Safety Evaluation of Red-Light 

Cameras

PUBLICATION NO. FHWA-HRT-05-048 APRIL 2005

Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296
FOREWORD 

This is a final report on a study to evaluate the effectiveness of red-light-camera (RLC) systems 

in reducing crashes. The intended audience is professionals who make decisions about safety 

programs for intersections. The study involved empirical Bayes before-and-after research using 

data from seven jurisdictions across the United States to estimate the crash and associated 

economic effects of RLC systems. The study included 132 treatment sites and specially derived 

rear end and right-angle unit crash costs for various severity levels. Crash effects detected were 

consistent in direction with those found in many previous studies: decreased right-angle crashes 

and increased rear end ones. The economic analysis examined the extent to which the increase in 

rear end crashes negates the benefits for decreased right-angle crashes. There was indeed a 

modest aggregate crash cost benefit of RLC systems. A disaggregate analysis found that greatest 

economic benefits are associated with the highest total entering average annual daily traffic, the 

largest ratios of right-angle to rear end crashes, and with the presence of protected left-turn 

phases. There were weak indications of a spillover effect that point to a need for a more 

definitive, perhaps prospective, study of this issue. 

Michael F. Trentacoste, Director 

 Offices of Safety Research and Development 

Notice 

This document is disseminated under the sponsorship of the U.S. Department of Transportation 

in the interest of information exchange. The U.S. Government assumes no liability for the use of 

the information contained in this document. This report does not constitute a standard, 

specification, or regulation. 

The U.S. Government does not endorse products or manufacturers. Trademarks or 

manufacturers' names appear in this report only because they are considered essential to the 

objective of the document. 

Quality Assurance Statement 

The Federal Highway Administration (FHWA) provides high-quality information to serve 

Government, industry, and the public in a manner that promotes public understanding. Standards 

and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its 

information. FHWA periodically reviews quality issues and adjusts its programs and processes to 

ensure continuous quality improvement. 
1. Report No 2. Government Accession No. 3. Recipient's Catalog No. 

FHWA-HRT-05-048 N/A N/A 

4. Title and Subtitle 5. Report Date 

 April 2005 

 

6. Performing Organization Code 

Safety Evaluation of Red-Light Cameras 

N/A 

7. Authors(s) 8. Performing Organization Report No. 

Forrest M. Council, BMI-SG; Bhagwant Persaud, Ryerson University; Kimberly 

Eccles, BMI-SG; Craig Lyon, Ryerson University; and Michael S. Griffith, Federal 

Highway Administration.

 N/A 

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 

N/A 

11. Contract or Grant No. 

DTFH61-96-C-00077 

13. Type of Report and Period Covered 

Battelle Memorial Institute

505 King Avenue

Columbus, OH 43201

Final – 11/19/01 – 11/30/04 

12. Sponsoring Agency Name and Address 14. Sponsoring Agency Code 

ITS Joint Program Office and Office of Safety Research and Development 

Federal Highway Administration 

6300 Georgetown Pike 

McLean, VA 22101 

 

15. Supplementary Notes 

Contracting Officer’s Technical Representative: 

 Michael Griffith, FHWA Office of Safety Research and Development 

16. Abstract 

The objective of this final study was to determine the effectiveness of red-light-camera (RLC) systems in reducing 

crashes. The study used empirical Bayes before-and-after research using data from seven jurisdictions across the 

United States at 132 treatment sites. The purpose of the study was to estimate the crash and associated economic 

effects of RLC systems and specially derived rear end and right-angle unit crash costs for various severity levels. 

Crash effects detected were consistent in direction with those found in many previous studies: decreased right-angle 

crashes and increased rear end ones. The economic analysis examined the extent to which the increase in rear end 

crashes negates the benefits for right-angle crashes. The analysis showed an aggregate crash cost benefit of RLC 

systems. A disaggregate analysis found that the greatest economic benefits are associated with the highest total 

entering average annual daily traffic, the largest ratios of right-angle to rear end crashes, and with the presence of 

protected left turn phases. There were weak indications of a spillover effect that points to a need for a more definitive, 

perhaps prospective, study of this issue.

17. Key Words 18. Distribution Statement 

Red light camera, Empirical Bayes, Crash 

evaluation, Economic analysis, Signalized 

intersection 

No restrictions. This document is available to the 

Public through the National Technical Information Service; 

Springfield, VA 22161 

19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price 

 Unclassified Unclassified 95 N/A 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized (art. 5/94)
ii 

SI* (MODERN METRIC) CONVERSION FACTORS 

APPROXIMATE CONVERSIONS TO SI UNITS

Symbol When You Know Multiply By To Find Symbol 

LENGTH 

in inches 25.4 millimeters mm 

ft feet 0.305 meters m 

yd yards 0.914 meters m 

mi miles 1.61 kilometers km 

AREA 

in2 square inches 645.2 square millimeters mm2

ft2 square feet 0.093 square meters m2

yd2 square yard 0.836 square meters m2

ac acres 0.405 hectares ha 

mi2 square miles 2.59 square kilometers km2

VOLUME 

fl oz fluid ounces 29.57 milliliters mL 

gal gallons 3.785 liters L 

ft3 cubic feet 0.028 cubic meters m3 

yd3 cubic yards 0.765 cubic meters m3 

NOTE: volumes greater than 1000 L shall be shown in m3

MASS 

oz ounces 28.35 grams g

lb pounds 0.454 kilograms kg

T short tons (2000 lb) 0.907 megagrams (or "metric ton") Mg (or "t") 

TEMPERATURE (exact degrees) o

F Fahrenheit 5 (F-32)/9 Celsius oC 

or (F-32)/1.8 

ILLUMINATION 

fc foot-candles 10.76 lux lx 

fl foot-Lamberts 3.426 candela/m2 cd/m2

FORCE and PRESSURE or STRESS 

lbf poundforce 4.45 newtons N 

lbf/in2 poundforce per square inch 6.89 kilopascals kPa 

APPROXIMATE CONVERSIONS FROM SI UNITS 

Symbol When You Know Multiply By To Find Symbol 

LENGTH

mm millimeters 0.039 inches in 

m meters 3.28 feet ft 

m meters 1.09 yards yd 

km kilometers 0.621 miles mi 

AREA mm2

 square millimeters 0.0016 square inches in2 

m2 square meters 10.764 square feet ft2 

m2 square meters 1.195 square yards yd2 

ha hectares 2.47 acres ac 

km2 square kilometers 0.386 square miles mi2 

VOLUME 

mL milliliters 0.034 fluid ounces fl oz 

L liters 0.264 gallons gal 

m3 cubic meters 35.314 cubic feet ft3 

m3 cubic meters 1.307 cubic yards yd3 

MASS 

g grams 0.035 ounces oz

kg kilograms 2.202 pounds lb

Mg (or "t") megagrams (or "metric ton") 1.103 short tons (2000 lb) T 

TEMPERATURE (exact degrees) o

C Celsius 1.8C+32 Fahrenheit oF 

ILLUMINATION 

lx lux 0.0929 foot-candles fc 

cd/m2 candela/m2 0.2919 foot-Lamberts fl

FORCE and PRESSURE or STRESS 

N newtons 0.225 poundforce lbf 

kPa kilopascals 0.145 poundforce per square inch lbf/in2

*SI is the symbol for th International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E e 380. 

(Revised March 2003)
iii 

Table of Contents 

 Page 

I. Executive Summary ..........................................................................................................................1

II. Introduction.......................................................................................................................................7

III. Project Overview...............................................................................................................................9

Phase I—Evaluation Design ..................................................................................................... 10 

Phase II—Evaluation Implementation...................................................................................... 11 

IV. Literature Review of Critical Studies............................................................................................13

Summary of Findings................................................................................................................ 13 

Lessons Learned and Issues Raised by Literature Search ........................................................ 18 

V. Determination of Study Questions to be Answered .....................................................................21

VI. Determination of RLC-Related Data Availability........................................................................25

VII. Methodology for National, MultiJurisdiction Study....................................................................27

Study Design Details................................................................................................................. 29 

Basic Objectives and Main Analytical Requirements .......................................................... 29 

Overview of the General Evaluation Methodology.................................................................. 30 

Empirical Bayes Before-and-After Evaluation Example...................................................... 31 

Data Collection Plan ................................................................................................................. 33 

Choice of Jurisdictions.......................................................................................................... 34 

Selection of Study Jurisdictions............................................................................................ 39 

Data Collection Requirements .............................................................................................. 41 

VIII. Study Methodology for Economic Analysis ..................................................................................43

Initial Economic Analysis Study Design .................................................................................. 43 

Examination of Alternative Economic Analysis Methods........................................................ 44 

Final Methodology for Economic Analysis.............................................................................. 49 

Types and Levels of Economic Cost Estimates.................................................................... 49 

Choice of Cost Levels.......................................................................................................... 50 

Economic Analysis Statistical Method ................................................................................. 51 

IX. Data Collection....................................................................................................................................53

Crash Data................................................................................................................................. 53 

Intersection Inventory and Volume Data.................................................................................. 53 

X. Data and SPF Preparation ..................................................................................................................57

Crash Data Linkage to Intersections......................................................................................... 57 

Defining Red-Light-Running Crashes ...................................................................................... 57 

Development of Safety Performance Functions ....................................................................... 60 

XI. Results..................................................................................................................................................63

Composite Effects at Camera sites ........................................................................................... 63 

Spillover Effects........................................................................................................................ 64 

Discussion of Crash Effects...................................................................................................... 64 

The Economic Analysis of RLC-Related Severity and Frequency Changes............................ 65 

Development of Unit Crash Cost Estimates ......................................................................... 65 

Empirical Bayes Estimates of the Economic Effects............................................................ 67 

Factors Affecting RLC Effectiveness ................................................................................... 69 

XII. Discussion and Conclusions..............................................................................................................73

Appendix A. Sources of Data by Jurisdiction........................................................................................79

Appendix B. Number of Treated, Reference, and Comparison Sites in Each Jurisdiction. .............81

Acknowledgments .....................................................................................................................................83
iv 

References..................................................................................................................................................85

Other References.......................................................................................................................................89
v 

List of Figures 

 Page 

Figure 1. Project workflow. ............................................................................................................ 9 

List of Tables 

Page

Table 1. Summary of findings from past studies. ......................................................................... 15 

Table 2. Summary of results for right-angle injury crashes at site (i). ......................................... 32 

Table 3. The composite effect over several sites (for illustration). .............................................. 33 

Table 4. P-values for various sample sizes and expected changes in safety................................. 34 

Table 5. After period crash rate assumptions................................................................................ 35 

Table 6. Sample analysis for right-angle crash effects. ................................................................ 37 

Table 7. Sample analysis for rear end crash effects...................................................................... 38 

Table 8. Best judgment on possibility of detecting safety effects. ............................................... 39 

Table 9. Best judgment on sites to use based on crash and noncrash data available.................... 40 

Table 10. Data items required....................................................................................................... 42 

Table 11. Definitions of crash types used in the analyses for each jurisdiction. .......................... 59 

Table 12. Safety performance functions for the signalized intersections reference group........... 62 

Table 13. Combined results for the seven jurisdictions................................................................ 63 

Table 14. Results for individual jurisdictions............................................................................... 63

Table 15. Before-and-after results for total crashes at spillover intersections.............................. 64 

Table 16. Original comprehensive crash cost estimates for urban signalized intersections......... 67 

Table 17. Economic effects including and excluding PDOs. ....................................................... 68 

Table 18. Severity-level distributions for right-angle and rear end injury ................................... 68 

Table 19. Data sources...................................................................................................................79 

Table 20: Sites by jurisdiction. ......................................................................................................81 

1 

I. EXECUTIVE SUMMARY 

ABSTRACT 

The fundamental objective of this research was to determine the effectiveness of red-lightcamera (RLC) systems in reducing crashes. The study involved an empirical Bayes (EB) beforeafter research using data from seven jurisdictions across the United States to estimate the crash 

and associated economic effects of RLC systems. The study included 132 treatment sites, and 

specially derived rear end and right-angle unit crash costs for various severity levels. Crash 

effects detected were consistent in direction with those found in many previous studies: 

decreased right-angle crashes and increased rear end ones. The economic analysis examined the 

extent to which the increase in rear end crashes negates the benefits for decreased right-angle 

crashes. There was indeed a modest aggregate crash cost benefit of RLC systems. A disaggregate 

analysis found that greatest economic benefits are associated with factors of the highest total 

entering average annual daily traffic (AADT), the largest ratios of right-angle to rear end crashes, 

and with the presence of protected left-turn phases. There were weak indications of a spillover 

effect that point to a need for a more definitive, perhaps prospective, study of this issue. 

Introduction and Background 

RLC systems are aimed at helping reduce a major safety problem at urban and rural intersections, 

a problem that is estimated to produce more than 100,000 crashes and approximately 1,000 deaths 

per year in the United States.(1) The size of the problem, the promise shown from the use of RLC 

systems in other countries, and the paucity of definitive studies in the United States established 

the need for this national study to determine the effectiveness of the RLC systems jurisdictionwide in reducing crashes at monitored intersections. This study included collecting background 

information from literature and other sources, establishing study goals, interviewing and choosing 

potential study jurisdictions, and designing and carrying out the study of both crash and economic 

effects. A description of all project efforts is described in this report and, to a lesser extent, in 

two Transportation Research Board (TRB) papers(2,3) that were also prepared. 

A literature review found that estimates of the safety effect of red-light-running programs vary 

considerably. The bulk of the results appear to support a conclusion that red light cameras reduce 

right-angle crashes and could increase rear end crashes; however, most of the studies are tainted 

by methodological difficulties that would render useless any conclusions from them. One 

difficulty, failure to account for regression to the mean1

 (RTM), can exaggerate the positive 

effects, while another difficulty, ignoring possible spillover effects2 to intersections without 

 

1

“Regression to the mean” is the statistical tendency for locations chosen because of high crash histories 

to have lower crash frequencies in subsequent years even without treatment. 

2 Spillover effect is the expected effect of RLCs on intersections other than the ones actually treated 

because of jurisdiction-wide publicity and the general public’s lack of knowledge of where RLCs are 

installed.
2 

RLCs, will lead to an underestimation of RLC benefits, more so if sites with these effects are 

used as a comparison group. 

