Red light camera interventions for reducing traffic violations and traffic crashes: A systematic review

Abstract

Background: Road traffic crashes are a major and increasing cause of injury and death around the world. In 2015, there were almost 6.3 million motor vehicle traffic crashes in the United States. Of these, approximately 1.7 million (27%) involved some form of injury and 32,166 (0.5%) resulted in one or more fatalities (National Highway Traffic Safety Administration, 2016, Traffic Safety Facts 2013: A Compilation of Motor Vehicle Crash Data from the Fatality Analysis Reporting System and the General Estimates System). The most common cause of urban crashes appears to be drivers running red lights or ignoring other traffic controls and injuries occur in 39% of all of these types of crashes (Insurance Institute for Highway Safety, IIHS, 2018, Red light running). While many drivers obey traffic signals, the possibility for violations exists due to issues such as driver distraction, aggressive driving behaviors, or a deliberate decision to ignore the traffic signal. One researcher suggests that eliminating traffic violations could reduce road injury crashes by up to 40% (Zaal, 1994, Traffic law enforcement: A review of the literature). Red light cameras (RLCs) are an enforcement mechanism that permit police to remotely enforce traffic signals; they may serve as a deterrent to drivers who intentionally engage in red light running (RLR). The one previous systematic review of RLCs found that they were effective in reducing total casualty crashes but also found that evidence on the effectiveness of cameras on red light violations, total crashes, or specific types of casualty crashes was inconclusive. However, this review searched only a small number of electronic databases and was limited to a handful of studies published in 2002 or earlier.

Objectives: This report updates and expands upon the previous Cochrane systematic review of RLCs. The aim of this review is to systematically review and synthesize the available evidence on the effectiveness of RLCs on the incidence of red light violations and the incidence and severity of various types of traffic crashes.

Search methods: This study uses a four-part search strategy that involves: (a) searching 27 online electronic bibliographic databases for published and unpublished evaluations of RLCs; (b) searching the websites of 46 international institutes and research agencies focusing on transportation issues for reports and other gray literature; (c) searching the reference lists of published studies to identify additional published and unpublished works; and (d) conducting a keyword search using Google and Google Scholar to search for additional gray literature.

Selection criteria: The criteria for inclusion were determined before the search process began. To be eligible, studies must have assessed the impact of RLCs on red light violations and/or traffic crashes. Studies must have employed a quantitative research design that involved randomized controlled trials, quasi-random controlled trials, a controlled before-after design, or a controlled interrupted time series. Research that incorporated additional interventions, such as speed cameras or enhanced police enforcement, were excluded, although normal routine traffic enforcement in the nonintervention control condition was not excluded. Both published and unpublished reports were included. Studies were eligible regardless of the country in which they were conducted or the date of publication. Qualitative, observational, or descriptive studies that did not include formal comparisons of treatment and control groups were excluded from this research.

Data collection and analysis: Initial searches produced a total of 5,708 references after duplicates were removed. After title and abstract screening, a total of 121 references remained. Full-text review of these works identified 28 primary studies meeting the inclusion criteria, in addition to the 10 studies identified in the prior Cochrane review. Because several of the primary studies reported on multiple independent study areas, this report evaluates 41 separate analyses. At least two review authors independently assessed all records for eligibility, assessed methodological risk of bias, and extracted data from the full-text reports; disagreements were resolved by discussion with a third review author. To facilitate comparisons between studies, a standardized summary measure based on relative effects, rather than differences in effects, was defined for each outcome. Summary measures were calculated for all studies when possible. When at least three studies reported the same outcome, the results were pooled in a meta-analysis. Pooled meta-analyses were carried out when at least three studies reported the same outcome; otherwise, the results of individual studies were described in a narrative. Heterogeneity among effect estimates was assessed using χ ² tests at a 5% level of significance and quantified using the I ² statistic. EMMIE framework data were coded using the EPPIE Reviewer database.

