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

Deviations from protocol

Although every effort was made to comply with the original protocol, some changes were deemed to be essential. These primarily occurred because the original protocol was created by the researchers at FIU, before the FIU/LSHTM collaboration developed. After this collaboration began, some modifications to the protocol were necessary to conform to the requirements of the grant that the LSHTM researchers received from the UK College of Policing. Following is a list of deviations from the protocol: 1.

The original FIU protocol included a requirement that studies must collect data for a minimum of one year to be eligible for inclusion in the review. This requirement was removed at the request of the LSHTM researchers. However, the majority of the included studies still meet the original requirement.

Types of studies

To be eligible for inclusion, a study must have measured the effectiveness of RLCs by comparing intersections that received the treatment (the treatment condition) with intersections that did not (the control condition). We identified studies were eligible for inclusion if they involved one of the following research designs, as they were defined in the original review (based on the Cochrane Effective Practice and Organisation of Care group): 1.

Experimental design/RCT: This category included studies that used random assignment to assign intersections to the treatment and control groups.

Qualitative, observational, or descriptive studies that did not include formal comparisons of treatment and control groups were excluded from this research.

For all research designs, the nonintervention control condition did not exclude normal routine traffic enforcement by criminal justice system personnel. Police still could issue citations for traffic violations at control intersections during the study period.

Types of participants

RLC studies do not have participants in the standard sense. Essentially, the participants were signalized intersections in the area under study. Intersections where additional interventions were in operation, such as speed cameras or enhanced police enforcement, were excluded. For treatment intersections, RLC enforcement must have applied to all motorists passing through the intersections where the cameras were installed.

Types of interventions

Eligible studies must have tested the effect of RLCs on traffic red light violations or crashes. An RLC is considered to be a digital or film still and/or video camera that is used to detect red light violators and identify them so that they may be charged with their violations. Studies that examined RLCs as part of a larger traffic enforcement initiative, specifically those that examined the joint effect of red light and speed cameras, were excluded.

Studies were included when the interventions included cameras at intersections or junctions that were designed to detect red light violators. Studies examining area-wide programs where RLCs operated at some but not all signalized intersections in the community were also included.

Types of outcome measures

Eligible studies had to have measured and reported data on at least one of the following outcome measures:

Red light violations, based on the number of vehicles passing through a junction after entering on a red light. Vehicles that enter a junction on a yellow/amber light but do not clear the intersection before the light changes to red are not considered to be violators.

Data on economic outcomes, including the costs of providing the intervention and the income it generated, and process outcomes (e.g., implementation data) were also collected when available.

Types of settings

Studies were eligible regardless of the country in which they were conducted or the form in which they were published. When studies were not published in English, efforts were made to obtain translations. There was only one possibly-eligible study for which this was not possible (Giæver & Tveit, 1998).

No date restrictions were placed on this study. However, RLCs have only been used for traffic enforcement since the 1960s (Retting, Ferguson, & Hakkert, 2003); therefore, no research prior to that time could have been conducted.

Search strategy

A four-part search strategy was used to locate research meeting the criteria for eligibility: 1.

Online electronic bibliographic databases were searched for published evaluations of RLCs (see Appendix A for a list of electronic databases).

Electronic searches

For all agency website and gray literature searches, the following keywords were used:

Red AND light AND camera(s)

These were adapted when necessary to meet specific requirements of individual search engines or to conform to international terminology variations and spelling conventions. Search terms were intentionally general in nature to ensure that searches cast the broadest possible net and that relevant background material was also identified.

All electronic database searches were conducted using the full search strategy as outlined in Appendix C and only superficially adapted for each database. Additional specialized searching was conducted by the College of Policing's National Police Library.

Screening and review process

All studies identified through the LSHTM and National Police Library search process were first exported to the EndNote bibliographic database for de-duplication. Once duplicate records had been removed, records were combined with FIU search results in a spreadsheet to identify and remove further duplications. Once duplicate records were removed, each study was screened to determine if it met basic inclusion criteria, specifically: 1.

The study dealt with the use of RLCs to reduce/prevent traffic light violations and/or traffic crashes.

At least two review authors independently examined the titles, abstracts, and keywords of electronic records for eligibility according to the inclusion criteria above. Results of this initial screening were cross-referenced between the authors and full-texts were then obtained for all potentially relevant reports of studies. The publication status of the study (unpublished vs. published) did not affect study eligibility.

The full-text reports of potentially eligible studies were independently assessed for final inclusion in the review by two review authors using screening codes in EPPI Reviewer 4. Any disagreements were resolved by discussion with a third review author. Reference lists of all eligible trials were inspected for further eligible studies.

Data extraction and management

All studies were managed using the EPPI Reviewer 4 software. For the EMMIE framework data extraction, data were coded independently by two review authors in EPPI Reviewer, using a standardized data coding set (see Appendix D: EPPI Reviewer standardized data coding set for EMMIE framework). The remaining coding of studies (including study characteristics, risk of bias, measurement of effect) was conducted using Microsoft Excel.

Details of study coding categories

All eligible studies were coded on a variety of criteria, including details related to the source of the study (publication source, title, authors, etc.), study characteristics (methodological type, dates of data collection, etc.), sample characteristics (size, location, etc.) study methods and procedures (selection process, characteristics of treatment and control areas, associated publicity campaigns, etc.), descriptions of the independent and dependent variables (construct, operationalization, etc.), effect size data (if any), adjustment for bias (regression to the mean [RTM], spillover/diffusion, etc.), and study conclusions.

Every eligible study was coded by two review authors, using a standardized data extraction instrument (see Appendix E). All disagreements were identified and resolved by discussion with a third review author.

Descriptive analyses

The review includes all studies meeting the inclusion criteria. Descriptive statistics extracted from each study included:

Study design: This describes study design; risk of bias; data collection methods, types of statistical analyses

Statistical analyses

To facilitate comparisons of studies, a standardized summary measure was defined for each outcome. Summary measures were based on relative effects, rather than differences in effects, where the outcome after intervention was divided by the outcome before intervention, as an expression of the proportional change in outcome. Summary measures were calculated for all studies where possible (i.e., where required information was reported or adequate data were available for the calculation).

Rate ratios were estimated by dividing the count of the outcome post- and preintervention in the intervention area by the corresponding ratio in the control area. For example, the estimated rate ratio for road traffic collisions was:

c ollisions after / c ollisions before in intervention area c ollisions after / c ollisions before in control area .

Assuming that traffic volume remains the same on the roads postintervention in the control and intervention areas, this rate ratio estimates the change in the collision rate in intervention areas compared to that in control areas. For outcomes expressed as counts or rates the intervention effect was estimated using rate ratios with a 95% confidence interval (CI), calculated assuming a Poisson distribution for the number of collisions in each area and time period (however, the adequacy of the fit of the Poisson distribution was not assessed). Where studies provided Empirical Bayes estimates, these were extracted directly from the primary studies with the standard errors (SEs), and included in the meta analysis. Similarly for ITS, the coefficient and the SE from studies were extracted and used to generate rate ratios and CIs. Studies from which SEs could not be extracted (or from which SEs were incalculable) were excluded from the final statistical analyses, and the results were described and compared where appropriate.

