Research Board.
    
    
74. Castro, C., Moreno-Rios, S., Tornay, F., and Vargas, C. (2008). “Mental Representations of Obligatory and Prohibitory Traffic Signs,” Acta Psychologia, 129, 8–17.
    
    
75. Roka, J., Castro, C., Bueno, M., and Moreno-Rios, S. (2012). “A Driving-Emulation Task to Study the Integration of Goals with Obligatory and Prohibitory Traffic Signs,” Applied Ergonomics, 43, 81–88.
    
    
76. Creaser, J., Rakauskas, M., Ward, N., and Laberge, J. (2007). A Simulator-Based Evaluation of Smart Infrastructure Concepts for Intersection Decision Support for Rural Thru-STOP Intersections (Report No. MN/RC-2007-31), St. Paul, MN: Minnesota Department of Transportation.
    
    
77. Knodler, M.A. and Noyce, D.A. (2005). “Tracking Driver Eye Movements at Permissive Left-Turns,” Proceedings of the Third International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, 134–142.
    
    
78. White, B. and Eccles, K. A. (2002). “Inexpensive, Infrastructure-Based, Intersection Collision-Avoidance System to Prevent Left-Turn Crashes with Opposite-Direction Traffic,” Transportation Research Record 1800, Transportation Research Board, Washington, DC, 92–99.
    
    
79. Eccles, K., Gross, F., Liu, M., and Council, F. (2012). Crash Data Analyses for Vehicle-to-Infrastructure Communications for Safety Applications (Report No. FHWA-HRT-11-040), Washington, DC: Federal Highway Administration.
    
    
80. Rephlo, J.A. (2013). Connected Vehicles for Safety, Mobility, and User Fees: Evaluation of the Minnesota Road Fee Test, retrieved from the Minnesota Department of Transportation Web site at http://www.dot.state.mn.us/mileagebaseduserfee/pdf/EvaluationFinalReport.pdf.
    
    
81. PlavŠić, M., Bengler, K.J., and Bubb, H. (2010). “Analysis of Glance Movements in Critical Intersection Scenario,” In Proceedings of the 3d International Conference on Applied Human Factors and Ergonomics, Miami, FL.
    
    
82. Hendricks, D.L., Fell, J.C., and Freedman, M. (1999). The Relative Frequency of Unsafe Driving Acts in Serious Traffic Crashes (Report No. DTNH22-94-C-05020), Washington, DC: National Highway Traffic Safety Administration.
    
    
83. Institute of Transportation Engineers. (2003). Making Intersections Safer: A Toolbox of Engineering Countermeasures to Reduce Red-Light Running (Publication No. IR-115), Washington, DC.
    
    
84. Antonucci, N.D., Hardy, K.K., Slack, K.L., Pfefer, R., and Neuman, T.R. (2004). NCHRP Report 500: Guidance for Implementation of the AASHTO Strategic Highway Safety Plan; Volume 12: A Guide for Reducing Collisions at Signalized Intersections, Washington, DC: Transportation Research Board.
    
    
85. Ferlis, R.A. (2001). “Infrastructure Systems for Intersection Collision Avoidance,” Proceedings of the International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design, 1, 378–382.
    
    
86. Inman, V.W., Davis, G.W., El-Shawarby, I., and Rakha, H. (2008). Test Track and Driving Simulator Evaluations of Warnings to Prevent Right-Angle Crashes at Signalized Intersections (Report No. FHWA-HRT-08-070), Washington, DC: Federal Highway Administration.
    
    
87. Tydlacka, J., Voight, A.P., and Langford III, W.C. (2010). “Operational and Crash Evaluation of Lighted Pavement Marker Stop Bars and LED Outlined Traffic Signal Backplates,” Proceedings of the ITS America 20th Annual Meeting and Exposition (CD-ROM).
    
    
88. Carson, J.L., Tydlacka, J., Gray, L.S., and Voight, A.P. (2008). NCHRP Synthesis 380: Applications of Illuminated, Active, In-Pavement Marker Systems, Washington, DC: Transportation Research Board.
    
    
89. American Association of State Highway and Transportation Officials. (2011). A Policy on Geometric Design of Highways and Streets (6th Ed.), Washington, DC.
    
    
90. Gates, T.J., Noyce, D.A., and Laracuente, L. (2007). “Analysis of Dilemma Zone Driver Behavior at Signalized Intersections (Paper 07-3351),” Transportation Research Board Annual Meeting Compendium of Papers (CD-ROM).
    
