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Federal Highway Administration Research and Technology
Coordinating, Developing, and Delivering Highway Transportation Innovations

This report is an archived publication and may contain dated technical, contact, and link information
Publication Number: FHWA-HRT-05-048
Date: April 2005

Safety Evaluation of Red-Light Cameras

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This study was funded by the Federal Highway Administration and would not have been possible without the enthusiastic cooperation by officials in the study jurisdictions in assembling the database. These jurisdictions include: Baltimore, MD; Charlotte, NC; El Cajon, CA; Howard County, MD; Montgomery, MD; San Diego, CA; and San Francisco, CA. The fundamental research that led to some of the methodological ideas used in the study was funded under an operating grant to Bhagwant Persaud (Ryerson University) from the National Sciences and Engineering Research Council of Canada (NSERC). The photo on the cover of the document is used with permission from TRIMARC/Northrop Grumman Mission Systems.


  1. Retting, R.A., R. Ulmer, and A. Williams. "Prevalence and characteristics of red-light running crashes in the United States." Accident Analysis and Prevention 31:687-94, 1999.
  2. Persaud, B., F. Council, C. Lyon, K. Eccles, and M. Griffith. "A multi-jurisdictional safety evaluation of red-light cameras." Transportation Research Record. Transportation Research Board, in press.
  3. Council, F., B. Persaud, C. Lyon, K. Eccles, M. Griffith, E. Zaloshnja, and T. Miller. "Guidance for implementing red-light camera programs based on an economic analysis of safety benefits." Transportation Research Record. Transportation Research Board, in press.
  4. Hauer, E., Observational Before-After Studies in Road Safety: Estimating the Effect of Highway and Traffic Engineering Measures on Road Safety. Pergamon Press, Elsevier Science Ltd., Oxford, United Kingdom. 1997.
  5. Zaloshnja, E., T. Miller, F. Council, and B. Persaud. "Comprehensive and Human Capital Crash Costs by Maximum Police-Reported Injury Severity within Selected Crash Types." Accepted for presentation at the 2004 Annual Meeting, American Association for Automotive Medicine, Key Biscayne, FL, September 2004.
  6. Council, F., E. Zaloshnja, T. Miller, and B. Persaud. Crash Cost Estimates by Maximum Police-Reported Injury Severity within Selected Crash Geometries. Federal Highway Administration HRT-05-051, U.S. Department of Transportation, Washington, DC. 2005.
  7. National Safety Council. (1990) Manual on Classification of Motor Vehicle Traffic Accidents, Fifth Edition (ANSI D-16.1-1989). Itasca, IL.
  8. Hillier, W., J. Ronczka, and F. Schnerring. An Evaluation of Red Light Cameras in Sydney. Report Number 1/93. Road Traffic Authority NSW: Road Safety Bureau. Rosebery, Australia. 1993.
  9. South, D., W. Harrison, I. Portans, and M. King. Evaluation of the Red Light Camera Program and Owner Onus Legislation. Victoria Transport: Road Traffic Authority. Hawthorn, Victoria. 1988.
  10. Andreassen, D. A Long Term Study of Red Light Cameras and Accidents. Research Report #261. Australian Road Research Board. Victoria, Australia. 1995.
  11. Kent, S., B. Corben, B. Fildes, and D. Dyte. Red Light Running Behaviour at Red Light Camera and Control Intersections. Report #73. Monash University: Accident Research Centre. Clayton, Australia. 1995.
  12. Mann, T., S. Brown, and C. Coxon. Evaluation of the Effects of Installing Red Light Cameras at Selected Adelaide Intersections. Walkerville, Australia: South Australian Department of Transport, Office of Road Safety, Report Number 7/94, 1994.
  13. London Accident Analysis Unit. West London Speed Camera Demonstration Project: An Analysis of Accident and Casualty Data 36 Months "After" Implementation and Comparison with the 36 months "Before" Data. London Research Centre: Environment and Transport Studies. London, England. 1997.
  14. Hooke, A., J. Knox, and D. Portas. Cost Benefit Analysis of Traffic Light and Speed Cameras. Police Research Series Paper 20. Police Research Group, Home Office. London, England. 1996.