Data synthesis

Results were pooled in a meta-analysis where three or more studies reported the same outcome (Cooper, 2003). The logarithm of the rate ratio and its SE (calculated assuming a Poisson distribution for the number of collisions in each area and time period) were pooled. If there were too few studies for a meta-analysis, the results of individual studies were described in a narrative.

Heterogeneity (variability) among the effect estimates was assessed using a χ ² test at a 5% significance level and quantified using the I ² statistic, the percentage of between-study variability that is due to true differences between studies (heterogeneity) rather than due than to sampling error. An I ² value greater than 50% was considered to reflect substantial heterogeneity. Substantial heterogeneity would mean that the results of different studies vary substantially more than would be expected if the effects of RLCs were the same in each setting. Where substantial variation in results was identified, subgroup analyses were conducted in an attempt to investigate the source. Stratification of the outcome data was only possible where sufficient information on studies was available for grouping. Subgroup analysis was conducted on the basis of risk of bias and by country (United States, Australia, or other). When assessing for differences of effect by subgroups, visual inspection of the forest plots was made and consideration to the widths of the CIs for each subgroup was given. Where the CIs of summary estimates for subgroups do not overlap, or do so very little, a difference in effect between the groups may be indicated, and follow-up tests of group difference were conducted. Details of each intervention are presented in tables of the characteristics of studies (Appendix F). Stata statistical software (version 14.2) was used to conduct the meta-analyses.

Assessment of risk of bias in included studies

Results of the search

The search strategy produced a total of 5,708 records after duplicates were removed. Title and abstract screening resulted in the exclusion of 5,587 records, leaving a total of 121 references that were potentially eligible for inclusion in this study. Full-text review of these works identified 28 primary studies that met the inclusion criteria, in addition to the 10 studies that had been identified in the prior Cochrane review. Eight of these newly identified studies (and one study from the previous review) had associated publications, which were subsumed in the primary studies (Cunningham & Hummer, 2010; Fitzsimmons, 2007a, 2007b, 2007c; Garber, Miller, Abel, Eslambolchi, & Korukonda, 2007; Hallmark, Orellana, McDonald, Fitzsimmons, & Matulac, 2010; Miller, Khandelwal, & Garber, 2006; Persaud, Council, Lyon, Eccles, & Griffith, 2005; Sayed & de Leur, 2007; Shin & Washington, 2007; Retting & Kyrychenko, 2002 from the previous review). Two of the newly identified studies reported on more than one independent study area: Fitzsimmons (2007a,b,c) and Hallmark et al. (2010) included separate analyses of RLCs in two cities in Iowa (Council Bluffs and Davenport) and Shin and Washington (2007) evaluated independent programs in two cities in Arizona (Phoenix and Scottsdale). Kloeden, Edwards, and McLean (2009) evaluated two RLC programs in Adelaide that occurred at different times (1988 and 2001).

The full-text reports for the 28 newly identified studies (along with their subsumed publications) were coded in detail in EPPI Reviewer and Excel for the EMMIE analysis and are included in the tables of characteristics of included studies (Appendix F). The previous Cochrane review (Aeron-Thomas & Hess, 2005) applied a naming convention combining authors and study location to the 10 included studies. For the additional studies identified in this updated review, first author names and years of publication have been retained to identify each study, with a city or year suffix where required. From the 28 additional primary studies, there were 31 separate analyses evaluated in this review.

The search process is diagrammed in Figure 1, which also shows the number of records excluded, with a summary of reasons. Further details of some of the excluded studies are also available in Appendix F.

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Figure 1

PRISMA flow diagram

Description of included studies

A total of 38 studies were included in this analysis. Of these, 37 were CBA studies with a distinct control group (27 newly identified studies plus 10 previously identified in the prior Cochrane review). One of the newly identified studies employed a controlled ITS design. No RCTs were found. See Appendix F for information on the studies included in and excluded from this review. The characteristics of the studies in the previously published Cochrane systematic review are not duplicated in this report (for copyright reasons).

Like the previous review, the majority of the newly identified studies were from the United States (20) and Australia (5); the remainder were from Canada (2) and Singapore (1). Fifteen of the newly identified studies were published in academic journals (although seven also had other associated publications); the rest were published as technical reports and/or Master's theses. The publication dates of the newly identified studies ranged from 1981 to 2016 inclusively; two studies (Andreassen, 1995; Maisey, 1981) included data from the 1970s. None of the original or newly identified studies distinguished between types of motorists; any vehicle passing through an intersection under study was included in the analyses. The methods of analysis used by study authors varied greatly. Most studies used simple ratios (as we have proposed in this review), while 12 included Empirical Bayes analyses.

The outcome measures from the 38 primary studies covered a range of definitions and measures of red light violations, crashes, and injuries. Only three studies reported red light violations and these studies reported no other outcomes (Arup Transportation Planning, 1992; Chin, 1989; Retting, Williams,, Farmer, & Feldman, 1999a). The rest examined various types of crashes. Most studies reported crash outcomes by crash type and collected crash statistics from official databases of police reports. The types of crashes examined included total crashes (all types combined), rear end crashes, and right angle crashes (or similar outcomes such as turning same roadway crashes; turning different roadway crashes; angle crashes; and turning and right angle crashes) In many studies, crash types were further disaggregated into total number of crashes of a given type (including those involving PDO), injury crashes, and RLR only crashes (identified through police reports where a red light violation occurred).

Fourteen of the newly identified studies reported crash outcomes resulting in injury to passengers or other road users (AECOM Canada, Ltd., 2014; Ahmed & Abdel-Aty, 2015; Burkey & Obeng, 2004; City of Lubbock, 2008; Cunningham & Hummer, 2010; Garber, 2007; Kloeden et al., 2009; Kull, 2014; Llau, Ahmed, Khan, Cevallos, & Pekovic, 2015; Maisey, 1981; Miller et al., 2006; Sayed & de Leur, 2007; Sharpnack, 2009) as did five of the previously identified studies (Hillier, Ronczka, & Schnerring, 1993; Mann, Brown, & Coxon, 1994; Ng, Wong, & Lum, 1997; Retting & Kyrychenko, 2002; South, Harrison, Portans, & King, 1988). In most cases, injury crashes included total injuries and fatalities. However, Llau et al. (2015) included “possible injuries” in the injury crash counts while Persaud et al. (2005) specifically excluded crashes classified as “possible injury” from the injury class counts. Burkey and Obeng (2004) examined injury and possible injury crashes separately.

Eight of the included studies reported results from which SEs could not be calculated: seven newly identified studies (Andreassen 1995; Chin 1989; Kull 2014; Pulugurtha & Otturu, 2014; Richardson 2003; Ross & Sperley, 2011; Sayed & de Leur, 2007) and one original study (South et al., 1988). Seven of these studies were excluded from all final meta-analyses. South et al. (1988) was included in the final meta-analyses for crash types where SEs could be calculated (total injury crashes, right angle injury crashes and rear end injury crashes) and was excluded from the crash type where this was not possible (turning, same roadway injury crashes).

