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Publication Number: FHWA-HRT-05-048
Date: April 2005

Safety Evaluation of Red-Light Cameras

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XI. Results

Results of this study were obtained separately for the composite effects at the camera sites and reference sites analyzed for spillover effects. As best as could be determined, RLC installation was the only change that occurred at the sites when the cameras were installed. In a few cases, the intersection was substantially changed in either the before- or after-period. In such cases, the data after a change in the after-period and before a change in the before-period were excluded from the analysis; therefore, the reported effects most likely result from RLC installation.

Composite Effects at Camera sites

Because the intent of the research was to conduct a multijurisdictional study representing different locations across the United States, the aggregate effects over all RLC sites in all jurisdictions was of primary interest. Table 13 shows the combined results for the seven jurisdictions. As seen, there is a significant decrease in right-angle crashes but a significant increase in rear end crashes. Note that "definite injury" crashes here are defined as K, A, and B crashes; they do not contain the "possible injury" crashes captured by KABCO-level "C." Table 14 indicates that, while the magnitude of the effects changes across jurisdictions, the direction of the effects is remarkably consistent.

Table 13. Combined results for the seven jurisdictions.


Rear end

Total crashes



Total crashes



EB estimate of crashes expected in the after-period without RLC
Count of crashes observed in the After-period
Estimate of percentage change(standard error)
- 24.6(2.9)
- 15.7(5.9)
Estimate of the change in crash frequency
- 379
- 55

Note: A negative sign indicates a decrease in crashes.

Table 14. Results for individual jurisdictions



(in random order)a

Right-angle crashes

Rear end crashes

Percentage change

(standard error)

Percentage change

(standard error)


- 40.0 (5.4)

21.3 (17.1)


+ 0.8 (9.0)

8.5 (9.8)


- 14.3 (12.5)

15.1 (14.1)


- 24.7 (8.7)

19.7 (11.7)


- 34.3 (7.6)

38.1 (14.5)


- 26.1 (4.7)

12.7 (3.4)


- 24.4 (11.2)

7.0 (18.5)

a Jurisdictions are not identified because of an agreement with them, and it is irrelevant to the findings.
Note: A negative sign indicates a decrease in crashes.

Spillover Effects

To investigate possible spillover effects of RLC programs, a separate analysis was performed using the untreated signalized intersection reference sites. For this analysis, the before-and after-periods for these sites in each jurisdiction were demarcated by the year of the first RLC installation at the treatment sites. (Because, by definition, specific treatment dates do not exist for each untreated reference site, this decision was based on the assumption that the public may have perceived that cameras were at noncamera locations from the time of the initial publicity campaign.) Table 15 shows the composite results of this analysis combining data from all of the jurisdictions. As seen, there are indications of a modest spillover effect on right-angle crashes. That this is not mirrored by the increase in rear end crashes that was seen in the treatment group would detract somewhat from the credibility of this result as evidence of a general deterrence effect.

Table 15. Before-and-after results for total crashes at spillover intersections.

  Right-angle crashes Rear end crashes
EB estimate of crashes expected in the after-period without RLC 3,430 3,802
Count of crashes observed in the After-period 3,140 3,873
Estimate of percentage change (standard error) - 8.5




Note: A negative sign indicates a decrease in crashes.

Discussion of Crash Effects

This statistically defendable study found effects that were consistent in direction with those found in many previous studies, although the benefits were somewhat lower than those reported in many sources. This indicates that regression to the mean might have been at play in many of those studies, and it emphasizes the need for controlling those effects in an evaluation of red-light-camera programs, and studies of road safety countermeasures in general.

The opposite direction effects for rear end and right-angle crashes deserves attention from two perspectives. First, the extent to which the increase in rear end crashes negates the benefits for right-angle crashes is unclear at this point. An examination of the changes in crash numbers is insufficient to provide clarity on this issue because of differences in severity levels between right-angle and rear end crashes and in the changes in these crashes following RLC installation. The economic analysis, discussed in the next section, examines the economic costs of the changes based on an aggregation of rear end and right-angle crash costs for various severity levels.