  15. Ng, C.H., Y.D. Wong, and K.M. Lum. "The impact of red light surveillance cameras on road safety in Singapore," Road and Transport Research 6(2): 72-80, 1997.
  16. Retting, R. and S. Kyrychenko, Crash Reductions Associated with Red Light Enforcement in Oxnard California. Insurance Institute for Highway Safety, April 2001.
  17. SafeLight. Charlotte. Reviewed from data and reports on its website, http://www.ci.charlotte.nc.us/citransportation/programs/safelight.htm.
  18. Maryland House of Delegates Commerce and Government Matters Committee. "Automated Enforcement Review-Red-Light Running Detection Camera Systems, Howard County, MD." January 18, 2001.
  19. Fleck, J.L., and B.B. Smith. "Can we make red light runners stop? Red light photo enforcement in San Francisco, California," Transportation Research Record 1693, Transportation Research Board, 2001.
  20. Vinzant, Janet C., and B.J. Tatro. Evaluation of the Effects of Photo Radar Speed and Red Light Camera Technologies on Motor Vehicle Crash Rates. Prepared for the City of Mesa Police Department. March 1, 1999. Found at http://www.ci.mesa.az.us/police/traffic/march_1999_report.htm.
  21. Fox, Halcrow, Accidents at Signal Controlled Junctions in Glasgow. Glasgow, Scotland. The Scottish Office Central Research Unit, 1996. (Note that the review was based on the detailed summary posted on the Web site, not the full report.) Found at http://www.scotland.gov.uk/cru/resfinds/drf23-00.htm (accessed 2/25/05).
  22. Winn, Ray. Running the Red and Evaluation of Strathclyde Police's Red Light Camera Initiative. The Scottish Office Central Research Unit: Glasgow, Scotland. 1995. (Note that the review was based on the Web-based report rather than the full report.) Found at http://www.scotland.gov.uk/cru/resfinds/drf7-00.htm.
  23. Retting, R.A., S. Ferguson, and A.S. Hakkert. "Effects of red light cameras on violations and crashes: A review of the international literature." Traffic Injury Prevention, 4:17-23, 2003.
  24. Harwood, D. W., F.M. Council, E. Hauer, W.E. Hughes, and A. Vogt. Prediction of the Expected Safety Performance of Rural Two-Lane Highways. Federal Highway Administration RD-99-207, U.S. Department of Transportation, Washington, DC. 2000.
  25. Lord, D., The Prediction of Accidents on Digital Networks: Characteristics and Issues Related to the Application of Accident Prediction Models. Department of Civil Engineering, University of Toronto, Toronto, Canada. 2000.
  26. Blincoe, L., A. Seay, E. Zaloshnja, T. Miller, E. Romano, S. Luchter, and R. Spicer. The Economic Impact of Motor Vehicle Crashes, Washington, DC: U.S. DOT National Highway Traffic Safety Administration, May 2002.
  27. Miller, T., D. Lestina, M. Galbraith, T. Schlax, P. Mabery, and R. Deering. "United States Passenger-Vehicle Crashes by Crash Geometry: Direct Costs and Other Losses." Accident Analysis and Prevention, Vol. 29, No. 3, May 1997.
  28. Hall, J.W., "Economic benefit of accident reductions," Proceedings of the 68th Annual Meeting of the Institute of Transportation Engineers, Washington, DC: ITE, 1998.
  29. Genstat 5 Committee, Statistics Department, Rothamsted Experimental Station. Genstat 5 Release 3 Reference Manual. R.W. Payne (committee chairman). Clarendon Press, Oxford, United Kingdom, 1993.
  30. National Highway Traffic Safety Administration. National Accident Sampling System Crashworthiness Data System 1999-2001. NHTSA: Washington, DC, 2002.
  31. National Highway Traffic Safety Administration. National Accident Sampling System 1984-1986. NHTSA: Washington, DC, 1987.
  32. National Highway Traffic Safety Administration. (NASS) General Estimates System (GES). NHTSA: Washington, DC. 2004. Found at <http://www-nrd.nhtsa.dot.gov/departments/nrd-30/ncsa/TextVer/GES.html>.
  33. Persaud, B.N., H. McGee, C. Lyon, and D. Lord. "Development of a procedure for estimating the expected safety effects of a contemplated traffic signal installation." Transportation Research Record, 1840, pp. 96-103, 2003.

Other References

Breiman, L., R. Olshen, J. Freidman, and C. Stone. Classification and Decision Trees. Wadsworth International Group, Belmont, CA. 1984.