A summary of outcome measures covered by the current and previous reviews is provided in Table 1.

Total crashes

Total crashes

Twenty-four studies reported 27 estimates of effect on the total number of all types of crashes (including PDO). Twenty of these studies (accounting for 23 estimates of effect) reported these estimates with CIs or SEs.

The effects of RLCs on the total number of crashes at intersections were highly heterogeneous (I ² = 90.4%, Q = 228.4, and df = 22, p < .001). The direction of the estimated effects was also inconsistent. Figure 2 shows the effect sizes (ES) and 95% CI for each individual study. When the CI overlaps the reference line (a vertical line located at one on the x-axis), this indicates that the observed effects of RLCs may be due to chance. An overall estimate of the relative change in total crashes at locations with RLCs compared with control sites is represented by a diamond shape at the bottom of Figure 2. This pooled effect estimate of 0.98 indicates a 2% reduction in total crashes. However, as the 95% CI crosses the reference line, ranging from a 9% reduction to a 7% increase in total crashes, the effect is uncertain.

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Figure 2

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

Four newly identified studies (accounting for four estimates of effect) reported results from which SEs could not be obtained (Pulugurtha & Otturu, 2014; Richardson 2003; Ross & Sperley, 2011; Sayed & de Leur, 2007). Pulugurtha and Otturu (2014) reported an EB analysis that found the RLC program was not effective in reducing crashes at the majority of selected signalized intersections. This study found some evidence that the RLCs were more effective at reducing total crashes at intersections with less than 40,000 vehicles entering per day, but reported more uncertain and marginal effects at intersections with greater than 40,000 vehicles per day. Using EB analysis to estimate the effect on total crashes, Richardson (2003) reported a reduction of 0.65 crashes per site per year as a result of RLC installation which was equated to an 11.7% reduction in total crashes per year. A before and after study conducted on a single RLC camera site (Ross & Sperley, 2011) reported an increase in total crashes after camera installation, around 77% increase in average number of total crashes per month. Sayed and de Leur (2007) conducted an EB analysis, reporting reductions in total crashes at 20 of the 25 locations after RLC implementation and a reduction in total crashes of 11.1%. These results are aligned with the meta-analysis which found the effects on total crashes to be heterogeneous, and inconsistent in the estimated direction of effect. Some of these additional studies highlighted variation within crash and intersection types impacting the effect of RLCs.

Total crashes stratified by country

As shown in Figure 3, the subgroup analysis by country indicates uncertain effects of RLCs on total crashes in both Australia (2% decrease; 95% CI; 6% decrease 7% increase) and the United States (1% decrease; 95% CI; 11% decrease–9% increase). In both cases, the CIs overlap the reference line and a further test of group differences indicates no evidence of difference in the subgroup effects (Q = 0.10, p = .76). In Australia, the effect estimates across studies are consistent (I ² = 0%, p < .001) while in the United States, there was much greater heterogeneity (I ² = 86.3%, p < .001).

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Figure 3

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

Total injury crashes

Seventeen studies examined injury crashes, providing 18 estimates of effect. Fifteen of these studies (16 estimates) reported CIs. Figure 4 shows that the estimates of effect on total injury crashes were also highly heterogeneous (I ² = 93.1%, p < .001). The overall pooled estimate of effect suggests that RLCs reduced total injury crashes by 20% with a 95% CI (32–5% decrease). In Llau et al. (2015), the number of injury crashes specifically included possible injuries; this distinction was not made for other studies.

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Figure 4

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

Two newly identified studies (Kull, 2014; Sayed & de Leur, 2007) reported results from which SEs could not be obtained, but reported reductions similar to studies in the meta-analysis. Kull (2014) reported a reduction in the rate of all injury crashes of 0.48 per intersection per year, while Sayed and de Leur (2007) reported reductions in injury crashes of 6.1% after RLC implementation.

Several other studies used different criteria to measure injuries and therefore are not included in the pooled analysis. Burkey and Obeng (2004) reported increases in crashes resulting in severe injuries (10% increase; 95 CI %; 20% decrease–50% increase), and increases for possible injuries (50% increase; 95% CI; 30–70% increase). Hu, McCartt and Teoh (2011) reported a reduction in all fatal crashes at RLC intersections per million population of 17% (95% CI; 30–0% decrease). Richardson (2003) reported a 29.2% decrease in the number of people injured at red light intersections per year (CI incalculable).

Total injury crashes stratified by country

As seen in Figure 5, a meta-analysis of studies from the United States reported a decrease in injury crashes of 19%, although the effect was uncertain (95% CI; 40% decrease–9% increase) and there was evidence of significant heterogeneity (I ² = 94.3%, p < 0.001). A meta-analysis of studies from Australia reported a decrease in injury crashes of 15% (95% CI; 23–6% decrease) with no evidence of heterogeneity (I ² = 4.8%, p < .001). The test of group differences indicates no evidence against the null hypothesis that the subgroup effects are the same (Q = 0.11, p = .74).

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Figure 5

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

Total PDO crashes

Seven studies examined PDO crashes, six of which included CIs or SEs. These are shown in Figure 6. Overall, there was a nonsignificant increase of 5% in PDO crashes (95% CI; 8% decrease–20% increase). It was not possible to obtain SEs from one study (Sayed & de Leur, 2007), and this reported reductions in PDO crashes of 14.3% after RLC implementation.

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Figure 6

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

Total crashes from RLR

Six studies looked specifically at RLR crashes (producing seven estimates of effect). RLR crashes were identified as those caused directly by a driver running a red light or failing to yield during a turn on red, or any crash where a red light violation ticket was issued. The pooled estimates showed a 47% overall reduction in the total number of RLR crashes, although this was nonsignificant (95% CI; 62% decrease–2% increase; I ² = 98.4%; p < .001).

Figure 7 identifies Fitzsimmons' Council Bluffs study (2007a, 2007b, 2007c) and Hallmark et al. (2010) as a visual outlier due to the extremely large reductions in RLR crashes found in this study. As a sensitivity analysis, the meta-analysis was repeated excluding this study; the direction of the overall estimate of effect was unchanged but the estimated reduction in crashes dropped by over half to 18%, which was now significant (95% CI; 25–11% decrease). Removing this study also reduced heterogeneity (I ² = 14.9%; p = .320). When City of Lubbock (2008) was also removed from the pooled analysis, RLR crashes were significantly reduced by 19% (95% CI; 25–11% decrease) and heterogeneity remained low (I ² = 17.5%, p = .304).