While it is difficult to make definitive conclusions from studies with failed methodology validity, 

the results of the review did provide some level of comfort for a decision to conduct a definitive, 

large-scale study of installations in the United States. It was important for the new study to 

capitalize on lessons learned from the strengths and weaknesses of previous evaluations, many of 

which were conducted in an era with less knowledge of potential pitfalls in evaluation studies 

and methods to avoid or correct them. 

The lessons learned required that the number of treatment sites be sufficient to assure statistical 

significance of results, and that the possibility of spillover effects be considered in designating 

comparison sites, perhaps requiring a study design without a strong reliance on the use of 

comparison sites. Previous research experience also pointed to a need for the definition of the 

term, “red-light-running crashes,” to be consistent, clear, and logical and for provision of a 

mechanism to aggregate the differential effects on crashes of various impact types and severities. 

Methodological Basics 

The general crash effects analysis methodology used is different from those used in past RLC 

studies. This study benefits from significant advances made in the methodology for observational 

before-after studies, described in a landmark book by Hauer.(4) The book documented the EB 

procedure used in this study. The EB approach sought to overcome the limitations of previous 

evaluations of red-light cameras, especially by properly accounting for regression to the mean, 

and by overcoming the difficulties of using crash rates in normalizing for volume differences 

between the before and after periods. 

The analysis of economic effects fundamentally involved the development of per-crash cost 

estimates for different crash types and police-reported crash severities. In essence, the 

application of these unit costs to the EB crash frequency effect estimates. The EB analysis was 

first conducted for each crash type and severity and site before applying the unit costs and 

aggregating the economic effect estimates across crash types and severity and then across 

jurisdictions. The estimates of economic effects for each site allowed for exploratory analysis 

and regression modeling of cross-jurisdiction aggregate economic costs to identify the 

intersection and RLC program characteristics associated with the greatest economic benefits of 

RLC systems. 

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) Unit costs were developed for angle, rear end, and 

“other” crashes at urban and rural signalized intersections. The crash cost to be used had to be 

keyed to police crash severity based on the KABCO3

 scale. By merging previously developed 

costs per victim keyed on the AIS injury severity scale into U.S. traffic crash data files that 

 

3 The KABCO severity scale is used by the investigating police officer on the scene to classify injury 

severity for occupants with five categories: K, killed; A, disabling injury; B, evident injury; C, possible 

injury; O, no apparent injury. (7) These definitions may vary slightly for different police agencies.
3 

scored injuries in both the Abbreviated Injury Scale (AIS) and KABCO scales, estimates for both 

economic (human capital) costs and comprehensive costs per crash were produced. In addition, 

the analysis produced an estimate of the standard deviation for each average cost. All estimates 

were stated in Year 2001 dollar costs. 

Data Collection 

 

The choice of jurisdictions to include in the study was based on an analysis of sample size needs 

and the data available in potential jurisdictions. It was vital to ensure that enough data were 

included to detect that the expected change in safety has appropriate statistical significance. To 

this end, extensive interviews were conducted for several potential jurisdictions known to have 

significant RLC programs and a sample size analysis was done. The final selection of seven 

jurisdictions was made after an assessment of each jurisdiction’s ability to provide the required 

data. The jurisdictions chosen were El Cajon, San Diego, and San Francisco, CA; Howard 

County, Montgomery County, and Baltimore, MD; and Charlotte, NC. 

Data were required not only for RLC-equipped intersections but also for a reference group of 

signalized intersections not equipped with RLCs but similar to the RLC locations. These sites 

were to be used in the calibration of safety performance functions (SPFs) used in the EB analysis 

and to investigate possible spillover effects. To account for time trends between the period 

before the first RLC installation and the period after that, crash and traffic volume data were 

collected to calibrate SPFs from a comparison group of approximately 50 unsignalized 

intersections in each jurisdiction. 

Following the site/jurisdiction selection, the project team collected and coded the required data. 

Before the actual data analyses, preliminary efforts involving file merging and data quality 

checks were conducted. This effort included the crash data linkage to intersections and the 

defining of crashes expected to be affected by RLC implementation. Basic red-light-running 

crashes at the intersection proper were defined as “right-angle,” “broadside,” or “right- or leftturning-crashes” involving two vehicles, with the vehicles entering the intersection from 

perpendicular approaches. Crashes involving a left-turning vehicle and a through vehicle from 

opposite approaches were also included. “Rear end crashes” were defined as a rear end crash 

type occurring on any approach within 45.72 m (150 ft) of the intersection. In addition, “injury 

crashes” were defined as including fatal and definite injuries, excluding those classified as 

“possible injury.” 

Results 

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. A significant decrease in right-angle crashes was found, but 

there is also a significant increase in rear end crashes. Note that “injury” crashes are defined by 

severity as K, A, or B crashes; but the frequencies shown do not contain a category for “possible 

injury” crashes captured by KABCO-level C; thus, these crashes could better be labeled “definite 

injury” crashes. 
4 

These effects, the direction if not the order of magnitude, were remarkably consistent across the 

jurisdictions. The analysis indicated a modest spillover effect on right-angle crashes; however, 

that this was not mirrored by the increase in rear end crashes seen in the treatment group, which 

detracts somewhat from the credibility of this result as evidence of a general deterrence effect. 

For the analysis of economic effects, it was recognized that there were low sample sizes of fatal 

and serious (A-level) crashes in the after period for some intersections. In addition, the initially 

developed cost estimates for B- and C-level rear end crashes indicated some anomalies in the 

order (e.g., C-level costs were higher, very likely because on-scene police estimates of “minor 

injury” often ultimately include expensive whiplash injuries), the B- and C-level costs were 

combined by Pacific Institute for Research and Evaluation (PIRE) into one cost. Considering 

these issues 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 

Noninjury (O). 

The study summarizes results for the economic effects including and excluding property-damage 

only (PDO) crashes. The latter estimates are included in recognition of the fact that several 

jurisdictions considerably under-report PDO collisions. Those estimates (with PDOs excluded) 

show a positive aggregate economic benefit of more than $18.5 million over approximately 370 

site years, which translates into a crash reduction benefit of approximately $50,000 per site year. 

With PDOs included the benefit is approximately $39,000 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. 

Further analysis indicated that right-angle crashes appear slightly more severe in the after period 

in two jurisdictions, but not in the other five. Because such an effect would cause a slight 

overestimation in economic benefits, an attempt was made to estimate the possible size of the 

benefit reduction. If such a shift were real, and if its effects could be assumed to be correctly 

estimated from individual KABCO unit costs already deemed to be inappropriate for such 

purposes, the overall cost savings reported could be decreased by approximately $4 million; 

however, 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 indicated 

substantial variation, much of which could be attributable to randomness. It was reasonable to 

suspect that some of the differences may be due to factors that impact RLC effectiveness; 

therefore, a disaggregate analysis, which involved exploratory univariate analysis and 

multivariate modeling was undertaken to try to identify factors associated with the greatest and 

least economic benefits. The outcome measure in these models was the aggregate economic 

effect per after period site year. 

The disaggregate analysis found that greatest economic benefits are associated with the highest 

total entering AADTs, the largest ratios of right-angle to rear end crashes, higher proportions of 

entering AADT on the major road, shorter cycle lengths and intergreen periods, and with the 

presence of protected left-turn phases. The presence of warning signs and high publicity levels 
5 

also appear to be associated with greater benefits. These results do not provide numerical 

guidance for trading off the effects of various factors. The intent of identifying these factors is 

that in practice RLC implementers would identify program factors such as warning signs that 

increase program effectiveness and 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 to rear end 

crashes). 

Conclusions 

This statistically defendable study found crash effects that were consistent in direction with those 

found in many previous studies, although the positive effects were somewhat lower that those 

reported in many sources. The conflicting direction effects for rear end and right-angle crashes 

justified the conduct of the economic effects analysis to assess the extent to which the increase in 

rear end crashes negates the benefits for right-angle crashes. This analysis, which was based on 

an aggregation of rear end and right-angle crash costs for various severity levels, showed that 

RLC systems do indeed provide a modest aggregate crash-cost benefit. 

The opposing effects for the two crash types also implied that RLC systems would be most 

beneficial at intersections where there are relatively few rear end crashes and many right-angle 

ones. This was verified in a disaggregate analysis of the economic effect to try to isolate the 

factors that would favor (or discourage) the installation of RLC systems. That analysis revealed 

that RLC systems should be considered for intersections with a high ratio of right-angle crashes 

to rear end crashes, higher proportion of entering AADT on the major road, shorter cycle lengths 

and intergreen periods, one or more left turn protected phases, and higher entering AADTs. It 

also revealed the presence of warning signs at both RLC intersections and city limits and the 

application of high publicity levels will enhance the benefits of RLC systems. 

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 in which RLC installations took place over many years and 

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 seems to be required. 

In closing, this economic analysis represents the first attempt in the known literature to combine 

the positive effects of right-angle crash reductions with the negative effects of rear end crash 

increases and identify factors that might further enhance the effects of RLC systems. Larger 

crash sample sizes would have added even more information. The following primary 

conclusions are based on these current analyses: 

1. Even though the positive effects on angle crashes of RLC systems is partially offset by 

negative effects related to increases in rear end crashes, there is still a modest to moderate 

economic benefit of between $39,000 and $50,000 per treated site year, depending on 

consideration of only injury crashes or including PDO crashes, and whether the 

statistically non-significant shift to slightly more severe angle crashes remaining after 

treatment is, in fact, real. 
6 

2. Even if modest, this economic benefit is important. In many instances today, the RLC 

systems pay for themselves through red-light-running fines generated. However, in many 

jurisdictions, this differs from most safety treatments where there are installation, 

maintenance, and other costs that must be weighed against the treatment benefits. 

3. The modest benefit per site is an average over all sites. As the analysis of factors showed, 

this benefit can be increased through careful selection of the sites to be treated (e.g., sites 

with a high ratio of right-angle to rear end crashes as compared to other potential 

treatment sites) and program design (e.g., high publicity, signing at both intersections and 

jurisdiction limits). 

Statistical information related to this Executive Summary can be found in tables 1 through 4 in 

this final report. 
7 

II. INTRODUCTION 

A red-light-camera (RLC) system automatically detects when a vehicle has entered an 

intersection during the red phase for an approach and takes a photograph of the red-light-running 

violation. Jurisdictional staff members review photographs to determine if a ticket should be sent 

to the driver. 

This treatment (i.e., crash countermeasure) is aimed at helping reduce a major safety problem at 

urban and rural intersections. Red-light-running is estimated to produce over 95,000 crashes and 

approximately 1,000 deaths per year.(1) After being used extensively overseas for over a decade, 

the use of RLC systems has risen dramatically in the U.S. in recent years. Given the size of the 

problem, and the promise shown from the use of RLC systems in other countries (and by some 

studies in the U.S.), there is a clear need to determine the effectiveness of the RLC systems in 

reducing crashes at monitored intersections as well as jurisdiction-wide. There is also a need to 

determine if such programs can be made more cost-effective through changes in such variables 

as signage, signal phasing, and public information programs. Studies conducted in over a dozen 

U.S. cities and several foreign countries indicate that RLC programs are effective in reducing the 

number of red-light-running violations. However, there is much less evaluation-based 

knowledge on the effect of RLC programs on crashes, especially the kinds of crashes typically 

caused by red-light running. 

While less controversial than speed-enforcement camera programs, RLC programs are not 

entirely without controversy. Even though an apparent safety benefit has been indicated in some 

studies, such programs have advocates and detractors at both local community levels and as high 

as the U.S. Congress. The debate is fuelled by the reality that no well-conducted, scientificallysound, multijurisdiction evaluation of RLC program effectiveness in reducing crashes has been 

undertaken. 

To meet this need, the Federal Highway Administration (FHWA) ITS (intelligent transportation 

system) Joint Program Office (JPO) requested submission of an RLC evaluation proposal under 

the ITS (intelligent transportation system) Program Assessment Support contract. The resulting 

contract supported the work described in this report. 

9 

III. PROJECT OVERVIEW 

As shown in figure 1, the work was conducted in two phases. Phase I involved the development 

of a detailed experimental design. This was reviewed and subsequently approved by FHWA 

with minor modifications. In Phase II, the design was then implemented. This report describes 

both of these phases in detail. 

Figure 1. Project workflow. 

Establish Study Goals 

with FHWA panel 

Analyze Data 

Prepare Reports 

Collect and Code 

Data From 

Jurisdictions 

Develop New Crash 

Cost Estimates 

Define Study Methodology for 

Crash Frequency-Based Study 

Define Study Methodology 

for Economic Cost Study 

Collect Background Information From 

Literature and Other Sources 

Interview Potential 

Jurisdictions 

Choose Study 

Jurisdictions 

Phase I Phase II 
10 

Phase I—Evaluation Design 

As shown in figure 1, the Phase I effort involved multiple steps to establish goals for the 

evaluation, collection of background information that would help shape the evaluation plan, 

choice of the jurisdictions to be involved in the evaluation, and definition of the data collection 

and statistical techniques to be used. Following are the tasks in the Phase I experimental design: 

• Conduct literature review—The study team specified, obtained, and produced detailed 

critical reviews of key U.S. and international studies. The intent of this review was not 

only to summarize what is known concerning program effectiveness, but also to identify 

the critical experimental design factors that can overcome problems found in past studies. 

• Determine study questions—The study team worked with the oversight panel established 

by FHWA to define and prioritize the study questions to be answered by the experimental 

design. The primary effort was in a workshop held early in the contract period, and the 

preliminary results of the literature review were used in the decisions. 

• Determine RLC-related data availability—The project team conducted telephone 

interviews with 15 local agencies that have significant RLC programs in place. The most 

important questions to be answered concerned not only the details of the RLC program 

(e.g., number of intersections, violation definition, level of fine), but also the quality and 

availability of data related to before-and after-program crashes, signal data for treated and 

untreated intersections, traffic flows, and public information efforts. 

• Specify a multijurisdictional experimental design to estimate RLC safety effects—Based 

on the outputs from the preceding tasks, the study team defined the multijurisdictional 

experimental plan, including data needs, the most appropriate local agencies to work 

with, data analysis techniques, and the specification of which of the desirable study 

questions might be answerable. 