The second perspective of the opposing effects for the two crash types is the implication that RLC systems would be most beneficial at intersections where there are relatively few rear end crashes and many right-angle ones. To provide better guidance on this issue requires an examination of the net economic effect on intersections grouped by the numbers of each crash type. That examination, which seeks generally to isolate all of the factors that would favor (or discourage) the installation of RLC systems, uses the net economic benefit as the outcome variable, also discussed in the next section.

The indications of a spillover effect point to a need for a more definitive study of this issue. That more confidence could not be placed in this aspect of the analysis reflects that this is an observational retrospective study of RLC installations that took place over many years, and in locations where other programs and treatments may have affected crash frequencies at the spillover study sites. A prospective study with an explicit purpose of addressing this issue appears to be required.

The Economic Analysis of RLC-Related Severity and Frequency Changes

Development of Unit Crash Cost Estimates

This study needed economic cost per crash for the categories of interest. Those categories included "right-angle," "rear end", and "other" at urban and rural signalized intersections. The crash cost needed to be keyed to police crash severity (KABCO) found in the files available for use. In addition, because of limited sample sizes for fatal and severe (A) injury crashes in the after-period for some study intersections, crash costs were needed for combined categories such as K+A severity.

The Pacific Institute for Research and Evaluation (PIRE) developed the cost estimates used in this RLC analysis as part of a larger effort of producing cost estimates for other crash types. Details of the development of the unit crash-cost estimates can be found in a recent paper and in an internal report available from FHWA.(5,6). In summary, by merging previously developed costs per victim keyed on the AIS injury severity scale into U.S. traffic crash data files that scored injuries in both AIS and KABCO scales, PIRE economists could produce estimates for both economic (human capital) costs and comprehensive costs per crash. The comprehensive cost estimates include both economic costs and costs associated with losses in the quality of life. In addition, the analysis produced an estimate of the standard deviation and the 95-percent confidence intervals for each average cost. Following is a list of databases PIRE used:

  • The 1999-2001 Crashworthiness Data System data provided the basic data source. This database includes both AIS and KABCO injury scaling for passenger vehicle occupants in towaway (but not nontowaway) crashes.(30) Note that nontowaway crashes would be predominately noninjury, "O" crashes.
  • The 1982-1986 National Accident Sampling System data were used to fill in the nontowaway part of the distribution.(31) While the data were not recent, the information provided the most recent medical description available on injuries to other non-CDS crash victims.
  • The 1999-2001 GES data were then used to weight the NASS data so that they represent the annual estimated GES injury victim counts in non-CDS crashes.(32) Applying this information controlled for crash type (as defined by geometry), police-reported injury severity, speed limit (≤ 72.42 km/h [45 mi/h] and > 80.47 km/h [50 mi/h]), and restraint use. Weighting the NASS data to GES restraint-use levels updates the NASS injury profile to a profile reflecting contemporary belt use levels. Sample size considerations drove the decision to pool 3 years of data. At the completion of the weighting process, a hybrid CDS/NASS file had been developed that included weights that summed to the estimated current annual incidence by police-reported injury severity and other relevant factors.

To meet the needs of this project and future FHWA projects, both comprehensive and human capital cost estimates were developed for six KABCO groupings within 22 selected crash types and two speed limit categories (≤ 72.42 km/h [45 mi/h] and > 80.47 km/h [50 mi/h]). As indicated earlier, the KABCO groupings ranged from detailed estimates for each level of crash severity within each crash type to combined levels of KABCO without regard to crash type. All estimates were stated in Year 2001 dollar costs.