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Figure 7

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

Two further studies reported estimates of effect of RLCs on road traffic crashes from RLR. However, these limited “total” crashes to only specific types, rather than all crashes due to RLR. Therefore, they were not considered comparable and were excluded from the pooled analysis. Andreassen (1995) reported a 7% increase in total RLR crashes, which were defined as the sum of pedestrian, right angle, turning (same roadway) and rear end crashes. Cunningham and Hummer (2010) similarly reported a 5% increase in RLR crashes, which were defined as the sum of turning (both same roadway and different roadways), right angle and rear end crashes.

Right angle crashes

Total right angle crashes

Fifteen studies evaluated the effect of RLCs on total right angle crashes (including property only crashes), providing 17 estimates of effect. Twelve of these studies (14 estimates of effect) included CIs. Garber (2007) and Persaud et al. (2005) provided both overall estimates of effect and estimates broken down by jurisdiction; the combined overall effects reported in each of these studies have been included in the pooled analysis as it was considered that the different geographic regions would not be independent. Figure 8 indicates a significant reduction in right angle crashes of 24% at intersections with RLCs compared with those without (95% CI; 45–10% decrease) with evidence of heterogeneity between studies (I ² = 94%, p < .001).

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Figure 8

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

Three newly identified studies reported results from which SEs were incalculable (Andreassen, 1995; Richardson, 2003; Sayed & de Leur, 2007). Andreassen (1995) reported mixed findings with some sites showing an initial decrease in crashes in years following RLC installation, and sites with fewer crashes reporting increased crashes in later years. Using an EB analysis, Richardson (2003) reported an increase of 0.07 crashes per site per year as a result of RLC installation which was equated to an increase of 6% in right angle crashes per year. Sayed and de Leur (2007) reported reductions in right angle crashes of 17.2% after RLC implementation.

Total right angle crashes stratified by country

As shown in Figure 9, a subgroup analysis by country suggested a nonsignificant decrease of 13% in right angle crashes in the United States (95% CI; 27% decrease–4% increase) and 37% in Australia (95% CI; 64% decrease–9% increase). There was evidence of significant heterogeneity in both countries (I ² = 91.4%, p < .001 in the United States and I ² = 94%, p < .001 in Australia) and no evidence of a difference in effect between subgroups (Q = 1.21, p = .27).

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Figure 9

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

Right angle injury crashes

Seven studies reported eight estimates of effect; one of these studies (Kull, 2014) reported effect estimates without CIs. The pooled analysis of studies with reported CIs, which is shown in Figure 10, estimates a significant overall reduction of 29% in right angle injury crashes (95% CI: 42–14% decrease), with moderate evidence of heterogeneity (I ² = 59.1%, p = .023).

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Figure 10

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

Right angle injury crashes stratified by country

There were not enough studies to estimate the pooled effect of RLCs on right angle crashes in the USA. In Australia, a pooled estimate indicated a significant 43% decrease (95% CI: 53–32% decrease) with no evidence of heterogeneity (I ² = 0%, p = .706), as shown in Figure 11.

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Figure 11

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

Other turning crashes

A number of studies examined different crash types, such as turning, same roadway crashes (where cars approaching from opposite directions on the same road crash when one turns across the path of the other); turning, different roadway crashes; total direct and indirect right angle crashes; and the sum of all turning and right angle crashes at intersections. Most of these outcomes were unique to individual studies and could not be compared to other reported outcomes; however, a few studies reported turning, same roadway crashes. These have been pooled to give an overall estimate of effect.

Total turning, same roadway crashes

Five studies reported turning, same roadway crashes as an outcome with seven estimates of effect. Two of these studies reported effect estimates without CIs (Andreassen, 1995; Richardson, 2003).

The pooled analysis, shown in Figure 12, estimates no effect of RLCs on turning, same roadway crashes (95% CI: 28% decrease–40% increase), with substantial heterogeneity (I ² = 94.1%, p < .001). The two studies without CI/SE Using before and after crash rates, Andreassen (1995) found a marginal increase in turning same roadway crashes after RLC implementation, while Richardson (2003) reported a decrease of 0.04 crashes per site per year as a result of RLC installation which was equated to a decrease of 2.4% in turning same roadway crashes per year.

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Figure 12

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

Rear end crashes

Total rear end crashes

Nineteen studies evaluated the effect of RLCs on total rear end crashes, reporting 21 estimates of effect. Sixteen of these studies (18 estimates of effect) reported CI/SE. Overall, these 16 studies showed significant evidence for an increase of 19% in rear end crashes with RLCs (95% CI: 9–31% increase). This is shown in Figure 13.

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Figure 13

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

Three studies were found where SE/CIs could not be obtained. Andreassen (1995) reported a 20% increase at RLC sites in comparison to control intersections. Using an EB analysis, Richardson (2003) reported an increase of 0.16 rear end crashes per site per year as a result of the installation of RLCs, this equated to a 9.2% increase per year. Sayed and de Leur (2007) reported a reduction in rear end crashes after RLC implementation of 12.4%.

Total rear end crashes stratified by country

A meta-analysis of studies in the United States found a significant 22% increase in rear end crashes (95% CI: 8–39% increase), with evidence of heterogeneity (I ² = 88.1%, p < .001). Pooled estimates from Australia indicated a significant increase of 10% (95% CI: 0–22% increase), with no evidence of heterogeneity (I ² = 0%, p = .622). This is shown in Figure 14. The test of group differences indicates no evidence against the null hypothesis that the subgroup effects are the same (Q = 1.52, p = .22).

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Figure 14

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

Total rear end injury crashes

Eight studies (nine estimates of effect) examined total rear end crashes that resulted in injury; of these seven studies reported eight estimates of effect with CI/SE. The pooled analyses of these eight estimates, which is shown in Figure 15, suggested a nonsignificant decrease in rear end injury crashes of 1% (95% CI: 20% decrease–24% increase) and moderate heterogeneity (I ² = 55.5%, p = .028).

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Figure 15

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

Using annual rear end injury crash rates, Kull (2014) found an increase of 17% following RLC installation.

Rear end injury crashes stratified by country

There were not enough studies from the United States to estimate a pooled effect on rear end injury crashes. In Australia, a meta-analysis found a nonsignificant 1% reduction in rear end injury crashes (95% CI: 27% decrease–36% increase) with no evidence of heterogeneity (I ² = 39%, p = .178). This is shown in Figure 16.

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Figure 16

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

Total rear end crashes from RLR

Three studies estimated the effect of RLCs on total rear end crashes that were specifically identified as resulting from a red light violation (four estimates of effect). As seen in Figure 17, a pooled estimate indicated a nonsignificant 6% increase in rear end crashes from RLR (95% CI: 34% decrease–69% increase) with evidence of heterogeneity (I ² = 82.5%, p < .001).

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Figure 17

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

Red light violations

Three studies estimated the effect of RLCs on the number of red light violations, although only two of these (Arup Transportation Planning, 1992; Retting et al., 1999a) reported CI/SEs. Arup Transportation Planning (1992) reported a significant decrease of 61% (95% CI: 50–70% decrease), while Retting et al. (1999a) reported a nonsignificant decrease of 47% (95% CI: 83% decrease–66% increase). One additional study (Chin, 1989) where CIs could not be obtained, reported rates of red light violation per red light cycle and found a reduction of around 44% after RLC installation.