    
91. Kiefer, R.J., LeBlanc, D., Palmer, M., Salinger, J., Deering, R., and Shulman, M. (1999). Development and Validation of Functional Definitions and Evaluation Procedures for Collision Warning/Avoidance Systems (Report No. DOT HS 808 964), Washington, DC: National Highway Traffic Safety Administration.
    
    
92. Inman, V.W. and Davis, G.W. (2009). The Effects of In-Vehicle and Infrastructure-Based Collision Warnings at Signalized Intersections (Report No. FHWA-HRT-09-049), Washington, DC: Federal Highway Administration.
    
    
93. McKnight, A.J. and Bahouth, G.T. (2008). “Analysis of Large Truck Rollover Crashes,” Annals of Advances in Automotive Medicine, 52, 281–288.
    
    
94. Chowhurdy, M.A., Warren, D.L., Bissell, H., and Taori, S. (1998). “Are the Criteria for Setting Advisory Speeds on Curves Still Relevant?” ITE Journal, 68(2), 32–45.
    
    
95. Winnett, M.A. and Wheeler, A.H. (2002). Vehicle-Activated Signs: A Large Scale Evaluation (Report No. TRL548), Wokingham, UK: Transportation Research Laboratory.
    
    
96. Pitale, J.T. and Shankwitz, C. (2009). Benefit:Cost Analysis of In-Vehicle Technologies and Infrastructure Modifications as a Means to Prevent Crashes Along Curves and Shoulders (Report No. MN/RC 2009-39), St. Paul, MN: Minnesota Department of Transportation.
    
    
97. Torbic, D.J., Harwood, D.W., Gilmore, D.K., Pfefer, R., Neuman, T.R., Slack, K.L., and Hardy, K.K. (2004). NCHRP Report 500: Guidance for Implementation of the AASHTO Strategic Highway Safety Plan Volume 7: A Guide for Reducing Collisions on Horizontal Curves, Washington, DC: Transportation Research Board.
    
    
98. Janson, B.N. (1999). Evaluation of Downhill Truck Speed Warning System on I-70 West of Eisenhower Tunnel, Denver, CO: Colorado Department of Transportation.
    
    
99. Appelt, V. (2000). “New Approaches to the Assessment of the Spatial Alignment of Rural Roads—Apparent Radii and Visual Distortion,” Proceedings of the 2d International Symposium on Highway Geometric Design. Cologne, Germany: Verlag, 620–631.
    
    
100. Rämä, P. and Kulmala, R. (2000). “Effects of Variable Message Signs for Slippery Road Conditions on Driving Speed and Headways,” Transportation Research Part F, 3, 85–94.
     
     
101. Luoma, J., Rämä, P., Penttinen, M., and Anttila, V. (2000). “Effects of Variable Message Signs for Slippery Road Conditions on Reported Driver Behaviour,” Transportation Research Part F, 3, 75–84.
     
     
102. Drobot, S., Mahoney, W.P., Schuler, E., Wiener, G., Chapman, M., Pisano, P.A., Kennedy, P…. Stern, A.D. (2009). “Intellidrive Road Weather Research and Development: The Vehicle Data Translator,” Proceedings of the Annual Meeting of ITS America, retrieved from http://ops.fhwa.dot.gov/weather/resources/publications/itsapaper9005/itsapaper900.pdf.
     
     
103. Garber, N.J. and Srinivasan, S. (1998). Effectiveness of Changeable Message Signs in Controlling Vehicle Speeds in Work Zones: Phase II Final Report. (Report No. VTRC 98-R10), Charlottesville, VA: Virginia Transportation Research Council.
     
     
104. Goodwin, L. (2003). Best Practices for Road Weather Management, Version 2.0 (Report No. FHWA-OP-03-081), Washington, DC: Federal Highway Administration.
     
     
105. Al-Kaisy, A., Ewan, L., and Veneziano, D. (2012). Evaluation of a Variable Speed Limit System for Wet and Extreme Weather Conditions, Phase 1 Report (Report No. SPR 743), Salem, OR: Oregon Department of Transportation.
     
     
106. Paine, M., Paine, D., Griffiths, M., and Germanos, G. (2007). “In-Vehicle Intelligent Speed Advisory Systems (Paper No. 07-0247),” Proceedings of the 20th International Conference on the Enhanced Safety of Vehicles, Lyon, France.

^[1]Intelligent Transportation Systems Joint Program Office (2015). Web site: http://www.iteris.com/itsarch/html/mp/mpindex.htm, accessed on 7/13/15.