Automated Red-Light Enforcement
The first red-light camera bill was signed in New York City in 1993 after several years of testing (Retting, 2010). Since then, many states and local jurisdictions have adopted red-light cameras, known along with speed cameras as automated enforcement.
At intersections with traffic lights, automated cameras take photographs of vehicles entering the intersection on a red light. Citations are sent to the vehicle’s registered owner. [FHWA’s] Red-Light Camera Systems Operational Guidelines (FHWA, 2005) provides information on red-light camera program costs, effectiveness, implementation, and other issues. Maccubbin, Staples, and Salwin (2001) provide more detailed information on programs operating in 2001. (UNC Highway Safety Research Center, 2011, p. 3-12)
Red-light cameras are used extensively in other industrialized countries. . . . [As of December 2011,] [a]ccording to the Insurance Institute for Highway Safety, red-light cameras are used in nearly 500 United States communities in 25 States and the District of Columbia. . . . Information on States’ laws authorizing or restricting use of automated enforcement is provided by the GHSA ([2014c]) and by IIHS ([2014b]). (UNC Highway Safety Research Center, 2011, p. 3-12)
The effectiveness of red-light camera programs has been a source of controversy in the research community. The methodologies used to assess effectiveness have varied, as have the conclusions drawn from different studies.
In one review of the literature, the UNC Highway Safety Research Center, 2011, p. 3-12, concluded that red-light cameras increase rear-end crashes, reduce side-impact crashes (the target [crash type]), and reduce overall crash severity40 ([Aeron-Thomas and Hess, 2005]; [Decina, Thomas, et al., 2007]; [Maccubbin, Staples, and Salwin, 2001]; [McGee and Eccles, 2003]; [Retting, Ferguson, and Hakkert, 2003]; [Peden et al., 2004]). Because there tend to be increases in lower-severity rear end crashes that somewhat offset reductions in the target group of higher-severity [right-angle] crashes, cameras were found to be more beneficial at intersections with a higher ratio of angle crashes to rear-end crashes. Intersections with high total volumes, higher entering volumes on the main road, short signal cycle lengths, protected left turn phases, and higher publicity may also increase the aggregate cost benefits of red light camera enforcement ([Council et al., 2005]).
Several additional studies also found positive results in red-light camera studies. Hu, McCartt, and Teoh, 2011, analyzed data on fatal crashes from 14 large U.S. cities with red-light camera enforcement programs and 48 cities without camera programs for the years 1992–1996 and 2004–2008. The average annual citywide rate of fatal red-light–running crashes declined for both groups, but the rate for cities with camera enforcement declined more (35 percent versus 14 percent). During 2004–2008, the rate of fatal red light running crashes citywide and the rate of all fatal crashes at signalized intersections were 24 percent and 17 percent lower, respectively, than what would have been expected without cameras. By examining citywide crash rates for cities with camera programs and using similar control cities, the study accounted for two common weaknesses of red-light camera research: regression to the mean and spillover effect.
Another study focused on red-light citations at the intersection with the highest incidence of traffic crashes in Louisiana following the installation of red-light cameras (Wahl et al., 2010). Over the eight-month study period, the researchers found a significant and sustained reduction in the mean number of citations per week (from 2,428 violations per week to 356 citations per week) and a nonsignificant reduction in collisions (122 to 97, p = 0.18) at the one intersection.
Two other studies also found positive results, although their research designs were not as strong. Newman, 2010, presented findings at the Institute of Transportation Engineers (ITE) 2010 Annual Meeting and Exhibit on the effectiveness of 16 red-light cameras at the busiest intersections in Springfield, Missouri. Following an extensive public education campaign and the installation of the cameras, there was a 20.5-percent reduction in right-angle collisions at photo-enforced signals. There was also an 11.4-percent increase in the number of rear-end crashes, although this increase was not as large as the 15.8-percent increase at the citywide level. Matched control intersections were not used in this study. A thesis from a James A. Baker III Institute research project using seven years of data from 50 intersections in Houston, Texas, concluded that red-light cameras reduced the monthly number of collisions by approximately 28 percent at intersections with a single camera. Installing two cameras per intersection resulted in reductions in collisions coming from all directions, even the two approaches that were not monitored with cameras (Loftis, Ksiazkiewicz, and Stein, 2011).