Additional subgroup analyses

Additional analyses were conducted with studies stratified based on whether studies accounted for RTM, whether or not RLC programs mentioned the use of warning signs, and whether or not the studies were peer-reviewed prior to publication. These subgroup analyses did not consistently explain the heterogeneity between-study results (forest plots not shown). However, studies accounting for regression to the mean tended to report more moderate decreases for right angle injury crashes than studies not accounting for regression to the mean. Similarly, peer-reviewed studies reported more moderate decreases for right angle crashes resulting in injury than studies that were not peer reviewed prior to publication (forest plots in Appendix H). Subgroup analyses were also conducted with studies stratified according to risk of bias across the six domains. These analyses did not invariably explain the heterogeneity between-study results. However, studies that had a low risk of bias in terms of controlling for confounders were found to report a greater increase in rear end crashes than studies with a high risk of bias. Studies with low risk of bias due to control of confounding reported a nonsignificant decrease in right angle crashes, compared with studies with a high risk of bias that reported a significant decrease. When stratified by risk of bias according to control for confounders and other potential sources of bias, there was some evidence that the subgroup effects on total crashes differed. The effect was inconsistent and reflected the highly heterogeneous results otherwise found for total crashes (forest plots of additional subgroup analyses in Appendix H).

Summary of effects

This systematic review shows that RLCs resulted in a significant reduction of of 24% in right angle crashes (95% CI: 35%–10% decrease), of 29% in right angle injury crashes (95% CI: 42%–14% decrease), and of 20% in total injury crashes (95% CI: 32%–5% decrease; see Table 3). A large reduction in red light violations was suggested; however, only two of three studies reported CIs preventing appropriate meta-analysis. RLCs are also associated with a significant increase in rear end crashes of 19% (95% CI: 9%–31% increase).

There was no evidence to suggest that study heterogeneity was consistently explained by country, although results were more consistent across outcomes for studies conducted in Australia. Additional subgroup analyses found some evidence that when stratified by risk of bias according to control for confounding, studies with a low risk of bias reported a nonsignificant reduction in right angle crashes.

Time of day and day of week

Time of day and day of week have been suggested as possible moderators for RLC effects. Retting, Williams, and Greene (1998); Porter and Berry (1999); and Kamyab, McDonald, and Stribiak (2000) all found that the incidence of red light violations is greater on weekdays than weekends. They also found that red light violations varied by time of day: Retting et al. (1998) and Porter and Berry (1999) indicated that the average number of red light runners was greater during the day than at night while Kamyab et al. (2000) reported a higher incidence of red light violations specifically between 3:00 pm and 5:00 pm. Fitzsimmons (2007a) suggested that these may be related to traffic volumes and congestion levels, pointing out that drivers who are on the road during peak hours may experience delays, which may lead to adverse behavior such as red light violations, particularly if traffic signals are improperly timed. Conversely, Arup Transportation Planning (1992) found that prior to the installation of RLCs, the number of violations per hour was lower during peak periods (defined as 7:30 am to 9:30 am and 4:00 pm to 6:00 pm), regardless of traffic flows. However, they also stated that during these peak periods, signal coordination was designed to favor major traffic flows, reducing delays and decreasing the potential for violations.

It should be noted that some of the studies in the meta-analysis did not collect data around the clock. Chin (1989), for example, only collected data on weekdays and only during specific periods of the day that fell between 8:00 am and 7:00 pm.

Signal timing

Another possible moderator is signal timing. Retting and Greene (1995) found that longer change intervals, which may be created by increasing the length of the yellow signal interval, may reduce red light violations and traffic crashes. Bonneson and Son (2003) reported that RLR rates are higher when the duration of the yellow signal interval is shorter than suggested by traffic engineering equations and Bonneson and Zimmerman (2004) found that after increasing the yellow interval duration, there was a decrease in the frequency of red light-related crashes. Retting, Ferguson, and Farmer (2008) found that longer yellow signal lengths were associated with a 36% decrease in red light violations.

Speed limit variations

Variations in the speed limit on the roads used in RLC studies may also serve as a moderator variable. Some studies in the meta-analysis did control for this, such as Miller et al. (2006) and Fitzsimmons (2007a, 2007b, 2007c) and Hallmark et al. (2010) considered the posted speed limit when selecting control intersections that were similar to the locations where RLCs were posted. Miller et al. (2006) only used roads with speed limits ranging from 35 to 55 mph; they pointed out that the effects of RLCs on roads with different characteristics, including speed limits, could vary.

Other potential moderators

Other factors that could influence the results include average daily traffic, the presence and/or number of left turn lanes (equivalent to a right turn lane in the United Kingdom), the size or openness of the intersections (typically larger in the United States and Australia in comparison to the United Kingdom), road gradient, and the percentage of heavy trucks in the traffic stream. Additionally, whether the signals at the intersection were actuated, pretimed, or coordinated may affect driver behavior. An actuated traffic signal will change its phase if a vehicle is detected, while pretimed signals are preprogrammed to maintain constant signal intervals regardless of traffic flow. Coordinated signals are found along major roads with multiple traffic signals and are timed to allow drivers traveling the road at the posted speed limit to have all green signal phases.

A UK study conducted on RLR in Birmingham (Lawson, 1991) reported some “average” characteristics associated with increased RLR; these features may also moderate the effect of RLCs across different studies. They found RLR crashes were relatively common late at night and in the early hours compared with non-RLR crashes, particularly on Saturday night and Sunday morning. The study reported that a combination of high speeds, large traffic volumes, and a sense of openness at the intersection could all contribute to increased RLR crashes. A sense of openness could be created by a combination of a downhill gradient toward an intersection, clear visibility through a stretch of road on the intersection exit, or the presence of multiple lanes. Lawson also noted a great variation in crash patterns between intersections and within approaches of intersections, and suggested crash history at individual sites, rather than general intersection characteristics, is the best indicator of where to place RLCs.

Public awareness and program publicity

Public knowledge of the implementation of a photo enforcement program is considered essential for program success. The use of publicity campaigns to enhance public awareness, as well as posted warning signs (discussed below), are believed to increase the general deterrent effect of the cameras and to create spillover from intersections with cameras to the wider area (see e.g., Ross & Sperley, 2011; Shin & Washington, 2007). Retting and Kyrychenko (2002) stated that the primary mechanism for preventing red light violations is driver awareness and stressed the importance of publicizing RLC enforcement programs.

In the United States, the Federal Highway Administration (FHA, 2004b) stated that a critical element for proper implementation of RLCs is an ongoing public education program. Similarly, in the United Kingdom, the Department for Transport (2007, p.9) stated, “Every effort should be made to publicize the use of cameras in an area.” The Department suggests that such publicity will enhance the deterrent effect of camera programs and improve public compliance with traffic laws.