• Specify an experimental plan for examining the effects of RLC on economic costs of 

related crashes—Because RLCs are likely to decrease more severe angle crashes and 

increase less severe rear end crashes, the study team also defined a study method which 

can combine both crash frequency and severity in terms of economic costs. 

As the task list shows, there were actually two experimental plans or methodologies developed in 

this Phase I effort. The first is a national design in which data from multiple jurisdictions across 

the United States were used to both define the crash-related effectiveness of RLC programs and 

to explore factors that could make such programs more beneficial in terms of crash reduction 

(e.g., signalization variables such as clearance intervals and cycle lengths, and public information 

programs and signage related to RLC programs). The primary outcome variable of interest here 

is RLC-related change in the frequencies of different types of crashes. The second experimental 

plan involved the development of a database and methodology to examine, in economic terms, 

costs and benefits of RLCs, thus simultaneously analyzing changes in crash severity and crash 

frequency. The following sections describe the development of this economic analysis 
11 

methodology initiated in Phase I and revised and completed in Phase II; The full development 

process is described in the Phase II section. 

Phase II—Evaluation Implementation 

After FHWA agreed with the Phase I evaluation design, funding was provided for the 

implementation of the evaluation efforts. The project team then performed the following steps: 

• Collect and code data from jurisdictions—Project staff established contacts with and 

visited the seven jurisdictions chosen for participation. Staff collected data related to 

intersection geometry, traffic flow, signalization parameters, and other descriptive data 

for treatment, signalized reference, and unsignalized reference intersections in each 

jurisdiction. The data, almost always in noncomputerized form, were then collected for 

data extraction, coding, and analysis file preparation. Because crash data for a multiyear 

before-period were not available in six of the seven jurisdictions, crash data were located 

and extracted from State data files. 

• Develop new crash cost estimates—The final economic analysis methodology required 

the development of human capital and comprehensive cost estimates for each crash 

severity level in each pertinent crash type (e.g., the cost of a “moderate injury” in a rear 

end collision at a signalized intersection). Because FHWA cost data had not been 

updated since 1994, and because even those older data were not specific to crash and 

location types, a subcontractor with extensive experience in highway safety economics 

joined the team to develop these estimates. 

• Analyze data—Following the national-study design and the crash-cost methodologies 

developed in Phase I, the project team analyzed both RLC-related crash frequency and 

crash cost in each of the seven jurisdictions. The effectiveness estimates from all 

jurisdictions were then combined to develop a national estimate of the effectiveness of 

RLC system. 

• Prepare reports—Based on the analysis results, project staff then prepared this FHWA 

final report, an FHWA technical summary, and two Transportation Research Board 

(TRB) journal articles (one on the crash effects analysis, the other on the analysis of 

economic effects and the identification of factors associated with the greatest economic 

benefits).(2,3)

The narrative following the literature review will describe each of the steps in both Phase I and 

Phase II in more detail. 

13 

IV. LITERATURE REVIEW OF CRITICAL STUDIES 

Unlike many literature reviews for safety research efforts, the goal of this task was not to review 

a large number of studies to summarize findings on RLC program effectiveness. Because the 

overall Phase I goal was to produce a scientifically sound experimental plan that could overcome 

as many threats to validity as possible, the literature review was aimed at a shorter list of 

international studies judged by the study team and the oversight panel to be critical studies. To 

this end, all possible studies of relevance were first identified on the basis of Internet searches 

such as Transportation Research Information Services (TRIS), and information from parallel and 

recent reviews and meta-analyses conducted for FHWA, the National Cooperative Highway 

Research Program (NCHRP), and the Insurance Institute for Highway Safety. The final choice of 

critical studies included studies from both the United States and other countries with a longer 

history of RLC program implementation, studies that appeared to be best in terms of scientific 

rigor, and studies often cited by other researchers or in political discussions of RLC 

effectiveness. The team scanned a number of study sources and reports and ultimately defined a 

listing of 17 critical studies. 

A study team member then reviewed each of these studies in detail. The goal was to not only 

extract information on measured RLC program effectiveness, but also identify problems or 

issues that we would attempt to overcome in this new evaluation design. To accomplish this, 

listings of study strengths and weaknesses were developed for each study reviewed. 

In the sections that follow, a general summary of the literature findings is presented first, 

followed by an itemization of the lessons learned from this exercise. 

Summary of Findings 

The studies reviewed varied widely, including the following areas: 

• Accident types (all, right-angle, those caused by red-light running). 

• Accident severities (all, injury plus fatal, weighted). 

• Area of study (treated intersections, treated approaches, jurisdiction-wide). 

• Use and designation of comparison sites. 

• Treatment type (cameras only, cameras plus warning signs, red-light-running and speed 

cameras). 

• Sample sizes, ranging from 3 to 78 camera-equipped intersections. 

• Countries (several from Australia and the United Kingdom, but few from the United 

States). 

• Study methodology (simple before-and-after, before-and-after with comparison group, 

chi-squared tests, statistical modeling). 
14 

It is not surprising that estimates of the safety effect of cameras vary considerably. A summary 

of the more relevant study findings is provided in table 1, including a synopsis of the main 

difficulties. 

From table 1, one could conclude that the bulk of the results support a conclusion that red-light 

cameras reduce right angle crashes and could increase rear end crashes; however, as the last 

column shows, most studies are tainted by methodological difficulties that raise questions about 

any conclusions from them. One difficulty, failure to account for regression to the mean, can 

exaggerate the positive effects, while another, ignoring possible spillover effects at intersections 

without RLC, will lead to an underestimation of RLC benefits, even more so if sites with these 

effects are used as a comparison group. (“Spillover effect” is the expected effect of RLCs on 

intersections other than the ones actually treated, resulting from jurisdiction-wide publicity and 

the general public’s lack of knowledge of where RLCs are installed.) Almost all studies had one 

or the other of these flaws and many had both, in addition to other flaws. 
Table 1. Summary of findings from past studies. 

Reference City Camera 

sites 

Comparison/ reference group 

Crash type studied and estimated effects (negative indicates reduction) 

Com

Right-angle and leftturn opposed Hillier, et al. -50% (1993)(8) Sydney, Australia Installed at 16 

intersections 

16 signalized intersections 

Rear end +25% 

to 60%

RTM

may compconfoto sigof stu

South, et al. (1988)(9) 

Melbourne, Australia 

Installed at 46 intersections 

50 signalized intersections 

No significant results. Looked at right angle, right-angle (turn), right against thru, rear end, rear end (turn), other, all crashes, number of casualties, no significant results 

RTM

accotraffic compaffecother 

Andreassen (1995)(10) 

Victoria, Australia 

 No significant results Lack 

that ttendelight-accidsites affec

Kent, et al. (1995)(11) 

Melbourne, Australia 

3 intersection approaches at different intersections 

Noncamera approaches 

No significant relationship between the frequency of crashes at RLC and nonRLC sites and differences in red-lightrunning behavior 

Crossproblspillononcthe s

Mann, et al. (1994)(12) 

Adelaide, Australia 

Installed at 13 intersections 

14 signalized intersections 

Reductions at the camera sites were not statistically different from the reductions at the comparison sites 

RTM

compissue

London Accident Analysis Unit (1997)(13) 

London, U.K. RLC at 12 

intersections and 21 speed cameras 

Citywide effects examined 

No significant results The r

confoprogr

15 
Table 1. Summary of findings from past studies. Continued

Reference City Camera 

sites 

Comparison/ reference group 

Crash type studied and estimated effects (negative indicates reduction) 

Com

Hooke, et al. (1996)(14) 

Various cities in England and Wales 

Installed at 78 intersections 

 All injury -18% A sim

compfor efRTM

chanis limresul

Ng, et al. All -7%(1997)(15) Singapore Installed at 42 

intersections 

42 signalized intersections Right angle -8%

RTM

at coissue

All -7%All injury -29%Right angle -32%Right-angle injury -69%

Retting and Kyrychenko (2001)(16) 

Oxnard, CA Installed at 

11 intersections 

Unsignalized intersections in Oxnard and signalized intersections in 3 similarly sized cities 

Rear end +3% 

(nonsignificant)

Looknot ju29 mafter 

Angle—all approaches -37%Angle—camera approaches 

-60%

All—camera approaches 

-19%

Rear end—camera approaches 

+4%

SafeLight, Charlotte(17) 

Charlotte, NC Installed at 

17 intersections 

no comparison group 

All < -1%

Probaselec

Rear end -32%Right angle -42%

Maryland House of Delegates (2001)(18)

Howard County, MD 

Installed at 25 intersections Other -22%

Probaselec

16 
Table 1. Summary of findings from past studies. Continued

Reference City Camera sites Comparison/ 

reference group 

Crash type studied and estimated effects (negative indicates reduction) 

Com

Fleck and Smith (1998)(19) 

San Francisco, CA 

Installed at 6 intersections 

Citywide effects examined 

Citywide injury collisions caused by red-light violators; unclear how these were defined 

- 9% Que

RLC examat tr

Total crash rates—crashes per million entering vehicles at each intersection Combined-treatment quadrant 

- 15.9%

Photo-radar quadrant - 7.5%RLC quadrant - 9.7%

Vinzant and Tatro (1999)(20) 

Mesa, AZ 6 intersections 

with RLC only, 6 intersections with RLC plus photo speed enforcement 

6 signalized intersections 

Control quadrant - 10.7%

It is assitreatthe frand

Crossing carelessly - 54.0%Unsafe right turn - 29.0%Failure to keep distance + 8.0%Other - 29.0%

Fox (1996)(21) Glasgow, 

Scotland 

Installed at 8 intersections and 3 “pelican” crossings 

Area wide effects on injury crashes examined 

All per month - 32.0%

RTM becanon-greadecrdifficcitywcam

Winn (1995)(22) Glasgow, Scotland 6 locations on 1 approach Various Injury crashes related to RLR violations - 62.0% Prob

*

 RTM = Regression to the mean, also called “bias by selection.” 

17
18 

A similar assessment of the literature was made independently in a recent meta-analysis, in 

which the review for the Insurance Institute for Highway Safety included most of the same 

studies cited in table 1 and some others.(23) That work found, expectedly, that largest safety 

benefits were reported by studies that did not control for regression to the mean and that small 

effects tend to be found where the possibility of spillover was ignored. The one study that 

measured both spillover and specific effects, while ensuring that regression to the mean was not 

a factor, was an evaluation of the Oxnard, California program by the Insurance Institute for 

Highway Safety.(16) That study found a significant reduction in injury crashes overall but did not 

separate the specific effects at treatment sites from citywide effects. (It is understood that a 

follow up study is doing this.) 

While it is difficult to make definitive conclusions from studies that generally fail the tests on the 

validity of the methodology, the results did provide some level of comfort for a decision to 

conduct a definitive large-scale study of U.S. installations. It was important, however, that the 

planned study capitalize on lessons learned from the strengths and weaknesses of the previous 

evaluations, many of which were conducted in an era when knowledge of potential pitfalls in 

evaluation studies and methods of avoiding or correcting them was not widespread. These 

lessons are reviewed next. 

Lessons Learned and Issues Raised by Literature Search 

From the literature review, a number of lessons were learned that were useful in designing a 

definitive U.S. study. Following is an itemization: 

• Number of treatment sites: This was limited in many studies, making for low significance of 

many results. A definitive study will have to pay careful attention to sample sizes. 

• RLC “spillover effects” in same city: Crashes could be affected at control/comparison sites, 

making it necessary to have such sites in other similar cities. This will also make it difficult 

to determine the effect at treated location versus all other locations in the same city, perhaps 

requiring a study design without a strong reliance on the use of comparison sites. 

• Differences in accident investigation and reporting practice between jurisdictions: This will 

make intracity comparisons or amalgamating data difficult and may require a separate 

analysis of the more severe crashes that are less likely to be affected by these differences if 

amalgamation is necessary to achieve large enough sample sizes. 

• Defining “red-light-running crashes”: The lack of precise definition in past studies, the lack 

of clarity between angle and turning crashes on police forms, and the lack of information on 

“legal” right-turn-on-red crashes could cloud the definition of the outcome variable. 

• RLC effects on rear end crashes: There is clearly a need to consider not only this crash type 

in the analysis, but also to account for the tradeoff in crash severity between right angle and 

rear end types. To do so requires the use of the economic cost of crashes as an outcome 

variable. 

• Exposure changes between before-and-after periods: Exposure is the major determinant of 

intersection crashes. Therefore, it is important to account for any changes between the before 
19 

and after period, particularly if these changes are triggered by the measure. All studies 

reviewed have failed to do this accounting, conveniently assuming that RLCs will not change 

exposure. It is also important to use proper methods for accounting for exposure changes 

because the conventional method of normalizing with crash rates (per unit of traffic volume) 

is a dubious one, given the non-linear relationship between crashes and exposure found in 

many cases. 

• Regression to the mean effects: In almost all studies reviewed, RLCs were installed at 

intersections with a high incidence of crashes, particularly those likely to be affected by 

RLC; this can lead to significant regression to the mean, particularly in the positive effects on 

RLC-targeted crashes, which must be accounted for. 

• Yellow interval improvements (and other intersection improvements) made at time of 

installation of RLCs: This makes it difficult to determine what caused the measured effect. It 

especially important to separate the effects of these other measures from that of RLC because 

some studies have shown that these other treatments can be just as effective as RLC. Thus 

the study should not resolve the difficulty of the confounding effects of other measures by 

avoiding affected sites, but should intentionally seek to include some of these sites if 

possible. 

• Disaggregate effects by signalization variables: There is little knowledge on the effect of 

variables such as cycle length and yellow and all-red interval combinations. Such knowledge 

will be useful in planning RLC programs or in explaining differential effects across sites and 

jurisdictions. The ability to seek this knowledge will be affected by the size of the sample 

and the variation in these factors. 

• Effect of signage: Warning the driver at a specific location may or may not change the effect, 

and may or may not limit the possible treatment effect on other intersections. Signing and 

enforcing one approach has been reported to have a benefit for all other approaches in some 

studies, but some studies have found otherwise. The issue will require further investigation, 

which will require that appropriate data be collected. 

• Public education level: It is desirable that this be specified and measured and its effects 

evaluated, in the light of other enforcement research that has shown the importance of 

combining enforcement with public information (PI) programs. 