Results: The results of this systematic review suggest that RLCs are associated with a statistically significant reduction in crash outcomes, although this varies by type of crash, and suggest a reduction in red light violations. RLCs are associated with a a 20% decrease in total injury crashes, a 24% decrease in right angle crashes and a 29% decrease in right angle injury crashes. Conversely, however, RLCs are also associated with a statistically significant increase in rear end crashes of 19%. There was also some evidence that RLCs were associated with a large reduction in crashes due to red light violations. There is no evidence to suggest that study heterogeneity is consistently explained by either country or risk of bias, nor did the presence or absence of warning signs appear to impact the effectiveness of RLCs. Studies accounting for regression to the mean tend to report more moderate decreases for right angle crashes resulting in injury than studies not accounting for regression to the mean. Studies with better control for confounders reported a nonsignificant decrease in right angle crashes, compared with a significant decrease for all studies.

Authors' conclusions: The evidence suggests that RLCs may be effective in reducing red light violations and are likely to be effective in reducing some types of traffic crashes, although they also appear linked to an increase in rear end crashes. Several implications for policymakers and practitioners have emerged from this research. The costs and benefits of RLCs must be considered when implementing RLC programs. The potential benefits of a reduction in traffic violations and in some types of injury crashes must be weighed against the increased risk of other crash types. The economic implications of operating an RLC program also must be considered, including the costs of installation and operation as well as the economic impact of RLC effects.

Figure 1

PRISMA flow diagram

Figure 2

Effects of red light cameras on total crashes. CI, confidence interval; ES, effect size

Figure 3

Effects of red light cameras on total crashes—stratified by country. CI, confidence interval; ES, effect size

Figure 4

Effects of red light cameras on total injury crashes. CI, confidence interval; ES, effect size

Figure 5

Effects of red light cameras on total injury crashes—stratified by country. CI, confidence interval; ES, effect size

Figure 6

Effects of red light cameras on total property damage‐only crashes. CI, confidence interval; ES, effect size

Figure 7

Effects of red light cameras on total crashes from red light running. CI, confidence interval; ES, effect size

Figure 8

Effects of red light cameras on total right angle crashes. CI, confidence interval; ES, effect size

Figure 9

Effects of red light cameras on total right angle crashes—stratified by country. CI, confidence interval; ES, effect size

Figure 10

Effects of red light cameras on right angle injury crashes. CI, confidence interval; ES, effect size

Figure 11

Effects of red light cameras on right angle injury crashes—stratified by country. CI, confidence interval; ES, effect size

Figure 12

Effects of red light cameras on total turning, same roadway crashes. CI, confidence interval; ES, effect size

Figure 13

Effects of red light cameras on total rear end crashes. CI, confidence interval; ES, effect size

Figure 14

Effects of red light cameras on total rear end crashes—stratified by country. CI, confidence interval; ES, effect size

Figure 15

Effects of red light cameras on total rear end injury crashes. CI, confidence interval; ES, effect size

Figure 16

Effects of red light cameras on total rear end injury crashes—stratified by country. CI, confidence interval; ES, effect size

Figure 17

Effects of red light cameras on total rear end crashes from red light running. CI, confidence interval; ES, effect size

Figure 18

Effects of red light cameras on total crashes—stratified by the use of warning signs. CI, confidence interval; ES, effect size

Figure 19

Effects of red light cameras on total injury crashes—stratified by the use of warning signs. CI, confidence interval; ES, effect size

Figure H1

Effects of red light cameras on right angle crashes resulting in injury—stratified by whether or not studies account for regression to the mean (Q = 7.78, p = .01). CI, confidence interval; ES, effect size

Figure H2

Effects of red light cameras on right angle crashes resulting in injury—stratified by whether or not studies were peer reviewed prior to publication (Q = 11.50, p = .001). CI, confidence interval; ES, effect size

Figure H3

Effects of red light cameras on rear end crashes—stratified by risk of bias according to control for confounders (Q = 17.11, p < .001)

Figure H4

Effects of red light cameras on right angle crashes—stratified by risk of bias according to control for confounders (Q = 9.00, p = .003). CI, confidence interval; ES, effect size

Figure H5

Effects of red light cameras on total crashes—stratified by risk of bias according to control for confounders (Q = 0.98, p = .01). CI, confidence interval; ES, effect size

Figure H6

Effects of red light cameras on total crashes—stratified by risk of bias according to control for any other potential sources of bias (including RTM and spillover; Q = 5.03, p = .02). CI, confidence interval; ES, effect size; RTM, regression to the mean