Many of the included studies specifically mentioned the use of public awareness programs and media publicity regarding the operation of RLCs. Some jurisdictions mailed written notices to local residents to alert them to the new photo enforcement program. In many cases, the programs began publicity campaigns months before the cameras were installed and some even provided specific information as to which intersections were selected for RLC monitoring. To further increase awareness, and possibly enhance specific deterrence, many jurisdictions implemented a warning period prior to beginning formal RLC enforcement. During this period, violators were issued warnings rather than being ticketed. Warning periods, when used, generally lasted 30 days or less (see, e.g., Fitzsimmons, 2007a, 2007b, 2007c; Hallmark et al., 2010; Llau et al., 2015; Porter, Johnson, & Bland, 2013; Retting & Kyrychenko, 2002; Retting et al., 1999a; Ross & Sperley, 2011).

While none of the included studies specifically examined the impact of publicity programs on camera effectiveness, many emphasized the importance of these programs. As Ross and Sperley (2011, p.9) pointed out, “To maximize the impact of red light camera enforcement, drivers must be aware of the enforcement…”

Warning signs

A number of the included studies reported that the jurisdictions under study posted signs to inform drivers that RLCs were in use (see Figure 19 for a complete list). Along with publicity campaigns, warning signs are designed to increase driver awareness of the automated enforcement programs and to enhance their deterrent effect.

Warning signs may be posted either at or near the specific intersections at which cameras are installed or at major entrance points to the city (or both). In some cases, the use and placement of signage may be affected by legislative requirements; in the United States, this varies by state. In North Carolina, for example, state statutes require warning signs to be posted on all approaches of any intersection at which an RLC is installed, regardless of which approaches are actually monitored by RLCs (Cunningham & Hummer, 2010). Conversely, in Oregon, cities operating RLCs are required to post signs on all major routes into the city; Ross and Sperley (2011) reported that Salem, Oregon also posted warning signs on each approach where a camera was operating to help increase driver awareness of photo enforcement. In California, warning signs are required but local governments are given the option of placing signs either at each monitored intersection or at all major city or county entrances (see e.g., California State Auditor, 2002; Retting & Kyrychenko, 2002; Retting et al., 1999a). However, using the latter option does mean that there is a risk that some major entrances may be overlooked. California State Auditor (2002) reported that in 1999 approximately 700 citations for red light violations were dismissed in Sacramento after a traffic court ruling found the city had failed to fully comply with the law when installing warning signs. Following this ruling, the city placed warning signs at each RLC-monitored intersection.

The location of warning signs may affect their utility. California State Auditor (2002) suggested that placing warning signs at major entrance points rather than at treated intersections would enhance the deterrent effect of RLCs across the entire jurisdiction. Kloeden et al. (2009) reported that in Adelaide, Australia an informal survey found that many people were unaware of the location of RLCs and suggested that the location of the signs, which in Adelaide were frequently placed well back from the intersection being monitored and off to the side of the road, may have contributed to the lack of knowledge about the program and to the lack of effectiveness of RLCs in reducing traffic crashes in 2001. They suggested that placing warning signs on the far side signal poles might improve the effects of cameras.

Only a small number of studies examined the impact of signage on RLC effectiveness. Shin and Washington (2007; as discussed in Washington, 2005) stated that angle and left-turn crash reduction benefits from RLCs appeared to be greater at intersections where warning signs were installed than at intersections without signage. Persaud et al. (2005; as discussed in Council, Persaud, Eccles, Lyon, & Griffith, 2005) reported that the use of warning signs at intersections was associated with a smaller benefit than warning signs at both intersections and city limits.

A meta-analysis was conducted to explore the effect of warning signs on total crashes (Figure 18) and total injury crashes (Figure 19) in the primary studies. There was no significant difference in the effect of RLCs on total road traffic crashes or total injury crashes between the studies that mentioned the use of warning signs and those that did not. However, for both crash types, studies reporting the use of warning signs do show a slightly greater trend toward a reduction in crashes than studies that did not mention the use of warning signs (albeit insignificant).

OPEN IN VIEWER

Figure 18

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

OPEN IN VIEWER

Figure 19

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

Obstacles to implementation

Very few of the included studies provided any detailed discussion of the types of implementation issues and obstacles that RLC programs face. However, several key obstacles that were mentioned included problems with contract vendors, issues involved in operating a legally-compliant RLC program, and public attitudes and concerns.

Other implementation factors

Both the US Department of Transportation and the UK Department for Transport provide guidelines that address the implementation of traffic enforcement camera programs. The Department for Transport (2007) published a report on best practices for implementing RLCs (as well as speed cameras and combined red light and speed cameras, known as traffic safety cameras) in the United Kingdom; the report includes guidance on deployment, visibility, and signage. It is based in part on an influential study (Hooke et al., 1996) conducted in Birmingham. Similarly, the FHA (2005) produced a set of operational guidelines for implementing RLC systems.

Some of the key issues that should be considered prior to implementing an RLC program include:

Location/intersection selection: The Department of Transport (2007) recommends reviewing intersection crash history over the preceding three to five years and deploying RLCs at intersections with an accident history that includes at least one accident involving a fatality or serious injury in the past 36 months. The FHA (2005) also recommends identifying intersections with high crash rates but also suggest identifying intersections that the police identify as having red light violation problems as well as those which have high rates of public complaints.

Implementation and operational costs

Only a small number of studies provided information on implementation costs, which can include factors such as the capital cost of the RLC equipment, the costs involved in setting up an RLC program, and operational or running costs.

According to Fitzsimmons (2007a, 2007b, 2007c) and Hallmark et al., (2010), a typical RLC system can cost US $50,000 or more, depending on both the type of intersection and the number of cameras to be installed. Washington and Shin (2005) also estimated the cost of a 35-mm wet film camera system to be around US $50,000 to $60,000, including the camera and housing, pole, and loop detectors, as well as installation, and pointed out that digital systems are significantly more expensive, costing up to US $100,000.

Operational costs include the ongoing costs involved in running the RLC program. One of these is the fee charged by the RLC vendor. This can be either a flat monthly fee or a cost per citation issued. Washington and Shin (2005) stated that monthly fees tend to be approximately US $5,000 per camera system and Fitzsimmons (2007a, 2007b, 2007c) and Hallmark et al., (2010) reported similar monthly fees per intersection. Fitzsimmons (2007a, 2007b, 2007c) and Hallmark et al., (2010) also noted that in Iowa, multiple possible payment structures were implemented. While a flat monthly operating fee was one option, cities could elect to pay the vendor a fee per citation. Depending on the vendor, the fee was either fixed or decreased as the number of citations per day increased.

Shin and Washington, (2007; as discussed in Washington & Shin, 2005) suggested that RLC programs could be operated as revenue-neutral programs, so that the operating costs are equal to the fines generated from the program. However, they also pointed out the difficulty in locating published estimates of installation and operating costs.

Fiscal viability

The studies that included economic information focused either on the program's fiscal viability, which involved comparing implementation and operation costs to revenue generated, or the safety and social benefits of the program, which focused on cost changes resulting from changes in the number of traffic crashes. None of the studies conducted a complete analysis that included both operational costs and societal benefits (including crash costs).