• Type of ticketing: This affects true enforcement level and driver perception of “cost” of a 

ticket. The level of the fine and whether there are driver points for a violation can affect 

outcome, and the requirement to ticket the driver (rather than owner) can mean that only low 

percentage of offenders (i.e., 25 percent in San Francisco) are ticketed. This issue requires 

resolution, which will require that appropriate data be collected. 

• Definition of Red-Light violation: This could affect ticketing and public perception. Short or 

long “grace period” after signal turns red could have different effects. It would be desirable 

to isolate these differential effects, if possible. 
20 

• Camera rotation: Determining if one can have a greater effect with the same number of 

cameras rotated to more sites is important. However, the optimum amount of rotation is 

undefined, so it might be desirable to develop this knowledge. 

• Relationship between changes in violations and changes in crashes: This will depend on 

many factors including the grace period chosen, driver versus owner, etc. To date, there is no 

knowledge on such a relationship. Establishing a link would be useful in that it would 

considerably simplify the task of evaluating RLC installations. This, however, will likely 

require a prospective study. 

These “lessons learned” were then incorporated into the experimental designs for both the crashfrequency-based study and the economic analysis study covered in later sections of this report.
21 

V. DETERMINATION OF STUDY QUESTIONS TO BE ANSWERED 

The core study question to be answered is, “What effect does RLC programs have on intersection 

safety, as measured by changes in crashes?” The evaluation design presented later has this 

question as its primary focus. However, even this question requires some further clarification 

and expansion, in that RLC installations at selected intersections in a jurisdiction are often part of 

a larger “Reduce Red-Light-Running” campaign that is jurisdiction-wide. The overall program 

will include a public information component (whether planned or just as media coverage of this 

new enforcement technique) that can clearly have an effect on driving behavior at other 

intersections in the jurisdiction. Thus, the core question is immediately expanded to, “What are 

both the local effect of RLCs at treated intersections, and the ‘spillover’ effect at nearby 

intersections or jurisdiction-wide?” In addition, there are other program components or factors 

that might make such programs more beneficial in terms of crash reduction. Examples could 

include yellow interval or phasing changes done with camera installation, public information 

programs and signage related to RLC programs, and the issue of whether to ticket vehicle owners 

or vehicle drivers. 

Because not every conceivable question can be answered in one evaluation, particularly a 

retrospective evaluation in which programs and data have been determined by the local agency 

rather than the evaluator, there was a need to clearly establish a list of key study questions to 

focus on (if the data would allow it). The FHWA established an internal project oversight panel 

to make these decisions. Forrest M. Council of BMI-SG and Bhagwant Persaud of Ryerson 

University, the project co-principal investigators, met with this panel in early January, 2002. 

Panel members attending that meeting included the following. 

Michael Griffith, Chair, FHWA Office of Safety Research and Development 

Pam Crenshaw, FHWA Office of Operations, Travel Management 

Pat Hasson, FHWA Resource Center 

Hari Kalla, FHWA Office of Safety, Safety Design 

John McFadden, FHWA Resource Center 

Joe Peters, ITS Joint Program Office 

Amy Polk, NASA Jet Propulsion Laboratory

Greg Hatcher, Mitretek Systems, Inc. 

Rob Maccubbin, Mitretek Systems, Inc. 

The panel and project team discussed items such as issues related to the literature review (e.g., 

defining “critical studies,” proposed study list, current progress, example detailed reviews), a 

listing of “lessons learned/issues raised” from the literature reviewed to date, preliminary 

thoughts on the experimental plan, etc. A major part of the discussion centered on the listing of 

study questions to be answered. At that initial meeting, the Chair and panel defined a draft 

listing of questions. This was revised slightly into the following final listing based on findings 

concerning available data and the range or spread of the data among the jurisdictions. 
22 

First-Level Priority: 

• What effect do RLCs have on intersection safety (i.e., intersection crashes) at monitored 

intersections versus intersection safety throughout the jurisdiction? 

• What is the relationship of signal timing (i.e., length of the yellow interval, length of the 

all-red interval, and various combinations of the yellow interval and all-red interval) with 

safety at intersections with RLCs? Later discussion indicated that the key factors of 

interest are yellow interval, all-red interval, cycle length, and signal coordination. The 

basic issue related to yellow time is the nature of the yellow phase length—e.g., a 

standard length, length based on Institute of Transportation Engineers (ITE) 

recommendations related to approach speeds and other factors, or some variation of 

these. The basic question for all-red phases is whether or not there is one (i.e., presence 

or absence of all-red phase). Cycle length is needed both to provide some measure of the 

number of red phases (and thus the number of opportunities for red-light-running) in a 

given time period, but also because longer red phases might “induce” more red-lightrunning. With respect to signal coordination, the issue is whether the treated signal 

approach is part of a set of coordinated signals that lead to queuing of vehicles (but not 

any additional details of the level of coordination). 

• Are there certain improvements (e.g., signal timing, signage, geometric changes, etc.) 

done in conjunction with RLC installation that make the automated enforcement program 

more or less effective? Later discussion of the signage issue indicated that the key 

question has to do with presence or absence of “warning” signage, whether the sign is 

located at the intersection or away from the intersection (e.g., at the edge of town or at the 

beginning of a corridor), and whether informational signs providing data to the public on 

the number of red-light-running violations that have been issued are installed (because 

such signs have been shown to increase the effect of seatbelt enforcement programs and 

perhaps RLC programs in some cities). 

• If other improvements are made during the installation of RLCs, what portion of the 

change in intersection crashes is due to these improvements and what portion is due to 

the RLC? 

• What effect does a “good” public information program have on safety at intersections 

with RLCs? Given the fact that public information has been shown to be an important 

part of other effective enforcement programs (e.g., belt-use programs and driving under 

the influence (DUI) roadblocks), there is a need for some measure of “public information 

program level” for the cities. As noted later, the city interview form did contain three 

such levels. 

Second-Level Priority: 

• What is the effectiveness of “fine only” (i.e., owner liability) program versus “fine and 

points” (i.e., driver liability) program? 
23 

• What is the relationship between the reduction in violations and the reduction in crashes? 

(Note that this issue is not covered in the proposed experimental design because it would 

require a somewhat different study approach; crash effects is the primary focus.) 

• What is the relationship of the grace period (i.e., ticketing threshold—how far into the red 

phase before a ticket is issued) and location of the camera activation loops with safety at 

intersections with RLCs? (Note that this was later removed as a question of interest 

because the “lag time” is a function of how the camera system is installed along with the 

legal definition of a violation. Because the driver is not aware of the extent of the “grace 

period,” it is no longer felt to be of significant interest.) 

25 

VI. DETERMINATION OF RLC-RELATED DATA 

AVAILABILITY 

Part of the difficulty in defining specific study questions early in the project was the lack of 

information on data availability and range for the different issues. While the project team (and 

the FHWA panel) had some insight into what data might be available on a State-controlled 

roadway system, there was less knowledge about what would be available in city or county data 

systems. For this reason, a decision was made to interview a small sample of key cities across 

the U.S. to address these questions. 

The “sampling frame” for these interviews was based on a November 2002, list provided by the 

ITS Joint Program Office. As part of their periodic survey of ITS deployment in 78 of largest 

metropolitan areas in the U.S., a set of questions asked about current and projected numbers of 

intersections where RLC systems were installed. An additional listing of jurisdictions with RLC 

programs was extracted from a Web page listing developed by the Insurance Institute for 

Highway Safety. Additional cities were added based on panel knowledge and on a separate 

study where RLC programs were being surveyed. 

The initial idea was to conduct a survey of a random sample of jurisdictions in this combined 

listing to determine, in general, what data might be available. (Note that only limited funds had 

been set aside for this task at the inception of the project.) However, after the detailed interview 

form was developed and tested, it became apparent that the interview would require 30–50 

minutes of time with pertinent city/county staff, that it was often difficult to identify and reach 

the appropriate staff (even with the ITS contacts on the JPO listing), and that staff in more than 

one city/county agency would usually have to be contacted due to the cross-agency nature of this 

program (i.e., both traffic and police staff). For these reasons, a decision was made to interview 

cities with 10 or more RLCs already in operation. Emphasis was also placed on cities that had a 

significant number of such treated locations in place in 1999 or earlier—sites that would be 

useful in a retrospective study requiring at least a 2-year “after” period. While “nonrandom,” 

this provided information on cities that were potential future participants in the actual evaluation. 

Within the time frame and budget, project staff interviewed 15 jurisdictions. 

• Chandler, AZ. 

• Prince George’s County, MD. 

• Fairfax County, VA. 

• El Cajon City, CA 

• Montgomery County, MD. 

• New York City, NY. 

• City of San Diego, CA. 

• San Francisco, CA. 

• Boulder, CO. 

• Sacramento City, CA 

• Charlotte, NC. 

• Arlington County, VA. 

• Howard County, VA. 
26 

• Baltimore, MD. 

• Greensboro, NC. 

While most of these interviews were completed by phone (as were follow up questions), two 

jurisdictions chose to complete the interview in writing. This led to some missing data for a 

limited number of questions. However, in general, the interview process did produce detailed 

data on both the nature of the RLC program and the availability of various forms of data in each 

jurisdiction. (The interview form and a summary of the results are available from the authors on 

request.) The results of the interviews will be provided at the end of the following section. 
27 

VII. METHODOLOGY FOR NATIONAL, MULTIJURISDICTION 

STUDY 

The experimental design for the national study was based on the study questions identified by the 

oversight panel, the noted issue-related findings from the literature review, the findings from the 

jurisdiction interviews, and the initial Phase I Scope of Work (SOW). Following is a list of 

factors, taken verbatim from the SOW, followed by a summary of the project team 

recommendation for consideration, which were incorporated in the final evaluation plan: 

• Sample size requirements—How many treated sites (number of signalized intersections 

equipped with red-light cameras) are required? 

Recommendation 1: In the proposed experimental plan, the project provided sample size 

estimates based on assumptions of crash counts and safety effects. However, the project 

team believed that it may be more important to ask, “What can be achieved, that is, what 

effects can be detected and at what significance levels, with the data that are likely to be 

available?” These estimates were also provided. 

• How many other types of sites (comparison or reference sites) are required to distinguish 

between what is the effect of the treatment and what is the effect of other factors such as 

changes in traffic flow, weather, and police reporting practices that may have also 

changed from the “before” to the “after” period? In other words, what technique will be 

used to estimate the number, severity, and types of crashes that could have occurred 

without the improvement? 

Recommendation 2: The multivariate empirical Bayes (EB) procedure was proposed for 

accounting for effects due to regression to the mean, traffic volume changes, and changes 

in other factors during the analysis period.(4) In this methodology, the comparison group 

is used as a reference group for estimating annual adjustments for the safety performance 

functions that are key to the method. The number of crashes in the reference group needs 

to be large enough for this purpose. It was estimated, based on experience and on 

guidelines in Harwood et al. (2000), that 100 crashes per year for each crash type would 

be required.(24)

• Spillover effect—There may be a large spillover effect of camera enforcement to 

intersections in the same community that are not equipped with cameras. The automated 

enforcement may provide general deterrence against red-light violations and crashes with 

effects not limited to specific intersections with cameras. 

Recommendation 3: The project team proposed that (a) this spillover effect be estimated 

separately from the specific deterrence effect and that it be related, if possible, to the 

proximity of a noncamera equipped intersection to an RLC site and (b) that untreated 

signalized intersections in the same community not be used as comparison sites to control 

for crash trends between the before and after periods. It was felt that these sites may be 

from neighboring communities or may be unsignalized intersections in the same 

jurisdiction. The use of signalized intersections from other communities raised issues of 
28 

differences in crash-reporting practices between cities so the unsignalized intersection 

option was preferable. However, it was anticipated that the use of unsignalized 

intersections as the comparison group would make the study somewhat more difficult due 

to the fact that while the major cities interviewed tend to have detailed traffic count data 

on signalized intersections, not all have an abundance of data on unsignalized 

intersections. 

• Types of crashes—It is desirable to obtain data that contain sufficient detail to identify 

crashes that were specifically red-light-running events. If it is not possible to categorize 

crashes specifically as red-light-running events, then the contractor should attempt to 

define red-light-running crashes as best as possible, given the data available. In addition, 

obtaining statistics for rear end crashes is necessary because they might increase under 

RLC enforcement due to increased driver compliance with stopping at red lights. 

Recommendation 4: Given the quality of available databases and that a retrospective 

study was required, it was felt that it would be impossible to achieve the desired goal of 

estimating the effects for crashes that were specifically red-light-running events. 

However, the project team believed that there are reasonable surrogates and proposed 

that, as a minimum, effects be estimated for left-turn opposed, right angle from adjacent 

approaches and, if possible, crashes in which a driver was charged for a red-light 

violation. Effects so estimated could be deemed to be conservative. It was also proposed 

that the effects on rear end crashes be separately estimated and that an assessment be 

undertaken of the net effects on crashes by considering the relative severities of the 

different crash types affected. 

• Selection bias—The accident history of an intersection during the "before" period is an 

important clue to what would have been its safety performance during the "after" period. 

However, that same accident history may also be one of the reasons that that particular 

intersection was selected for treatment. This factor makes prediction of safety 

performance during the "after" period subject to bias called "regression to the mean” 

(RTM). In addition, the RTM bias may be present even if locations were not selected 

because of an abnormally high or unusually low rate of crashes. 

Recommendation 5: Substantial evidence of this bias was found in previous evaluations. 

The interviews also substantiated that virtually all cities use “high crash counts” as a 

factor in choosing the RLC locations (as would be expected and logical). Therefore it 

was deemed critical that regression to the mean be properly accounted for using the most 

advanced state of the art methodology. To this end, in Recommendation (2), the EB 

methodology was proposed; this methodology has other attractive features, e.g., the fact 

that it mitigates the difficulties in more traditional methods of identifying a comparison 

group for accounting for regression to the mean. 