Because this RLC analysis involved placing a value on fatal crashes, comprehensive cost estimates (which include quality-of-life losses) were used as recommended in Council, et al.(3) This specific analysis was focused on right-angle and rear end crashes at signalized intersections in urban areas. Speed limits of ≤ 72.42 km/h (45 mi/h) and > 80.47 km/h (50 mi/h) were used as surrogates for urban and rural here because it was not possible to define an urban/rural variable in the databases PIRE used. Even though a limited number of the study locations had speed limits of 80.47 km/h (50 mi/h), urban unit costs were assigned to all crashes in the analysis. The effect of this approximation probably is small because only 10 of the 132 sites had speed limits of 80.47 km/h (50 mi/h) or more, and all were located in urban areas. Because the initially developed cost estimates for B- and C-level rear end crashes indicates some anomalies in the order (e.g., C-level cost were higher, probably because on-scene police estimates of "minor injury" often ultimately include expensive whiplash injuries), the B- and C-level costs were combined by PIRE into one cost. In initial economic analysis, an attempt was made to use three cost categories within each of the pertinent crash types, K+A, B+C, and no-injury. (It is not feasible to analyze fatal injuries separately in a study such as this because there were limited fatal crashes in any time period. The cost of one fatal crash in any cell could significantly bias the results.) However, because of the low sample sizes of fatal and serious (A-level) crashes in the after-period for some intersections, and the need to use the same cost categories across all intersections in all seven jurisdictions, two crash cost levels were ultimately used in all analyses, injury (K+A+B+C) and non-injury (O). The original estimate developed by PIRE and the combined weighted cost-per-crash estimates used for each crash type are shown in table 16. Also shown in table 16 are the standard deviations for the 2 severity categories used in the analyses.

Table 16. Original comprehensive crash cost estimates for urban signalized intersections

by severity level and combined weighted estimates used in the economic effects analysis.

Crash severity level

Right-angle crash cost

Rear end crash cost














(standard deviation)





K+A+B+C "injury crash"

(standard deviation)





Empirical Bayes Estimates of the Economic Effects

Table 17 gives the results for the economic effects including and excluding PDO crashes, estimated from equations 7 to 10 and the associated procedures shown earlier. The latter estimates are included because several jurisdictions considerably underreport PDO collisions. The columns labeled "All crashes" include non-right-angle, nonrear end "other" crashes for which reliable unit costs could not be developed by PIRE because of small sample sizes. It was decided that the same costs as for angle crashes would be used for the "other" category. For completeness, the small changes in these other crashes needed to be accounted for in reporting effects on all crashes, even though the changes may be random and have nothing to do with RLC installation. The results show a positive aggregate economic benefit of more than $14 million over approximately 370 site years, which translates into a crash reduction benefit of approximately $38,000 per site year. The implication from this result is that the lesser severities and generally lower unit costs for rear end injury crashes together ensure that the increase in rear end crash frequency does not negate the decrease in the right-angle crashes targeted by red-light-camera systems.

Table 17. Economic effects* including and excluding PDOs.

  All severities combined PDOs excluded
  Right- angle crashes Rear end crashes All crashes Right- Angle crashes Rear end crashes All crashes
EB estimate of crash costs without RLC $66,814,067 $69,347,624 $161,843,021 $61,687,367 $52,681,148 $134,407,104
Cost of crashes recorded after RLC (370 site years) $48,319,090 $75,222,780 $147,470,550 $43,868,392 $53,944,539 $115,901,685
Percentage change in crash cost (s.e.) [negative is decrease] - 27.7(0.6) 8.5(0.7) - 8.9(0.4) - 28.9(0.6) 2.4(0.8) - 13.8(0.5)
Crash cost decrease (per site year) - - $14,372,471($38,845) - - $18,505,419($50,015)

*Using a combined unit cost for K+A+B+C

As noted earlier, sample size considerations forced the combination of all injury crashes into one category (K+A+B+C). Concern was raised that the distribution of crash severity within this combined category might have changed between the before-and-after periods for either or both crash types. That is, injury-related angle crashes could have become more or less severe between the two periods. If so, the use of one injury-crash cost for both periods would be questionable. Table 18 presents the distributions of both right-angle and rear end injury crashes in each period.

Table 18. Severity-level distributions for right-angle and rear end injury crashes in the before-and-after periods.

Crash type Total frequency Percentage in each Injury category
Before 1,854 0.5 7.7 30.8 61.1
After 634 0.8 8.5 37.4 53.3
Rear end          
Before 1,930 0.1 3.1 10.9 86.0
After 1,008 0.0 2.7 13.5 83.8

As can be seen, while there is no apparent shift in the severity distribution for rear end injury crashes from before to after, the right-angle crashes appear slightly more severe in the after-period (i.e., the percentages of K, A, and B are slightly higher). Further analysis revealed that this shift occurred in only two of the seven jurisdictions. Nevertheless, because the same crash costs essentially were used for before-and after-periods, this means that the cost of the after-period right-angle crashes may be slightly underestimated, even when all jurisdictions are combined. An attempt was made to estimate the potential effect of this shift on the economic savings, even though this could be done only by using anomalous data for individual KABCO categories that we argued against using earlier. With these data, it appears that if the shift were real, the overall cost savings reported in the last row of table 17 could be decreased by approximately $4 million; however, note that there would still be positive economic benefits, even if it is assumed that the unit cost shifts were real and correctly estimated.