Of the studies that examined fiscal viability, three reported negative economic outcomes (Andreassen, 1995; City of Lubbock, 2009; Sharpnack, 2009). Andreassen (1995) reported capital costs of at least AU $1.8 million and annual running costs of AU $285,000 to operate 15 RLC sites in Sydney with five cameras. Annual revenue in 1989 was AU $272,000, which did not even cover program operating costs. As Andreassen (1995) did not report any significant reduction in crashes from the RLC program, it does not appear that the program was fiscally viable. Sharpnack (2009) found that between 2003 and 2009, the RLC program in Costa Mesa, California raised approximately US $5.7 million. During this period, the city paid approximately US $6 million to the contract vendor, for a net cost to the city of about US $300,000, not counting the cost of civilian police personnel who devoted approximately 30 hr per week to RLC program tasks. Similarly, City of Lubbock (2009) found that between July and December 2007, their program, which included RLCs at 11 intersections, reported a monthly deficit ranging from US $18,858 to US $47,419.94.

Two studies reported both positive and negative economic outcomes when comparing program costs and revenue. California State Auditor (2002) examined six RLC programs in California and reported that three of the programs (Fremont, Oxnard, and San Diego) were generating cumulative net revenues while the other three (Los Angeles, Sacramento, and San Francisco) were operating with a cumulative deficit. The researchers noted that while the vendors provide similar services, they receive different amounts from each local government, which may account for some of the variation in revenue and expenditure. Garber et al. (2005) also found wide variations when they examined the fiscal feasibility of six RLC programs in Virginia. Two programs (Arlington and Fairfax City) reported a net revenue, three (Alexandria, Fairfax County, and Vienna) reported net deficits, and one (Falls Church) was operating at a break-even point. Fairfax County had the largest annual deficit (an annual net loss of US $97,811) while Arlington had the largest annual gain (an annual net revenue of US $12,499). Overall, the researchers concluded that, in general, the RLC programs were not generating net revenue.

It is interesting to note that RLC programs are often expected to be fiscally viable. This is a unique requirement, as other types of crime prevention programs generally are not held to this standard. Obviously, developing a revenue-neutral program is desirable given the budgetary constraints faced by police departments today. However, it is not clear why a program needs to break even or actually produce any positive economic outcomes to be considered worthwhile if it produces a significant increase in road safety. The measure of success for a crime prevention program is usually its ability to prevent crime, not to pay for itself.

Crash safety benefits

Five studies (Garber, 2007; Persaud et al. 2005, as discussed in Council et al., 2005; Ross & Sperley, 2011; Shin & Washington, 2007; South et al., 1988) examined the economic effect of changes in the number of traffic crashes that resulted from the use of RLCs, separate from the installation and operational costs of the programs.

Ross and Sperley (2011) examined the overall cost of crashes at one intersection in Salem, Oregon before and after an RLC was installed and found that the average monthly cost of crashes increased by about 70% during the post-installation period, rising from US $16,296 to US $27,738. They noted that during the period prior to camera installation there was a higher percentage of injury crashes and that most of the crashes occurring after the cameras were installed were rear end crashes, which tend to produce less severe injuries. However, despite this, the overall increase in the average number of crashes per month after the cameras were installed resulted in an overall higher monthly cost.

Garber (2007) focused on the safety impact of cameras in the six programs in Virginia. They reported that injury crashes increased after camera installation but pointed out that not all crash types are equally severe. To measure the effect of RLCs on net injury severity, they examined comprehensive crash costs, monetizing rear end and right angle injury crashes as well as all crashes (injury and non-injury) combined. They found that in both cases, cameras were associated with increased crash costs in some jurisdictions and decreased costs in others.

Three studies (Persaud et al., 2005, as discussed in Council et al., 2005; Shin & Washington, 2007; South et al., 1988) found positive economic impacts after RLC implementation. South et al. (1988) reported that crash costs at RLC intersections in Victoria, Australia were 13.8% lower than expected when compared to control sites. This was equivalent to an annual net savings of AU $30,253 per site. Shin and Washington (2007) examined crash costs in Phoenix and Scottsdale, Arizona before and after RLC program implementation, using national crash cost estimates for various types of crashes. They estimated a mean annual safety benefit of US $4,504 for the 10 target approaches in Phoenix and US $684,134 for the 14 target approaches in Scottsdale. The difference is reportedly due to the differential impact of the cameras in the two cities; in Phoenix, the cameras primarily contributed to a decrease in PDO crashes while in Scottsdale, they had a greater impact on reducing injury crashes. Council et al. (2005) conducted cost comparisons on crash changes in seven jurisdictions around the United States. When the jurisdictions were combined, they found an overall economic benefit of about US $39,000 per intersection per year when PDO crashes were included and US $50,000 per intersection per year when PDO crashes were excluded from analysis.

Other economic factors

Early cost-benefit analysis studies have identified significant benefits for RLCs. In the United Kingdom, Hooke et al. (1996) found that the average fixed cost per site of RLCs was just over £9,200 and average annual costs were over £5,600 per site. The study compared the costs of installing and operating RLCS (including capital costs, maintenance, and prosecution costs) to benefits resulting from crash reduction and fixed penalty revenues and found that RLCs generate significant net benefits to the police and the community as a whole. Seven of the 10 areas examined achieved a net positive return within 1 year of camera installation and all but one had done so after 5 years. Overall, the researchers reported that the return on investment was twelve times the initial investment after five years. Additionally, RLCs released traffic police for other duties; the study suggested that a savings of one percent of traffic officer time in England and Wales was equivalent to a national saving of up to £4million. Similarly, in the United States, the Federal Highway Administration (FHA, 2005) stated that RLC systems “provide an economic benefit of $28,000 to $50,000 at a treated site when considering the economic cost of crashes by crash type.”

Deterrence theory

According to Gibbs (1975), deterrence is “the omission or curtailment of a crime from the fear of legal punishment” (p. 39). Deterrence theory posits that fear of punishment encourages potential wrongdoers to comply with the law. Empirical research examining the effectiveness of traffic lights and traffic regulations on traffic violations suggests that deterrence may result when violation is associated with penalty and the potential for penalty (Cramton, 1969). This suggests that other mechanisms, such as RLCs, may also affect driver behavior and deter drivers from running red lights.

Porter and Berry (1999) conducted a survey of drivers and found that respondents did not believe that the police would catch most red light runners, so they felt there were few consequences for this behavior. However, surveys of communities in which RLC enforcement has been implemented show different results. For example, a survey conducted by Retting and Williams (2000) found that in cities with RLCs, 61% of respondents considered it likely that someone would get a ticket for running a red light in their city, compared with only 46% of respondents in cities without camera enforcement. Additionally, respondents in cities with cameras were more than twice as likely to know someone who had received a traffic ticket for running a red light as those in cities without cameras. RLCs not only increase the risk of punishment, and thus the negative consequences of RLR, but also increase the perception of risk among the general population. This suggests that the mechanism by which RLCs may reduce RLR is deterrence.