• Evaluation periods—The adoption of RLC enforcement is relatively recent in the United 

States; therefore, “after” periods will be short in most cases. The contractor should 

design a research study that will obtain as much “after” data as possible. 
29 

Recommendation 6: Naturally, the research study should be designed to obtain as much 

“after” period data as possible. At the same time the study should ensure that the after 

period is at least one year long and that it ends at a time when it is certain that all crashes 

occurring have entered the database. On a related issue, the ability to detect an effect also 

depends on the before period crash count. Fortunately, the EB methodology is such that 

much longer before periods can be used than the period of two to four years employed in 

conventional before-and-after comparisons. Unfortunately, the available “before” data in 

cities is not as extensive as would be the case for State-controlled rural roads. Of the 15 

cities interviewed, two have virtually no before-period data, three have 2 years, five have 

3–4 years, and three have more than 5 years. The remaining two did not know how many 

years of data were available. Thus, two-thirds have 3 years or more. 

The remainder of this section presents details of the design on the basis of these 

recommendations and data available in the jurisdictions interviewed. 

Study Design Details 

A detailed study design was proposed to the project oversight panel. The panel approved this 

plan after limited modification. The following text describes the design as ultimately 

implemented by the project team. 

Basic Objectives and Main Analytical Requirements 

The basic objective was to estimate the change in target crashes. Following is a list of possible 

target crash types: 

• Right-angle (side impact). 

• Left-turn (two vehicles turning). 

• Left-turn (one vehicle oncoming). 

• Rear end (straight ahead). 

• Rear end (while turning). 

• Other such as crashes specifically identified as red-light-running. 

These were estimated separately for two groups of sites: 

• Sites where cameras are used (specific deterrence effects). 

• Signalized intersections without cameras at various levels of proximity to the treatment 

sites. 

The preparation of a study design entailed both the preparation of a data collection plan and an 

analysis plan. The analytical requirements to provide the desired estimates drove the data 

collection needs. 

The analysis examined the safety effect of red-light-camera enforcement to provide insights into 

a number of issues, within the confines of available data. The data collection plan, discussed later 

in this report, provided insights into the capacity of the available data to address these issues. 
30 

Meeting the objectives and addressing the key issues placed the following list of special 

requirements on the data collection and analysis tasks: 

• Select a large enough sample size to detect, with statistical significance, what may be 

small changes in safety for some crash types. 

• Select carefully comparison sites or cities to ensure that safety at these sites is unlikely to 

be affected by the RLC installations. 

• Account properly for traffic volume changes. 

• Pool data from several jurisdictions to improve reliability of results and facilitate broader 

applicability of research products. 

Overview of the General Evaluation Methodology 

The general analysis methodology used is different from those used in the past, benefiting from 

significant advances made in the past 10 years in the methodology for the conduct of 

observational before-and-after studies, which culminated in a landmark book by Hauer.(4) That 

book also provides guidance on study design elements such as size and selection criteria for 

treatment and comparison groups and the pooling of data from diverse sources. All these are 

crucial elements in successfully conducting a study to obtain results that will have wide 

applicability. 

The evaluation considered the issues identified earlier on the basis of panel input and the 

literature review and survey to the extent that is practical. The inclusion of a variable in the 

analysis was ultimately resolved on the basis of whether relevant data could be obtained within 

the confines of the project, and whether obtainable sample sizes and the variation in levels of a 

variable were sufficiently large to isolate its effects (if any). 

The methodologies documented by Hauer range from simple before-and-after comparisons to the 

more powerful EB methodology.(4) The team proposed that the latter approach be pursued in 

seeking to overcome the difficulties associated with conventional before-and-after comparisons, 

while providing a fresh approach to overcome the limitations of previous evaluations of red-light 

cameras. Specifically, the analysis would: 

• Properly account for regression to the mean. 

• Overcome the difficulties of using crash rates in normalizing for volume differences 

between the before-and-after periods. 

• Reduce the level of uncertainty in the estimates of safety effect. 

• Provide a foundation for developing guidelines for estimating the likely safety 

consequences of contemplated RLC installation. 

• Properly account for differences in crash experience and reporting practice in 

amalgamating data and results from diverse jurisdictions. 

• Avoid the difficulties of conventional treatment-comparison experimental designs caused 

by possible spillover and/or migration effects to natural comparison groups. 
31 

In the EB approach, the change in safety for a given crash type at an RLC intersection is given 

by equation 1: 

 (1)

where π is the expected number of crashes that would have occurred in the after period without 

the cameras and λ is the number of reported crashes in the after period. 

In estimating π, the effects of regression to the mean and changes in traffic volume were 

explicitly accounted for using safety performance functions (SPFs) relating crashes of different 

types and severities to traffic flow and other relevant factors for each jurisdiction based on a 

reference group of signalized intersections without RLCs. Annual SPF multipliers were 

calibrated to account for the temporal effects on safety of variation in weather, demography, 

crash reporting and so on. Because of the possibility of spillover effects to the reference group of 

signalized intersections, it was decided to estimate the annual multipliers for the period after the 

first RLC installation from the trend in annual multipliers of SPFs calibrated for a comparison 

group consisting of unsignalized intersections in the jurisdiction. 

In estimating the SPFs a parameter k, which is a constant for a given model, is iteratively 

estimated with the use of a maximum likelihood procedure. (In that process, a negative binomial 

distributed error structure is assumed with k being the dispersion parameter of this distribution; 

the estimated value of k is the one that maximizes the likelihood of observing the crash counts, 

given the calibrated SPF.) See Hauer for more detail.(4)

Empirical Bayes Before-and-After Evaluation Example 

An illustration of the EB before-and-after evaluation methodology is provided next. Full 

theoretical details can be found in Hauer.(4) This example of the evaluation methodology is 

applied at a site and aggregate level. Note that the data presented are for illustrative purposes 

only, and do not represent data collected in this study. 

Data and SPFs: 

Consider an intersection at which RLC was implemented in September 2000. Refer to this as site 

(i). Suppose it is desired to estimate the effect of RLC on right angle injury crashes at this site. 

Suppose the SPF for right angle injury crashes for a given year y in the before period for this 

jurisdiction is: 

(2)

where "y is the calibrated multiplier for this jurisdiction for a given year using the recalibration 

procedure, and MAJAADTy and MINAADTy are respectively the major and minor road entering 

average annual daily traffic (AADT) in year y.
32 

For this illustrative example, it is assumed that the recalibration process has been completed, and 

that the values of "y are as given in table 6. That process also calibrated a value of 1.44 for the 

negative binomial distribution overdispersion parameter k that is used in the EB procedure. 

Crashes are available from 1996–2001 as shown in table 2. For illustrative purposes, assume that 

the yearly entering AADTs were either all available or some were estimated using a procedure in 

Lord.(25) The AADTs are also shown in table 2. 

Effect for Site (i): 

The calculations in table 2 are based on the methodology in Hauer.(4) They pertain to a single 

site (i) for which the results show that π(i) = 4.384 right angle injury crashes are expected in the 

after-period without treatment. Four such crashes (λ(i)) were recorded. 

The crash modification factor for this one site is, from Hauer:(4)

 (3) 

It means that the point estimate of the crash reduction is 100(1- 0.875) = 12.5%. 

Table 2 shows the AADTs for the EB evaluation example. 

Table 2. Summary of results for right-angle injury crashes at site (i). 

1996 1997 1998 1999 

Jan-Aug 

2000 

Oct-Dec 

2000 2001 

Crashes in year(X) 4 6 3 5 4 1 3 

Sum = Xb = 22 Sum = λ = 4 

MAJAADT 41302 42169 43460 43891 44321 42322 42875 

MINAADT 3596 3671 3783 3821 3858 3720 3520 

Model Alpha × 10-5 1.32 1.45 1.20 1.25 1.38 1.38 1.35 

Parameter k 1.44 1.44 1.44 1.44 1.44 1.44 1.44 

Model Prediction E{κi,y} 0.709 0.799 0.686 0.723 0.538 0.192 0.724 

Ci,y = E{κi,y}/ E{κi,1} 1 1.126 0.967 1.019 0.759 0.271 1.020 

The standard deviation of θ (from Hauer) is given by:(4)

(4) 

Aggregate effects over several sites: 

Results for a single site will tend to be meaningless and almost certainly lack reasonable 

statistical significance. To aggregate results over several sites, the procedure is to simply add 
33 

their individual values of the λ(i), π(i), and Var{π(i)}over all sites and replace these values in the 

equations with their respective sums, λsum, πsum, and Var{πsum)}. 

For illustration, five sites with various lengths of before and after periods are added to the 

analysis. Assume that the observed number of crashes, λ(i), the expected number of crashes 

without treatment, π(i), and its variance Var{π(i)} have been calculated and are given in table 3. 

In this example one site (Site 4) experienced more crashes in the after period than expected. 

The crash modification factor for the five sites is, therefore: 

 (5)

This means that the point estimate of the composite crash reduction percentage of 100(1-0.822) = 

17.8 percent. 

The standard deviation of this composite θ is: 

 (6) 

By including more sites in the analysis, some with relatively long after periods, θ has been 

estimated more accurately with a standard deviation of 0.140 compared to 0.453 using only one 

site with a short after period. Table 3 shows the composite effect at five sites, for illustration. 

Table 3. The composite effect over several sites (for illustration). 

Site Number, i λ (i) π(i) Var{π(i)} 

1 4 4.302 0.802

2 5 5.555 1.033

3 10 13.250 2.065

4 5 4.500 0.820

5 14 18.450 2.540

Sum 38 46.052 7.260

Data Collection Plan

Based on the requirements for the methodology described, the project team then developed a 

data collection plan. The first aspect of the plan was the choice of jurisdictions to include in the 

study. In the following description, this choice was based on sample size needs and the data 

available in each jurisdiction. The second aspect of the plan involved the specification of data 

variables to be collected. 
34 

Choice of Jurisdictions 

Evaluation Study Sample Size Estimation: 

 

When planning a before-and-after safety evaluation study it is vital to ensure that enough data 

are included such that the expected change in safety can be statistically detected. Even though in 

the planning stage the expected change in safety is not known, it is still possible to make a rough 

determination of how many sites are required based on the best available information about the 

expected change in safety. Alternatively, one could estimate, for the number of available sites, 

the change in safety that can be statistically detected. For a detailed explanation of sample size 

considerations, as well as estimation methods, see chapter 9 of Hauer.(4) The sample size analysis 

presented in this section addressed two cases: 1) how large a sample is required to detect 

statistically an expected change in safety and 2) what changes in safety can be detected with 

likely available sample sizes. The focus is on detecting effects at the treatment sites. It was 

assumed (and was later verified) that the number of noncamera equipped signalized intersections 

will be sufficiently large that spillover effects, if present, will be detected. 

Case 1—Sample Size Required to Detect an Expected Change in Safety: 

For this analysis, it was assumed that a conventional before-and-after study with comparison 

group design would be used, because available sample size estimation methods are based on this 

assumption. The sample sizes estimates so provided would be conservative in that EB 

methodology proposed would require fewer sites. To facilitate the analysis, it was also assumed 

that the number of signalized reference sites is equal to the number of treatment sites. This 

assumption was very conservative, because it was later decided to attempt to collect data on three 

signalized reference sites for each treatment site to better explore the spillover effect. 

The statistical accuracy attainable for a given sample size is described by the standard deviations 

of the estimated percentage change in safety. From this, one can estimate P-values for various 

sample sizes and expected change in safety for a given crash history. A set of such calculations is 

shown in table 8 based on assumptions of 20 crashes/site-year of which 3.5 are right angle 

crashes and 12.0 are rear end crashes, 3 years of “before” crash counts, 1.5 years of “after” 

period crash counts. The crash rates are estimated as an average of published data for RLC sites 

in Charlotte, NC, and Howard County, MD. The calculations are based on methodology in Hauer 

and a spreadsheet on his Web site, http://www.roadsafetyresearch.com.(4)

Table 4. P-values for various sample sizes and expected changes in safety.* 

Number of treated sites 20 60 100 

Percentage change in 

crashes 

10 20 30 10 20 30 10 20 30 

Right-angle crashes 0.58 0.23 0.05 0.42 0.08 <0.01 0.33 0.05 <0.01

Rear end crashes 0.51 0.23 0.10 0.42 0.14 0.04 0.40 0.12 0.03

*Based on 3.5 right angle and 12.0 rear end crashes/site-year, and before and after periods of 3 and 1.5 years respectively. 
35 

The shaded cells in table 8 indicate where P-values of at least 0.10 are attainable. Thus, for 

example, if the sample contains 20 treated sites, and a 30-percent reduction in the number of 

right-angle crashes is expected because of the RLC installation, one may expect to obtain a 

statistically significant result at the 10 percent level (P = 0.05). With 60 treated sites, if there is a 

20-percent increase in the number of rear end crashes, one may not expect a statistically 

significant result at the 5 percent level (P > 0.10); however, that result would be significant at the 

15 percent level. 

Case 2— Safety Change Detectable with Likely Available Sample Sizes: 

On the basis of preliminary crash data available early in the study, an estimate was made of the 

maximum percentage change in crash frequency that could be statistically detectable at 5-percent 

and 10-percent significance levels. Estimates were prepared for a variety of severity and impact 

types and for four representative jurisdictions from the survey. Because it was likely that 

additional data would become available as the study proceeded, it was felt that these estimates 

could be regarded as conservative. The estimates were also conservative based on other 

considerations mentioned below. 

The crash rate assumptions in table 5 were used for this exercise. They were based on published 

data from RLC installation sites in Howard County, MD (HC), and Charlotte, NC (CH), and on 

typical severity ratios indicating that about 35 percent of all crashes at signalized intersections 

involve injuries. 

Table 5. After period crash rate assumptions. 

Crash Type 

Rate A 

(crashes/intersection/year) 

(HC) 

Rate B 

(crashes/intersection/year) 

(CH) 

All right-angle 4.5 2.4 

Injury right-angle 1.6 0.9 

All rear end 20.7 6.4 

Injury rear end 7.3 2.3 

Tables 6 and 7 show, for each crash type, an estimate of the number of intersection years of 

after-period crash data for treatment sites in each of several jurisdictions and for 2 groups of 

jurisdictions. Separate parts of the table are presented for right-angle and rear end crash effects. 