Examination of the aggregate economic effect per after-period year for each site indicates substantial variation, much of which could attributed to randomness. It was reasonable to suspect that some of the differences may be to the result of factors that affect RLC effectiveness. The results of the examination of those factors are described next.

Factors Affecting RLC Effectiveness

Two types of disaggregate analyses were undertaken to identify factors associated with the greatest economic benefits or that might discourage the use of RLCs. The basic outcome measure used is the aggregate economic effects, that is, the combined economic effects on rear end, right-angle, and other crashes of various severities. The economic effect for each crash type and severity was derived from equation 5 as the difference between the expected cost of crashes in the after-period if no RLC were installed and the cost of crashes actually occurring at the treatment sites in the after-period.

The first analysis was a univariate exploration of the results of aggregate economic effects for each intersection, aiming to identify factors that might be associated with the variation in the effects at individual sites. In this, spreadsheets were used to sort the data and results for each site by various columns, and to group by ranges of a variable to explore the relationship between factors and the measured aggregate economic effect per after period site year for a group as a whole for all crash types combined. For example, sorting by ascending order of AADT, the spreadsheet was set up so that aggregate economic effect per after-period site year in a given row applies to all sites with AADT less than or equal to the value in that row; one can then look for trends in the outcome measure. Similarly, by sorting by publicity level, one can obtain the aggregate economic effect per after-period site year separately for the 85 sites with high publicity level and the 47 sites with medium publicity level.

Naturally, some of the conclusions from the univariate exploratory analysis could result from correlation among the various variables found to affect the RLC effect. This could mask the effects or indicate effects that are not real. The results of the exploratory analyses were used to guide a more formal analysis that used multivariate modeling to assess whether the conclusions from the univariate analysis might remain despite the obvious correlations among variables found in that analysis to be associated with economic effects.

In this more formal disaggregate analysis, data for all jurisdictions were combined to develop a model to estimate the value of aggregate economic effect per site year for an individual site using traffic volumes and other site characteristics, such as proportion of rear end or right-angle crashes and signalization features, and RLC implementation features such as publicity level as explanatory variables. The model was a linear one with a normal error distribution. It took the following form:

The equation reads capital letter phi subscript cost per after period year equals alpha plus b subscript 1 times x subscript 1, plus b subscript 2 times x subscript 2, plus b subscript 3 times x subscript 3, plus etc, etc through b subscript n times x subscript n.

where α is the calibrated intercept and b1, b2, ... , bn are the estimated effects on Фcost per after-period year of factors x1, x2, x3 ...xn.

Stepwise linear regression was performed with the SAS statistical analysis software package, using the estimates of the Фcost per after-period year as estimates of the dependent variable. It should be pointed out that the absence of a variable in the final model does not necessarily mean that the variable would not affect the safety effect of RLCs because an effect with low statistical significance could result from correlation with other variables, a lack of variation in the data, or a sample that is too small. In addition, it should be emphasized that the generally small size of the aggregate economic effect of RLCs was already strongly indicative of the reality that one is unlikely to detect with significance many factors that affect the safety effect of RLCs.

Data for all treated intersections in all seven jurisdictions were used in this analysis. However, the different jurisdictions had different crash reporting thresholds, which resulted in significantly different numbers and percentages of non-injury crashes across jurisdictions. Because this analysis required that the crash costs for all intersections (and thus all jurisdictions) be calculated on a common basis, non-injury crashes were omitted from this analysis. Because the analysis is aimed at identifying factors of interest, and because these factors can be identified as logically with injury crashes as with total crashes, this was felt to be proper procedure.