Butler, Pamela Crenshaw. A Quantifiable Measure of Effectiveness of Red Light Running Cameras at Treatment and Non-Treatment Sites, Washington, DC: Howard University Thesis, May 2001.

Feber, D. J., K.J. Crocker, and J.M. Feldmeier. "The AAA Michigan Road Improvement Demonstration Program: an Analysis of the Effectiveness of Using Safety Enhancements to Help Reduce Societal and Insurance Costs." Transportation Research Board, Washington, DC. 2000.

Hauer E., J. Lovell, and B.N. Persaud, New Directions For Learning About Safety Effectiveness. Federal Highway Administration and National Highway Traffic Safety Administration, Report FHWA/RD‑86‑015, Washington, DC. January 1986.

Hauer, E., and J. Bamfo. Two Tools for Finding What Function Links the Dependent Variable to the Explanatory Variables. International Cooperation on Theories and Concepts in Traffic Safety, Lund, Sweden. 1997.

Maisey, G. The Effect of a Mechanical Surveillance Device on Urban Signalized Intersection Accidents. Research and Statistics Report No. 17. Road and Traffic Authority, Western Australia. 1981.

Makinen, T., and H.L. Oei. Automatic Enforcement of Speed and Red Light Violations: Applications, Experiences and Developments. Report No. R-92-58, SWOV Institute for Road Safety Research, Leidschendam, Holland. 1992.

Persaud, B. N., R.A. Retting, P. Garder, and D. Lord. "Safety Effect of Roundabout Conversions in the U.S.: Empirical Bayes Observational Before-After Study." Transportation Research Record: Journal of the Transportation Research Board, No.1757, Transportation Research Board, National Research Council, Washington, DC. 2001.

Radalj, T., Evaluation of Effectiveness of Red Light Camera Programme in Perth, Main Roads Western Australia (an Australian Governmental agency). Found at http://www.monash.edu.au/cmo/roadsafety/abstracts_and_papers/026/Tony_Radalj_Paper_26_revised.pdf (accessed 2/25/05).

Retting, R.A., J. Chapline, and A. Williams. Changes in Crash Risk Following Re-Timing of Traffic Signal Change Intervals. Insurance Institute for Highway Safety, September 2000.

Zaal, D. Traffic Law Enforcement: A Review of the Literature. Monash University Accident Research Centre, Report No. 53. Clayton, Australia. 1994.

[1] "Regression to the mean" is the statistical tendency for locations chosen because of high crash histories to have lower crash frequencies in subsequent years even without treatment.

[2] Spillover effect is the expected effect of RLCs on intersections other than the ones actually treated because of jurisdiction-wide publicity and the general public's lack of knowledge of where RLCs are installed.

[3] The KABCO severity scale is used by the investigating police officer on the scene to classify injury severity for occupants with five categories: K, killed; A, disabling injury; B, evident injury; C, possible injury; O, no apparent injury. (7) These definitions may vary slightly for different police agencies.

[4] A detailed discussion of "human capital" and "comprehensive" costs can be found in Blincoe, et al.(26) In summary, human capital costs include direct and indirect costs to individuals and society as a whole from the decline in the general health status of those injured in motor vehicle crashes. Components include medical and rehabilitation costs, emergency services costs, lost market productivity, lost household productivity, insurance administration, workplace costs, legal costs, travel delay costs, and property damage costs. Comprehensive costs include all these components plus additional costs associated with intangible consequences of crashes to individuals and families such as pain and suffering and loss of life. In studies of motor vehicle crashes, both types of costs are usually keyed to individual levels of injury severity measured on the AIS within an individual body part (because consequences can vary by severity within body part injured). AIS is specified by trained medical data coders, usually within a hospital context. Average human capital or comprehensive costs are often defined in reports for an eight-point injury scale based on the Maximum AIS score (MAIS) for an individual. Appendix A of Blincoe, et al., shows the average human capital cost ranges from approximately two-thirds of the comprehensive cost for a minor (MAIS 1) injury level to less than one-third the comprehensive cost for a fatality (i.e., $0.98 million versus $3.37 million, in Year 2000 dollars).(26)

[5] The KABCO severity scale is used by the investigating police officer on the scene to classify injury severity for occupants with five categories: K, killed; A, disabling injury; B, evident injury; C, possible injury; O, no apparent injury.(7) These definitions may vary slightly for different police agencies.

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