For the two crash rate assumptions it shows the maximum percentage change in crash frequency 

that would be statistically detectable at 5 percent and 10 percent levels of significance for both 

crash rate assumptions. (For Howard County and Charlotte, only their respective crash rates from 

table 6 are used.) Only jurisdictions with 10 or more intersection years in the after period were 

considered as feasible for this analysis. Assuming that some sort of a national estimate would be 

useful and could be obtained through amalgamation of the results over several jurisdictions, 

which is certainly possible for injury crashes at least, calculations were also shown for two 

groups of jurisdictions: 
36 

• All jurisdictions listed 

• The “top seven” which includes jurisdictions for which significant effects are likely for 

all crash severities (Howard County, Baltimore, Charlotte, San Diego, San Francisco, 

Montgomery County, and El Cajon City) but excludes New York City because its data 

were found to be unsuitable for use in this study. These “top seven” were ultimately 

included in the study. 

This presentation allowed for various options for deciding on the size of the planned 

retrospective study; nevertheless, it was felt that consideration must also be given to the ease or 

difficulty of obtaining quality data from each jurisdiction (which is why New York City was 

ultimately excluded). 
37 

Table 6. Sample analysis for right-angle crash effects. 

Minimum percentage of change*

detectable for two crash-rate 

assumptions (A, B) 

Crash type Jurisdiction 

Intersectionyears in after 

period (and # 

camera sites) P = 0.10 P = 0.05

New York City, NY 126 (30) 18,21 21,24

Howard Co., MD 88 (26) 20 22

Baltimore, MD 85 (48) 20,23 23,26

Charlotte, NC 69 (30) 24 28

San Diego, CA 43 (19) 23,28 27,32

San Francisco, CA 37 (17) 24,29 28,33

Montgomery Co., MD 35 (15) 25,30 28,34

El Cajon City, CA 35 (9) 25,30 28,34

Sacramento, CA 28 (18) 26,32 30,36

Prince George’s Co., 

MD

20 (16) 29,35 33,39

Arlington, VA 15 (5) 32,38 36,43

Chandler, AZ 12 (8) 34,41 38,46

Boulder, CO 10 (3) 36,43 40,48

Group 1a 392 (104) 16,17 19,19

All 

right-angle 

All 603 (224) 15,16 18,18

New York City, NY 126 (30) 23,27 26,31

Howard Co., MD 88 (26) 25 29

Baltimore, MD 85 (48) 26,30 29,34

Charlotte, NC 69 (30) 32 37

San Diego, CA 43 (19) 31,37 36,42

San Francisco, CA 37 (17) 33,39 37,44

Montgomery Co., MD 35 (15) 33,40 38,45

El Cajon City, CA 35 (9) 33,40 38,45

Sacramento, CA 28 (18) 36,43 40,47

Prince George’s Co., 

MD

20 (16) 40,47 44,52

Arlington, VA 15 (5) 43,51 48,56

Chandler, AZ 12 (8) 46,54 51,59

Boulder, CO 10 (3) 48,56 53,61

Group 1a 392 (104) 18,20 21,23

Injury 

right-angle 

All 603 (224) 17,18 20,21 *

 Assumes a decrease in right-angle crashes and an increase in rear end crashes. 

a

 Group 1 includes Howard County, Baltimore, Charlotte, San Diego, San Francisco, Montgomery County and El Cajon City. 
38 

Table 7. Sample analysis for rear end crash effects. 

Minimum percentage of change*

detectable for two crash-rate 

assumptions (A, B) 

Crash type Jurisdiction 

Intersectionyears in after 

period (and # 

camera sites) P = 0.10 P = 0.05

New York City, NY 126 (30) 22,25 27,32

Howard Co., MD 88 (26) 22 28

Baltimore, MD 85 (48) 23,28 28,35

Charlotte, NC 69 (30) 27 37

San Diego, CA 43 (19) 25,34 31,43

San Francisco, CA 37 (17) 25,36 32,46

El Cajon City, CA 35 (9) 26,37 32,47

Sacramento, CA 28 (18) 27,41 34,52

Prince George’s Co., 

MD

20 (16) 30,48 37,62

Arlington, VA 15 (5) 33,56 41,74

Boulder, CO 10 (3) 38,73 49,99

Montgomery Co., MD 35 (15) 26,37 32,47

Chandler, AZ 12 (8) 35,65 45,87

Group 1a 392 (104) 21,21 26,26

All 

rear end 

All 603 (224) 20,21 25,26

New York City, NY 126 (30) 25,33 31,42

Howard Co., MD 88 (26) 26 33

Baltimore, MD 85 (48) 27,39 33,50

Charlotte, NC 69 (30) 43 56

San Diego, CA 43 (19) 32,55 41,73

San Francisco, CA 37 (17) 34,60 44,81

Montgomery Co., MD 35 (15) 35,63 45,84

El Cajon City, CA 35 (9) 35,63 45,84

Sacramento, CA 28 (18) 38,73 49,99

Prince George’s Co., 

MD

20 (16) 45,93 58, >100

Arlington, VA 15 (5) 52, >100 68, >100

Chandler, AZ 12 (8) 59, >100 79, >100

Boulder, CO 10 (3) 67, >100 90, >100

Group 1a 392 (104) 22,23 27,29

Injury 

rear end 

All 603 (224) 21,22 22,28 *

 Assumes a decrease in right-angle crashes and an increase in rear end crashes. 

1a Group 1 includes Howard County, Baltimore, Charlotte, San Diego, San Francisco, Montgomery County and El Cajon City. 

Sample Design Conclusions: 

Judgments on the likelihood of detecting significant effects assume that there is, in fact, an effect 

on crashes. If an effect does not exist, of course, no effect will be statistically detectable. Table 8 

presents the authors’ best judgment during the sample design stage on the likelihood of detecting 
39 

(at the 10-percent level) safety effects expected on the basis of the literature review, which 

revealed that it is not unreasonable to expect effects on the order of a 25-percent decrease in 

right-angle crashes and a 30-percent increase in rear-end crashes. Even so, for reasons explained 

earlier, these judgments were based on results that are likely to be conservative. 

Table 8. Best judgment on possibility of detecting safety effects.

 All 

right-angle 

Injury 

right-angle 

All 

rear end 

Injury 

rear end 

New York City, NY U U U U

Howard Co., MD U U U U

Baltimore, MD U U U U

Charlotte, NC U W U W

San Diego, CA U W U W

San Francisco, CA U W U W

Montgomery Co., MD U W U W

El Cajon City, CA U W U W

Sacramento, CA W W U W

Prince George’s Co., 

MD 

W W U W

Arlington, VA W W W W

Chandler, AZ W W W W

Boulder, CO W W W W

Group 1 U U U U

All U U U U

U = significant results may be obtained W = significant results may not be obtained.

Selection of Study Jurisdictions

As noted, the Us in table 8 were based on one criteria for inclusion in a study of RLC effects—

the available crash data. However, there were other criteria that needed to be considered—the 

availability and quality of the other data. Table 9 summarizes these crash-related findings from 

table 8, along with information on other data extracted from the interview forms. While a variety 

of information was captured in the interview, because of both study needs such as traffic flow 

data and the high-priority questions of interest, emphasis was placed on the presence of data on 

yellow interval changes, traffic flows at signalized and unsignalized intersections, the level of 

publicity campaign (to attempt to get a range of levels), and the type of signing. Note that the 

level of the publicity campaign was a project staff judgment based on the jurisdiction’s response 

to the initial telephone questionnaire discussion concerning public information. In the 

questionnaire, the three levels were given the following definitions: 

• High—A major planned public information campaign including components such as the 

FHWA public information program, combined PI efforts with other departments in the 

jurisdiction (e.g., local health department), and television spots. 

• Medium—Moderate public information program with limited expenditures, but good 

coverage of the RLC effort by news media. 
40 

• Limited—Limited public information program with media coverage only from interviews 

or press conferences, or both. 

 

Table 9 shows 3 categories of the authors’ judgment of the best cities based on these data 

variables. Howard County, MD, was judged the best overall, shown with bold italics. The 

second group of cities (in some order of preference) are those in italics—Baltimore, MD, 

Charlotte, NC, San Diego, CA, San Francisco, CA, Montgomery County, MD, and El Cajon 

City, CA. Each has some shortcomings, either in crash sample size or other data. For example, 

Baltimore has limited traffic count data, and El Cajon City’s crash data require further 

investigation. The remaining cities had serious problems either in crash counts or other data or in 

the size of the sample. 

Table 9. Best judgment on sites to use based on crash and non-crash data available. 

 Significant 

crash types 

Signal data 

available 

Traffic flow 

data 

Publicity 

campaign 

New York City, NY All 4 types YI, CL, SC None HPI, none 

Howard Co., MD All 4 types YI, ARI, CL, SC FTC, UITC MPI, SO 

Baltimore, MD All 4 types YI, ARI, CL, SC LTC MPI, SI 

Charlotte, NC ARA, ARE YI, ARI, CL, SC FTC, UITC HPI, SB 

San Diego, CA ARA, ARE YI, ARI, CL, FTC, UITC HPI, SI 

San Francisco, CA ARA, ARE YI, ARI, CL, SC LTC, UITC HPI, SB 

Montgomery Co, MD ARA, ARE YI, ARI, CL, SC LTC, UITC HPI, SO 

El Cajon City, CA ARA, ARE YI, ARI, CL, SC FTC, UITC MPI, SB 

 

Sacramento, CA ARE YI, ARI, CL, SC FTC, UITC HPI, SB 

Prince George’s Co., 

MD 

ARE YI, ARI, CL, SC LTC, UITC LPI, SO 

Arlington, VA None YI, ARI, CL, SC None MPI, SO 

Chandler, AZ None YI, ARI, CL, SC FTC, UITC MPI, SO 

Boulder, CO None YI, ARI, CL, SC FTC HPI, SO 

 

Fairfax Co., VA No after data ARI, CL, SC Unclear MPI, SI 

Greensboro, NC No after data ARI, SC FTC MPI, SI 

Significant Crash Types: ARA—All right-angle; IRA—Injury right-angle; ARE—All rear end; IRE—Injury rear end 

Signal Data Available: YI—Yellow interval (length and changes); ARI—All-red interval (presence and changes); CL—Cycle length, 

SC—Signal coordination 

Traffic Data Available: FTC—Full traffic counts (i.e., regular program of traffic counts for all signalized intersections); LTC—

Limited traffic counts (some intersections, or only as requested); No—no traffic counts; UITC—Unsignalized 

intersection traffic counts 

Public Information: HPI—High public information campaign; MPI—Medium public information campaign; LPI—Limited public 

information campaign; SI—Warning signs at intersections; SO—Warning signs at other locations (e.g., edge of 

town or corridor); SB—Warning signs at both intersection approaches and other locations 

Note that, as indicated earlier, New York City, the site with the largest crash sample, fell into the 

infeasible group because of the lack of traffic count data. New York City was also considered 

different from other U.S. cities by an RLR expert outside the project team because of its size, the 

number of intersections, the small proportion of intersections that are treated, the possible 

dilution of any publicity campaign, high tourism, and other factors. 
41 

As can be seen, three of the cities appeared superior to the other four cities—Howard County and 

Baltimore, MD, and Charlotte, NC. While a multijurisdictional study could be done with just 

these three cities, the project team did not recommend that because the sample size of 

jurisdictions would be small, two are in Maryland, and all three are eastern cities. 

Data Collection Requirements 

It was recommended that, for any installation a minimum of 2 years of before-period and 1 year 

of after-period information should be available and that, ideally, at least 3 years of crash data be 

required for each of these periods. As found later in the actual data collection, all sites in all 

jurisdictions had 4 to 9 years of before-period data, with an average before-period of 6 years. 

The length of the after period data varied from less than 1 year (in approximately 8 percent of the 

sites) to 5 years, with an average after-period of approximately 2.76 years for all sites. 

Crash data for the same years were also required for a reference group of locations, similar to the 

RLC locations, except that these were not equipped with RLCs. These sites were to be used in 

the recalibration of safety performance functions and to investigate possible spillover effects. 

Because the reference group was to be used both for this SPF recalibration and to study the 

distance-of-influence issue, a later decision was made to attempt to identify three referencegroup signalized intersections for each treated intersection in a jurisdiction. To account for time 

trends between the before-and after-periods, crash data also were collected from a comparison 

group of approximately 50 unsignalized intersections in each jurisdiction. 

Table 10 lists the basic data items originally proposed for collection. As will be described in the 

later section concerning data collection, modifications were made to this listing based on 

available data and funding issues. 
42 

Table 10. Data items required. 

Crash variables Traffic variables Geometric 

variables 

Operational 

variables 

Other 

variables 

Crash date 

Crash time 

Weather condition 

Light condition 

Impact type 

Crash severity 

Initial direction of 

vehicles 

Vehicle maneuver 

Vehicle type 

Location type 

(intersection) 

Citation issued 

Major, minor road 

entering AADT (at 

least one count in 

each of before-and 

after-period 

Number of 

approach lanes 

Median 

presence/width 

Approaches with 

left-turn lanes 

Approaches with 

right-turn lanes 

Cycle lengths 

Yellow intervals 

All red intervals 

Signal 

coordination 

Left-turn priority 

phasing 

Speed limit 

RLC installation 

date 

Grace periods 

Ticketing protocols 

RLC approach 

Date, type of other 

significant 

changes to 

intersection 

RLC signing 

Presence of LEDs, 

back plates, and 

30.48 cm (12 in) 

lens for signals 

(treatment sites 

only) 
43 

VIII. STUDY METHODOLOGY FOR ECONOMIC ANALYSIS 

As noted, two experimental plans were developed for this study. The first, described in detail in 

the preceding sections, was aimed primarily at examining changes in crash frequency resulting 

from the installation of RLC systems. The second experimental plan involved development of a 

database and methodology to examine the costs and benefits of RLCs in economic terms, thus 

allowing changes in crash severity and frequency to be analyzed simultaneously and 

identification of factors associated with the greatest RLC benefits. 

The use of economic analysis in the study of RLCs is important because of the nature of the 

expected effect of the treatment. Based on past research, RLCs probably will lead to decreases in 

right-angle crashes, for which injuries are often severe, and increases in rear end crashes, for 

which injuries tend to be less severe. It is also possible that within each of these crash types, the 

average level of injury severity is different with and without RLCs; therefore, it is probable that 

an evaluation of the economic benefits could result in a different conclusion about the effect of 

RLCs than might result from an analysis of changes in crash frequency by crash severity (e.g., 

the examination of changes in the frequency of injury crashes). Gaining a handle on the 

economic benefits is also important from the perspective of being able to assess costeffectiveness of RLCs and to compare this against the cost-effectiveness of other safety 

treatments aimed at reducing crashes at signalized intersections. 