Other research has found effects in the opposite direction. Burkey and Obeng’s, 2004, analysis of 303 intersections in Greensboro, North Carolina, over a 57-month period found a 40-percent increase in total crash rates resulting from increases in the number of rear-end crashes, sideswipes, and collisions involving cars turning left on the same roadway. They found no decrease in severe crashes (those that included fatal, disabling, and nondisabling injuries) and a 40- to 50-percent increase in possible-injury crashes (those reported in police records as possibly causing injury). Another study examined seven years of data from camera programs in five jurisdictions in Virginia and found a significant 18-percent increase in injury crashes (Garber et al., 2007). In addition, another group of researchers replicated a frequently cited study by Retting and Kyrychenko, 2002, found no significant effect at the p = 0.05 level, and concluded the original authors had incorrectly reported a reduction in crashes after the installation of red-light cameras (Large, Orban, and Pracht, 2008).
A recent meta-analysis found favorable results for red-light cameras only in studies with weaker research designs (Erke, Goldenbeld, and Vaa, 2009). Results of the meta-analysis showed a 15-percent increase in total crashes, a 40-percent increase in rear-end collisions, and a 10-percent decrease in right-angle crashes, although none of these results was significant. The author concluded that red-light cameras may have limited effectiveness; however, others have countered that their analyses overweighted non–peer-reviewed studies (Lund, Kyrychenko, and Retting, 2009).
The studies reviewed used a variety of methodologies, data sources, time periods, comparisons, and metrics to reach their conclusions, so it is difficult to compare them directly. However, it does seem that it is premature to conclude that red-light cameras have been widely found to be highly effective.
Effectiveness of red-light cameras can be measured in a variety of ways. Common measures include the number or rate of collisions, right-angle crashes, and red-light violations at monitored intersections, as well as measures of crash severity. Studies have also used the number of red-light–running citations as a metric.
Costs will be based on equipment choices, operational and administrative characteristics of the program, and arrangements with contractors. Cameras may be purchased, leased, or installed and maintained by contractors for a negotiated fee ([FHWA and NHTSA, 2008]). In 2001, [35-mm wet-film] red-light cameras cost about $50,000 to $60,000 to purchase and $25,000 to install. Monthly operating costs were about $5,000 [per camera system] ([Maccubbin, Staples, and Salwin, 2001]). (UNC Highway Safety Research Center, 2011, p. 3-13)
A standard digital camera system costs $100,000 for the equipment and installation; information on operating costs for the digital system was not reported (Maccubbin, Staples, and Salwin, 2001).
Most jurisdictions contract with private vendors to install and maintain the cameras and use a substantial portion of the income from red-light citations to cover program costs. Speed camera costs probably are similar. (UNC Highway Safety Research Center, 2011, pp. 3-13–3-14)
However, most red-light cameras and speed cameras are separate systems; one camera does not enforce both violations.
Time to Implement
Once any necessary legislation is enacted, automated enforcement programs generally require four to six months to plan, publicize, and implement.
Many jurisdictions using automated enforcement are in States with laws authorizing its use. Some States permit automated enforcement without a specific State law. A few States prohibit or restrict some forms of automated enforcement ([GHSA, 2014c]; [IIHS, 2014b]). See NCUTLO [National Committee on Uniform Traffic Laws and Ordinances] (2004) for a model automated enforcement law. (UNC Highway Safety Research Center, 2011, p. 3-14)
Public surveys typically show strong support for red-light cameras and somewhat weaker support for speed cameras ([IIHS, 2014a]; NHTSA, 2004). Support appears highest in jurisdictions that have implemented red-light or speed cameras. However, efforts to institute automated enforcement often are opposed by people who believe that speed or red-light cameras intrude on individual privacy or are an inappropriate extension of law enforcement authority. They also may be opposed if they are viewed as revenue generators rather than methods for improving safety. Per citation payment arrangements to private contractors should be avoided to reduce the appearance of conflicts of interest (FHWA, 2005). (UNC Highway Safety Research Center, 2011, p. 3-14)
Although a recent report by Madsen and Baxandall, 2011, noted that such practices are less common, contracts may still link revenue to citations through a predetermined proportion of revenue; a variable proportion of revenues based on timeliness of fine collection, quotas, and volume-based payments; and surcharges from fine alternatives, such as traffic school.
“State courts have consistently supported the constitutionality of automated enforcement” (UNC Highway Safety Research Center, 2011, p. 3-14).
“More research is needed to shed light on spillover effects (positive or negative) of automated enforcement programs” (UNC Highway Safety Research Center, 2011, p. 3-14). In addition, drivers may start to avoid monitored intersections and increase traffic on neighboring streets.
This UNC report forms the basis of our series of fact sheets. Although, for most fact sheets, our team has relied on UNC’s overall effectiveness assessment, for this intervention, we have located studies that appear to contradict the UNC ratings. Automated enforcement received five of five stars, indicating “demonstrated to be effective by several high-quality evaluations with consistent results.”
Originally published by CDC.gov