It is important to note that deterrence theory assumes that the target behavior is committed intentionally: drivers may deliberately run red lights or may intentionally try to “beat the light” by speeding up when the light turns yellow. In the latter case, even though drivers may not have actually intended to run the red light, they were aware that their attempt to beat the yellow light created the potential to do so. Deterrence assumes that drivers' behavior can be influenced by RLCs; however, some drivers may be unable to stop or clear the junction safely at the end of the green signal phase (during the yellow signal phase), this is referred to as the dilemma zone. This has implications for safety, as driver behavior may be more unpredictable; one driver may choose to stop and become at risk for a rear end collision, while a driver who continues may be at risk of crossing on the red light and prosecution and at greater risk of a collision at the intersection and potential prosecution (Maxwell & Wood, 2006). Enforcement mechanisms that attempt to deter behaviors such as RLR are unlikely to affect situations in which the behavior is committed inadvertently or unintentionally, such as when a driver was unable to see the signal, was inattentive and did not realize that the traffic light was red, or was unable to stop. Therefore, it is likely that RLCs will be more effective in reducing RLR among drivers who deliberately engage in this behavior.

Types of deterrence

There are two main types of deterrence. Specific deterrence focuses primarily on punishing apprehended offenders, with the assumption that they will be deterred from reoffending out of a desire to avoid future punishment. General deterrence focuses on the population at large and assumes that the threat of punishment will deter people from initial law violations. The greater the perception of risk of punishment, the greater the likelihood that general deterrence will be effective. To be effective, traffic enforcement policies need to do both, so that a sanction not only impacts the individual who is being punished but also others who do not directly experience the sanction (Bates, Soole, & Watson, 2012).

As a specific deterrent, RLCs focus on reducing repeat offending by punishing those individuals who offend. Because RLCs are fully automated and operate remotely, without requiring human intervention, drivers who run red lights are more likely to be detected and punished. One purpose of this punishment is to deter these drivers from running red lights again in the future.

General deterrence is achieved through increasing the risk of apprehension and punishment. This may include both the actual/objective risk of being detected and the perceived/subjective risk, which reflects drivers' belief about the likelihood that they will be detected in a violation. Overall, perceived risk appears to be most likely to influence driving behavior (Zaal, 1994). RLCs are designed to create what Belin, Tillgren, Vedung, Cameron, and Tingvall (2010) calls a “feeling of continued surveillance” that suggests a greater risk of apprehension and punishment for running red lights.

In many cases, the primary aim of enforcement strategies such as RLC programs is general rather than specific deterrence - the focus is on preventing red light violations rather than on offender detection (see, e.g., MacLean, 1985; Zaal, 1994). South et al. (1988) suggests that increased enforcement, obtained through the use of RLCs, may deter potential offenders and Retting and Kyrychenko (2002) also found that the effects of RLCs were not limited to only the specific intersections with cameras, providing further support for a general deterrent effect. Retting et al. (1999a, p.173) suggests that “The presence of cameras may promote a general readiness to stop at red lights.”

Spillover: The halo effect

According to Hu et al. (2011, p.277), “A high likelihood of apprehension helps convince motorists to comply with traffic laws.” RLCs are designed to increase the actual risk of apprehension as well as increasing the perception of increased risk, to create a general deterrent effect. However, their impact may not be limited to only those intersections with cameras. RLCs may also create spillover, so that they have a more general effect on driver behavior at signalized intersections, reducing red light violations not only at those locations where cameras are placed but also at surrounding non-camera intersections. Retting and Kyrychenko (2002, p. 1825) suggests that the potential for positive spillover is a key element of RLC programs because “the goal of highly conspicuous traffic enforcement is to produce generalized changes in driver behavior with respect to traffic safety laws.”

Attitudinal surveys suggest that RLCs do create an overall perception of increased risk. As discussed above, Retting and Williams (2000) found that in cities with RLC enforcement programs, significantly more respondents believed it was likely that someone who runs a red light would get a ticket. Arup Transportation Planning (1992) surveyed drivers in Brisbane, Australia before and after the implementation of an RLC program. He found that prior to the introduction of RLCs, most drivers did not consider it likely that they would be caught disobeying a red traffic light (only 38% of drivers rated their chances of being caught as “likely” or “very likely”); this increased to 52% after the RLC program was implemented. He also stated that a survey in Adelaide, Australia after the implementation of RLCs found that 85% of people surveyed believed that RLCs “will make it more likely for drivers to get caught running a red light” and 72% believed RLCs “would change their driving habits” (p.27).

South et al. (1988) has pointed out that for RLC enforcement to have an effect, motorists must be aware that cameras are in operation. He discussed a study of RLCs in Victoria, Australia in which RLCs were installed and monitored prior to media publicity of the program. During the period when the public was unaware of the program, approximately 300 violators per week were photographed by the RLCs; this decreased to about 20 violators per week after the media began publicizing the RLC program.

To increase the potential for spillover, and therefore increase the effectiveness of the enforcement program, Ross and Sperley (2011) suggests the use of jurisdictional boundary signs warning drivers about the use of photo enforcement mechanisms, rather than posting signs at the specific traffic intersections where cameras are installed. Shin and Washington (2007) examined the effect of RLCs in two cities in Arizona. In Phoenix, warning signs were placed at all approaches at RLC intersections, while in Scottsdale, signs were only placed on some of the intersections where cameras were deployed. The researchers found significantly greater spillover effects in Scottsdale; they suggested that the drivers in Phoenix knew which intersections and approaches had cameras and modified their behavior accordingly, while drivers in Scottsdale were not as certain of where the cameras were placed and therefore were more likely to be deterred from traffic violations citywide.

In general, empirical tests of the spillover effect of RLCs have yielded inconsistent results. Retting and Kyrychenko (2002) and Retting et al. (1999a) found red light violations in Fairfax and Oxnard decreased at both camera and non-camera intersections; they argued that in both cities the decline at non-camera sites was due to spillover (see e.g., Retting, Williams, Farmer, & Feldman, 1999b). Similarly, Kull (2014) found a decline in right angle crash rates in Chicago at intersections with and without RLCs (36% and 27%, respectively), and concluded that the decrease in crashes at the non-camera sites was due to spillover. AECOM Canada, Ltd. (2014) reported relatively large spillover effects as well, suggesting that the reduction in collisions at non-RLC intersections was due to changes in driver behavior that may have resulted from the widespread publicity of the RLC program throughout the jurisdiction as well as the public's lack of knowledge as to which specific intersections were equipped with cameras. Chin (1989) found that the installation of RLCs at intersections in Singapore not only reduced RLR on those approaches covered by the cameras but on other approaches as well.

Conversely, while Hobeika and Yaungyai (2006) found a spillover effect of RLCs on PDO crashes in Fairfax, they also concluded that there was no spillover effect for injury crashes. Garber (2007) found that there were no significant changes in various types of traffic crashes at non-camera sites in Virginia that were used to test for spillover.