To combine changes in both crash frequency and injury severity, each crash can be characterized 

by one measure—a dollar value based on the average level of injury severity for that type of 

crash. After that conversion is done, the results of the analysis can be reported in terms of 

changes in total economic cost of crashes expected without RLCs and those that actually 

occurred in the period after RLC installation. The unit crash costs can then be applied to the 

crash frequencies recorded after RLC implementation and those expected without RLC as 

estimated by the empirical Bayes method being used for evaluation. 

The key is the successful conversion of police-reported crash injury levels to a set of acceptable 

dollar-cost measures. As described in the following sections, the team explored different 

alternatives for this economic conversion and then worked with FHWA to define a final study 

design. 

Initial Economic Analysis Study Design 

In Charlotte, NC, there were accessible files of hospital and emergency room data that could be 

linked to traffic crashes, the initial discussion of an economic analysis study design centered on 

using these linked data. After further thought and exploration, it was clear that the possible use 

of these data raised a number of critical issues: 

• The available database covers only one of the two major hospitals in the catchment area, 

meaning that information for the remaining patients in the other hospitals would need to 

be estimated. That is, it must be assumed that there are no shifts from one hospital to the 
44 

other between the before-and after-period, and that the medical cost changes seen in one 

hospital would be similar in the other hospital.) 

• The database would not include information on nonhospitalized fatalities or on 

nonhospitalized occupants experiencing either lesser injuries or no injuries. Because the 

proportion of car crash occupants who are hospitalized is a small proportion of the total 

crash-involved population, costs for the nonhospitalized groups would need to be 

developed from other sources. 

• Most important, the economic data available in the database to be linked with crashes 

would contain only medical/hospital costs, which is only one component of either human 

capital cost or comprehensive cost. 

FHWA agreed that the issues were significant, and it required that a feasibility study be 

conducted to determine if these issues could be overcome. 

The following section describes the results of this exploration. 

Examination of Alternative Economic Analysis Methods 

The research team identified the following major issues that require resolution to successfully 

conduct an economic study: 

• RLC systems potentially could affect the full injury distribution from fatally injured to no 

injury; thus, the economic cost chosen must cover this complete distribution. 

• Hospital-related data (even that including costs for emergency room visits in addition to 

hospital admissions) include perhaps 15 percent of the total crash population. The data 

do not include information on those who die before reaching the hospital nor those whose 

injuries do not require hospital or emergency room treatment. 

• Medical costs most likely to be captured by hospitals are only one element of the total 

cost of crashes. Other elements such as lost work productivity, rehabilitation costs, 

insurance cost, quality of life losses, and others are not captured in medical cost data. 

• As requested by FHWA, data on both “comprehensive costs” (including “willingness to 

pay” costs related to pain and suffering) and “human capital costs”4

 (including many 

 

4 A detailed discussion of “human capital” and “comprehensive” costs can be found in Blincoe, et al.(26) In 

summary, human capital costs include direct and indirect costs to individuals and society as a whole from the 

decline in the general health status of those injured in motor vehicle crashes. Components include medical and 

rehabilitation costs, emergency services costs, lost market productivity, lost household productivity, insurance 

administration, workplace costs, legal costs, travel delay costs, and property damage costs. Comprehensive costs 

include all these components plus additional costs associated with intangible consequences of crashes to individuals 

and families such as pain and suffering and loss of life. In studies of motor vehicle crashes, both types of costs are 

usually keyed to individual levels of injury severity measured on the AIS within an individual body part (because 

consequences can vary by severity within body part injured). AIS is specified by trained medical data coders, 

usually within a hospital context. Average human capital or comprehensive costs are often defined in reports for an 
45 

other economic elements, but not “pain and suffering”) should be considered in this 

analysis. 

• Research conducted by and for the U.S. Department of Transportation (DOT) National 

Highway Traffic Safety Administration (NHTSA) has provided a detailed analysis of 

human capital costs for motor-vehicle crash victims in terms of Year 2000 dollars. 

Unfortunately, these costs are keyed to injury severity levels defined by the Abbreviated 

Injury Scale (AIS) rather than by the KABCO5 scale found in police reports, which will 

be the basic data being provided by the participating localities for this evaluation. 

• There has been no conversion from AIS-based cost to KABCO cost for FHWA since 

1994. There is agreement that the updating of the 1994 data to current year costs using 

changes in the Consumer Price Index would not be sufficient because these costs would 

not be consistent with the current NHTSA research nor current economic cost for 

fatalities being used by other federal agencies, and the 1994 costs did not include costs 

for each of the levels of the KABCO scale, only for “injury.”(25) Because RLCs may 

cause tradeoffs in injury severities within the injury category (e.g., a decrease in serious 

injuries and an increase in minor injuries), these 1994 costs are not considered sufficient 

for this study. 

Given these issues, the team explored two alternative approaches that led to exploring two 

databases containing information on the economic costs of motor vehicle crashes. 

Analysis of the Economic Benefits of RLCs Using Maryland CODES Data: 

 

First, an attempt was made to find hospital and emergency room cost data similar to that 

originally proposed from Charlotte. Information on NHTSA’s Crash Outcome Data Evaluation 

Systems (CODES) project was examined on the current CODES Web site. There are no CODES 

projects in either California or North Carolina, but the State of Maryland has one, where three of 

the test cities are located. E-mail and telephone discussions were held with the database engineer 

for the National Study Center for Trauma and EMS, University of Maryland—Baltimore, the 

current Maryland CODES coordination agency. This group has data on all motor vehicle 

crashes reported to the State of Maryland beginning in the early 1990s, including nonhospitalized 

injury and property-damage-only (PDO) crashes. For a typical CODES analysis, the crash data 

are linked to hospital discharge data or emergency room data, or both, based on a probabilistic 

match of variables linking the crash to hospital admission or emergency room visit records. 

Medical cost data can then be extracted for each driver of each vehicle and linked with the crash 

data. The project team requested data files from the CODES administrator for all linked and 

unlinked crashes occurring in Howard and Montgomery Counties and Baltimore City for the 

 eight-point injury scale based on the Maximum AIS score (MAIS) for an individual. Appendix A of Blincoe, et al., 

shows the average human capital cost ranges from approximately two-thirds of the comprehensive cost for a minor 

(MAIS 1) injury level to less than one-third the comprehensive cost for a fatality (i.e., $0.98 million versus $3.37 

million, in Year 2000 dollars).(26) 

5

 The KABCO severity scale is used by the investigating police officer on the scene to classify injury severity for 

occupants with five categories: K, killed; A, disabling injury; B, evident injury; C, possible injury; O, no apparent 

injury.(7) These definitions may vary slightly for different police agencies. 
46 

period of 1994–2001. After discussions with the administrator, the CODES Advisory 

Committee overseeing the Maryland data granted approval for the request and data were 

prepared and sent to the project team. 

If these Maryland CODES data were used in the economic analysis, they would provide more 

complete, but similar data to that of the Charlotte source. While slightly more difficult to work 

with than the Charlotte-linked data, these data include all hospital admissions and emergency 

room visits (at least, all that can be linked) rather than just those for one hospital, as was the case 

in Charlotte. 

However, the same problems with the Charlotte study were present in the Maryland data. The 

only cost data available are medical costs. After again considering how other cost items could be 

added to the data set, such as how to add costs for lost productivity, perhaps from NHTSA data 

based on the AIS level of injury, the authors concluded that this study approach was not 

sufficient. 

Economic Benefits Analysis of RLCs Using Pacific Institute for Research and Evaluation 

Comprehensive Cost Data: 

 

As noted earlier, the key to this economic analysis effort is to assign a human capital or 

comprehensive crash cost to each relevant crash in a test city. If accomplished, the economic 

analysis could be conducted in each of the seven cities rather than in just one, as originally 

envisioned. Because the city data are based on police-reported KABCO injury scales, this could 

be done only if AIS-based comprehensive cost could be mapped to the KABCO scale with 

suitable precision. That mapping became the goal of the efforts. 

Dr. Ted Miller of the Pacific Institute for Research and Evaluation (PIRE), a leading U.S. expert 

on economic costs for motor vehicle crashes, developed the earlier KABCO-mapped 

comprehensive costs for FHWA. Because of this, the project team initiated a detailed review of 

his more recent publications related to this topic. Subsequent discussion with Miller indicated 

that it was feasible to update the mapping. In addition to providing comprehensive costs for this 

RLC study, the proposed approach would result in KABCO-mapped costs that could be used by 

FHWA in future evaluations and problem analysis efforts. 

In a 1997 article, Miller et al., used National Automotive Sampling System (NASS) 

Crashworthiness Data System (CDS) files, Fatal Analysis Reporting System (FARS) data, and 

General Estimates System (GES) data to develop an estimate for the total direct cost (not human 

capital or comprehensive costs) and years of functioning life lost for occupants involved in 30 

different crash geometries (e.g., cross-path crashes at signalized intersections).(27) While 

comprehensive costs were not calculated directly, it is noted that “years of functioning life lost” 

is a measure that can be converted to comprehensive costs, combining human capital direct and 

indirect costs with pain and suffering costs, as discussed in appendix A of the earlier cited 

report.(25) Miller’s goal was to better identify the highest cost geometries, and thus the ones to 

best target for treatment efforts. Because direct (and comprehensive) costs can be developed for 

AIS levels, his approach involved developing such occupant-based costs for each AIS level in 

each of the 30 different crash geometries. The NASS-CDS files contained the AIS level for each 
47 

occupant; however, because the FARS and GES data to be used in the national estimates do not 

include the AIS level, Miller had to map his AIS costs to KABCO levels. Because a KABCO 

rating from the original investigating police officer was also included for each occupant in the 

NASS CDS file, the occupant-injury cost of each level on the KABCO scale within each 

geometry could also be calculated. Because current CDS files do not contain crashes involving 

nontowaway vehicles or pedestrians, the old NASS files were used to fill in these missing data. 

Then, using national crash-occupant frequencies within each KABCO level for each of the 30 

crash geometries based on GES and FARS data, Miller et al., produced an estimate of the total 

national direct cost for each geometry.(27) 

While this current project’s goal was not to produce such national estimates of cost by crash 

geometry, there was the critical need for human capital and comprehensive cost for each 

KABCO level within certain intersection-related crash geometries (e.g., right-angle crashes at 

signalized and stop-controlled intersections). Thus, it appeared logical that Miller could use his 

data to produce such estimates. 

Possible Study limitations in Miller’s Dataset: 

 

Three critical study limitations based on files available to Miller were identified by the project 

team before the development of the final data request: (a) sample size limitations in the NASS 

and CDS crash files to be used, (b) whether available variables in the Miller files would allow 

further classification of crash geometry by urban and rural crash location, and (c) whether 

Miller’s earlier occupant-based cost findings could be converted to costs per crash within each 

geometry of interest. 

The possible sample size limitations in NASS files containing both AIS and KABCO injury 

scaling arose from the need to estimate cost for each KABCO level within each of the more than 

30 crash geometries, further categories by urban versus rural crash location. Such detailed 

estimates had never been attempted before, and low sample sizes could result in both unstable 

and illogical findings. As indicated later, such problems did arise. 

Second, Miller’s earlier analysis did not separate crash geometries by urban and rural location or 

by speed limit. Because KABCO levels are much broader than AIS levels, the cost of injury 

within any KABCO level for a given crash geometry might differ depending on speed limit or 

urban and rural location. For example, the severity and thus the cost of A-injury angle crashes at 

rural higher-speed intersections may be greater than A-injury right-angle crashes at urban 

intersections. Given this fact, it was desirable to further categorize Miller’s 30 geometries by 

either speed limit or urban and rural location. 

Unfortunately, examination of documentation for the databases to be used by Miller indicated no 

urban and rural indicator in one of the critical files; however, speed limit variables were present. 

The team then used FARS, NASS, GES, and Highway Safety Information System (HSIS) data 

from two states to compare crash-related speed limits to various urban versus rural designations. 

There was significant overlap of limits within urban and rural designations in all three files. 

Based on the distributions and the need to have sufficient samples sizes in all the subcategories, a 
48 

decision was made to attempt to categorize cost estimates by locations with speed limits of 72.42 

km/h (45 mi/h) and slower versus 80.47 km/h (50 mi/h) and faster. 

Third, the primary need in this study (and in future FHWA studies) is cost estimates per crash 

rather than per occupant. Fortunately, Miller’s preliminary investigations indicated that cost per 

crash could be developed from the existing databases. 

Possible Economic Study Limitations from Small Jurisdictional Samples of Crash Data: 

This discussion is based solely on information available in Miller’s databases; however, the 

choice of what economic analyses to conduct, and thus what specific cost categories to request 

from Miller, also depended on the nature of the database to be used in this RLC analysis—the 

sample size of crashes within different KABCO severity levels in the seven jurisdictions. Two 

significant issues deserve further attention. 

First, in the initial analysis planning, the project team developed estimates of sample sizes 

necessary for analysis. The team then examined the total right-angle and rear end crash 

estimates and the injury right-angle and rear end crash estimates from each of the seven 

jurisdictions to determine if individual site-by-site analysis would be feasible. As shown in table 

8, while each of the seven jurisdictions should provide adequate data for a “total crash” analysis, 

an “injury-crash” analysis may yield only statistically significant effects in two of the seven 

jurisdictions. This finding was based on combining all injury levels (K,A,B,C) in the latter 

analysis. Even there, five of the seven individual analyses may not yield significant effects. 

In this economic analysis, the best methodology would be to assign a human capital or 

comprehensive cost to each occupant injury in each of the involved vehicles, and sum these to 

produce a total crash cost as the outcome variable. This requires assigning a cost to each level of 

injury, then subdividing the “combined injury” category in more detail. While the costs will be 

multipliers for each injury, and thus the magnitude of cost as the outcome variable will be much 

larger than the magnitude of individual injury crash counts, there remains the logical question of 

whether these small samples of individual injury-level crashes will support such an analysis. 