The exploratory univariate analysis led to the following general observations on the net economic effects:

  • High publicity level (85 sites) is associated with a greater benefit than medium publicity level (47 sites).
  • Fine plus demerit point penalty (90 sites) is associated with a greater benefit than a fine-only penalty (42 sites).
  • Warning sign at intersections only (39 sites) is associated with a smaller benefit than warning sign at both intersections and city limits (73 sites).
  • Benefits are greater at sites with one or more left-turn protected phases (105 sites) than at those with no protected phases (27 sites). This variable may well be a surrogate for the volume of left-turning traffic or opportunities for crashes involving a vehicle going straight through and one turning left at the end of a protected or permitted phase.
  • There are indications that the aggregate economic benefit increases with total entering AADT, increasing proportion of total traffic being on the major road, and with an increasing ratio of right-angle to rear end crashes.
  • There are indications that the aggregate economic benefit increases with shorter cycle lengths and shorter intergreen periods. These intuitive indications were derived despite the difficulty of defining these variables for a given intersection because of variation in them over the years and even over a single day. The maximum recorded values for these variables in the study period were used in the analysis in the absence of a more stable and pertinent measure of these factors.

Clearly some of these variables that indicate effects in the univariate analysis are correlated, and therefore they may show effects that are not real. For example, left-turn protection is likely related to traffic volume levels; high publicity levels may exist in jurisdictions with the highest traffic volumes. To mitigate this difficulty, the multivariate regression analysis was undertaken to see if the direction of the effect of a given variable remains the same if the effects of other variables are considered simultaneously. This additional analysis confirmed the direction of all of the effects observed except for the penalty variable and the one related to the presence of a left-turn protected phase.

In interpreting these results, consideration should be given to the following important points:

  • Factors other that the ones previously identified were examined. These include traffic signal actuation, presence of turn restrictions, major road speed limit, and number of approach legs; for these, the inability to detect a clear-cut effect may have resulted from the small samples for one level of the factor (e.g., only 27 of the 132 sites had no protected left-turn phases).
  • The intent of the multivariate regression analysis was to confirm the direction of the effect, not to establish effects with statistical significance or to assess the size of the effect. To undertake analyses for these purer purposes would have required a substantially larger database, much more precision in the estimate of economic effect at each site, and more accurate specification and measurement of the independent variables. For the purposes of this current investigation, it suffices that both the univariate and multivariate analyses are reasonably in accord with the perceptions that are commonly held by those involved in red-light-camera programs.
  • Some of the variables may well be surrogates for others that more directly influence the aggregate economic effects. For example, the presence of left-turn protection probably is associated with the volume of left-turning traffic or, more directly, with opportunities for crashes involving a vehicle going straight through and one turning left at the end of a protected or permitted phase.
  • The results do not provide numerical guidance for trading off the effects of various factors. The intent of identifying these factors is to assist RLC implementers in choosing sites for treatment installation and determining the type of signing and publicity that might enhance the results of the program. For site identification, the results indicate that an implementer should give the highest priority for RLC implementation to the sites with most or all of the positive binary factors present (e.g., left-turn protection) and with the highest levels of the favorable continuous variables (e.g., higher ratios of right-angle crashes to rear end crashes). Based on the combined univariate analyses and modeling, as well a logical consideration of the result of the crash effects analysis that rear end crashes increase and right-angle ones decrease following RLC implementation, it would appear that the most important determinant of site choice would be a high ratio of right-angle to rear end crashes. After site choices are made, signing at both intersection and city limits and a high-level publicity campaign also appear to increase program benefits.
  • To quantify the potential aggregate benefit for a contemplated RLC site, it is possible to use the SPFs and probable estimates of safety effect shown in table 17. The rudiments of the procedure are documented in two recent publications.(23,33) In that procedure, crash and traffic data at the intersection are used to obtain an empirical Bayes estimate of the expected number of crashes by impact type and severity without a red-light camera. The estimate of probable safety effect from table 13 is applied to the EB estimate to derive an estimate of the expected change in crashes per year by type with the RLC implemented. The cost per crash derived for this project can then be applied to the crash changes expected for each impact and severity type. The results can then be summed to obtain an estimate of the aggregate benefit per year for the contemplated installation.

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