While the cost numbers will be large enough, the question is whether the sample is large enough 

to produce a stable measure of cost in the before-and-after periods for an individual site. 

The second issue raised by the sample sizes within the RLC database is whether a cost should be 

assigned to each severity level, including fatalities, or should some levels be combined. The 

primary issue here is that even in right-angle crashes, fatalities will represent a small proportion 

of the total injury distribution (perhaps less than 2 percent). The comprehensive and human 

capital cost assigned to a fatality will always be multiples of cost for even an A-injury. The final 

analysis showed that the fatal crash costs were 10 to 30 times the A-injury crash costs within the 

same crash type; thus, one or two fatalities in either the before-or after-period for a given 

location could greatly affect the cost-related analysis. While this could be legitimate if one could 

expect repeated similar samples to have the same number of fatalities, this is not likely, 

particularly in samples this small. (It is also noted that Hall, in crash-cost research done for the 

State of New Mexico, argues that not only are such fatalities somewhat random in any crash 

sample, the main factors determining whether an injury is a fatality rather than a severe injury 
49 

probably are not affected by roadway-related treatments. They are more likely to be related to 

factors such as occupant age, restraint use, and type and size of vehicles involved. He argues that 

such small numbers of fatalities should not be allowed to affect decisions on roadway-based 

treatments such as RLCs).(28) 

The issue becomes whether to assign separate cost to a fatality and an A-injury or combine these 

two into one category (perhaps representing approximately 10 percent of the injury distribution 

in these crashes) and assign some type of “average K/A cost.” 

This same argument can be carried further, combining other categories of injury. Ultimately, 

this could result in an “average crash cost” (in a similar proportion-weighted manner) for each 

crash geometry. The cost would be assigned to a crash based on crash type rather than crash 

injury; however, this would assume that only the crash type frequencies (or ratios) change 

between before-and after- periods in the RLC evaluation, and that there is no internal shift in 

injury distribution within a given crash type. Right-angle crashes could be reduced, but it would 

be assumed that the severity distribution of the right-angle crash in the before-period is the same 

distribution as in the after-period. While this might be a good assumption based on a hypothesis 

that RLCs should not change the impact speed in right-angle crashes (because impact speed is a 

major predictor of crash severity), whether or not this is a good assumption is unknown. It can 

also be argued that red-light-running right-angle crashes targeted by RLCs tend to be at higher 

speeds than other right-angle crashes. 

A final problem is that the reporting thresholds appear to differ across the seven jurisdictions. 

For example, PDO crashes are less likely to be reported in California sites and Howard County 

and Montgomery County, MD, than in Baltimore, MD, or Charlotte, NC. Given that PDOs are 

assigned a cost in this cost analysis, this differing threshold will lead to different economic 

outcomes across sites. The same will be true for the frequency analysis of total crashes. This 

factor will be taken into consideration when the results from different jurisdictions are compared, 

and when combination of the results is attempted. 

Final Methodology for Economic Analysis 

Types and Levels of Economic Cost Estimates 

A number of issues are present in an attempt to measure the economic effect of RLC systems. 

Some occur because of a lack of current data on the human capital or comprehensive cost of a 

crash referenced to individual levels of the police-reported KABCO scale. Others occur because 

of the nature of the seven-jurisdiction database that will be used in this overall evaluation of 

RLCs, with available sample sizes of observations in each severity level for pertinent crash types 

(right-angle and rear end) being the most critical one. Given all these issues and uncertainties, 

and the fact that the same issues will arise in future FHWA studies, a decision was made to have 

Miller and the PIRE staff develop multiple levels of both comprehensive and human capital 

costs. The following levels were requested: 

• Level 1—For each of the 22 crash geometries (categorized by two speed limit categories 

as a surrogate for urban/rural), estimates of cost for crash severity levels K, A, B+C, and 

O. (Sample size issues in the cost databases made it impossible to develop reasonable 
50 

estimates of B versus C separately.) This analysis was first done for each of the two 

speed limit categories, and then with all speed limits combined. 

• Level 2—For each crash geometry, estimates of cost when K and A are combined into 

one cost level and B and C are combined into one cost level – thus K+A, B+C, O. 

(Again, estimates were calculated with and without categorization by the two speed limit 

categories.) 

• Level 3—Allows for comparison of “injury” versus “noninjury” crashes. Some crash 

forms (and some reporting officers) define a “C- injury” as a “minor injury” while others 

define it as a “possible injury.” Thus, two definitions of Level 3 costs were used: 

− Level 3A—For each crash geometry (with and without speed limit 

categorization), estimates of cost when all injuries are combined into one cost 

level separated from the PDO cost level, thus K+A+B+C versus O. 

− Level 3B—For each crash geometry (with and without speed limit 

categorization), estimates of cost when K, A, and B injuries are combined into 

one cost level separated from the C and PDO cost levels, thus K+A+B versus 

C+O. 

• Level 4—For each crash geometry (with and without speed limit categorization), 

estimates of crash cost without regard to crash severity, in other words, no division by 

levels of severity. 

• Level 5—For each level of crash severity (with and without speed limit categorization), 

estimates of cost without regard to crash geometry. 

• Level 6—Level 5 cost estimates, but with the following categories: K+A, K+A+B, 

K+A+B+C, B+C, C+O. 

Choice of Cost Levels 

Because it was not feasible or necessarily desirable to conduct economic analyses at all these 

levels for each of the seven jurisdictions, a pilot economic analysis study using one jurisdiction 

was chosen. Charlotte, NC, data were chosen because Charlotte has a medium sample size that 

provides some knowledge of whether smaller and larger samples can be analyzed, and Charlotte 

data contain all KABCO crash levels, allowing a multilevel analysis. Here, four economic 

analyses were conducted, one for each of levels 1–4: 

• All KABCO levels within each geometry. 

• Costs for K+A, B+C, 0 within each geometry. 

• Costs for injury versus no-injury within each geometry (based on both definitions of “no 

injury”). 

• “Urban costs” (those with lower speed limits) based on crash geometry only. 

The outcomes of these four analyses were used to determine which level is most appropriate 

given the available sample sizes in the remaining six jurisdictions. 
51 

Economic Analysis Statistical Method 

The general analysis methodology used to define the economic effects of the RLC program for a 

given jurisdiction closely parallels the methodology used for total crashes and injury-crash 

frequencies. Here, instead of the difference between the crashes “expected without treatment” 

versus “observed with treatment” in the after-period, the measure of effectiveness would be the 

difference between the net economic costs “expected without treatment” and “observed with 

treatment” in the after-period. 

For simplicity, the theory is presented for estimating the change in crash costs over all treatment 

sites in a jurisdiction, for a specific crash type, aggregated over all KABCO subgroups (e.g., two 

subgroups K+A+B+C, O). The crash types of interest are right-angle, rear end, and other (i.e. 

other than rear end and right-angle). The following notation is used: 

CostBi equals the cost of crashes in KABCO subgroup i actually occurring at the 

treatment sites in the before-period. 

ΛcostA equals the cost of crashes actually occurring at the treatment sites in the 

jurisdiction in the after-period. 

VAR{CostB} equals the variance of the cost of crashes in the before-period. 

VAR{ΛcostA} equals the variance of the cost of crashes in the after-period. 

ΠcostA equals the expected cost of crashes in the after-period over all treatment sites had 

there been no RLC (after correcting for regression to the mean and traffic volume and 

other differences between before-and after-periods). 

VAR{ΠcostA} equals the variance of the expected cost of crashes over all treatment sites 

in the after period without RLC. 

Bi equals the observed number of crashes in KABCO subgroup i over all treatment sites 

in the before-period. 

Πi equals the expected number of crashes in KABCO subgroup i over all treatment sites 

in the after-period without RLC (after correcting for regression to the mean and traffic 

volume and other differences between before-and after-periods). These were derived for 

the crash frequency analysis presented by Persaud et. al using the empirical Bayes 

methodology.(2) 

The estimated change in crash costs is 

(7) 
52 

The variance of change in crash costs is 

 (8) 

The cost modification factor is 

 

 (9) 

The variance of cost modification factor is given by 

 (10) 

Of interest at this point is how estimates were obtained for the four terms, ΛcostA, VAR{ΛcostA}, 

ΠCostA , and VAR{ΠcostA}. Following are the approximate methods used. 

The value of ΛcostA (i.e., actual after crash cost) was estimated by summing the individual PIRE 

costs for each crash in the after-period over all treated intersections in the jurisdiction. The value 

of VAR[ΛcostA] was estimated by summing the variance for each individual cost of the crashes of 

interest in the after-period. 

ΠcostA, (i.e., the expected after cost without treatment) was estimated for a KABCO subgroup by 

first estimating an expected cost for each site as the product of Πi (i.e., the expected number of 

crashes in the KABCO subgroup) and the PIRE unit economic cost for the crash type, KABCO 

subgroup, and speed limit category. These were then summed over all treatment sites and 

KABCO subgroups to get ΠcostA..

VAR{ΠcostA} for each site and subgroup was taken as product of Πi and the PIRE unit variance 

for the crash type, KABCO subgroup, and speed limit category. These variances were then 

summed over all sites and KABCO subgroups. This is an approximation that likely 

underestimates the variance, considering there is variance in the EB estimates of the expected 

number of crashes without treatment; however, the PIRE unit cost variances are also 

approximations because they do not include all components (e.g., variance in medical costs by 

diagnosis). Fortunately, the point estimates of the economic effects, which are of primary interest 

in this analysis, are quite insensitive to VAR{ΠcostA}. 

As noted, the theory so far applies for a given crash type of the three comprising all crashes. To 

obtain estimates of economic effect for all crash types combined, ΛcostA, VAR{ΛcostA}, ΠCostA, 

and VAR{ΠcostA} are first determined for each crash type as outlined and then summed over the 

three crash types before applying equations7 to 10. 
53 

IX. DATA COLLECTION 

Following the approval of the basic research design described earlier, the project team initiated 

data collection in each of the seven jurisdictions. The data items listed in table 14 were sought in 

each jurisdiction. Team members visited each site and collected available raw data. They then 

coded the data into computerized analysis files based on protocols and data formats developed 

earlier. 

Crash Data 

Crash data for 3 or more years before RLC installation was sought for each treatment, reference, 

and control intersection in each city. Preliminary telephone interviews with each of the seven 

jurisdictions indicated that historic crash data were available in most cases; however, discussions 

held during the site visits indicated that while there were some computerized historical crash data 

available in some cities, most did not retain computerized (or raw) data for the needed beforetreatment periods. The only exception was Charlotte, NC. There, staff could provide 1997 

through March 2002 data in Microsoft®Access files from their current computer system. Earlier 

1994–1996 data were not stored in the current system. Working with Charlotte staff, a computer 

analyst who had worked with the older files was located, and she was able to retrieve the old 

files and covert them to Access files for project use. 

Neither the three jurisdictions in Maryland nor the three in California could provide adequate 

historic crash data because they either did not store multiple years of older data or were restricted 

as to what could be released. The California State Highway Patrol (CHP) is the repository of all 

police crash reports for the entire State. The project team requested and received computerized 

data from the CHP on all crashes for the three California jurisdictions for the years 1992–2002. 

Variables extracted for use in this analysis are described in the “Results” section. 

The same data deficiency was found in the three Maryland jurisdictions. As noted earlier, the 

team had learned that the Maryland CODES team retained history crash data for all Maryland 

crashes. Project staff requested and received a computerized file of all crashes occurring in 

Howard and Montgomery Counties and Baltimore City for the period 1994–2001. Later, the 

additional data for 2002 were requested and received. 

Intersection Inventory and Volume Data 

As indicated, all available raw intersection geometric, signalization, and traffic flow data listed in 

table 10 were collected from the individual jurisdictions and coded to analysis files by the project 

team. During the early part of the data collection phase, it became apparent that the effort 

required and the cost of the data collection was going to greatly exceed the original estimate. 

This collection and coding required both an onsite visit to each jurisdiction where the project 

team met with transportation staff and collected the raw data and subsequent extraction and 

coding of the data into a computerized analysis file. A small part of the increased effort resulted 

from the addition of a limited number of required variables at the end of the experimental plan 

effort; however, most of the increase resulted because in most cities, little or none of the data 

except for the crash data are computerized. Thus, in addition to finding and copying (and later 

coding) paper files, the team often needed to manually extract data from Computer Aided 
54 

Detector Design (CADD) files, intersection drawings, and aerial photographs held in different 

offices. 

As the result of internal research team discussions and conversations with the FHWA task order 

manager, steps were taken to both minimize data collection costs and to examine alternative data 

collection levels. It was decided that the decision concerning final data collection levels for 

treatment, signalized reference, and unsignalized control sites would be based on the 

development of safety performance functions using the data from two cities where full coding 

was done. The SPFs would be developed with various combinations of variables to determine 

the minimum set of data items required. 

The project team developed these SPFs based on data from El Cajon, CA, and Howard County, 

MD, analyzed the data, and developed recommendations for FHWA review. (A copy of the 

internal memo describing this effort is available from the project team or FHWA.) 

The findings indicated that the project would require what amounted to full coding of all traffic 

signal data, coding for all available traffic volume data (which is limited in some jurisdictions), 

and almost full coding of the intersection geometry data. More specifically, FHWA agreed with 

the project team that the final analysis files would include the following information: 

• All pertinent crash data linked to the appropriate treatment, signalized reference, and 

unsignalized reference intersections. 

• For RLC (treated) intersections, full coding of all non-crash data including coding of 

signal data (signal timing and changes over the project period), signal data related to leftturn protection, and data related to actuated versus fixed cycle length. Where available, 

data on light emitting diode (LED) and backplate presence were coded. All data related to 

the intersection geometry shown in table 14 were coded, including lane and median 

measurements and speed limits where available. All available traffic volumes were 

coded and converted to AADTs for each approach. Information on the location of RLC 

warning signs was also coded. 

• For signalized reference intersections, modified coding of all non-crash data. This 

included all data noted in the paragraph previous for LED and backplate data, and lane 

and median widths. In addition, the signal timing data to be coded included only indicator 

variables for left-turn protection and whether a signal is actuated or always works under a 

fixed cycle length. 

• For unsignalized control intersections, modified coding of all non-crash data. This 

includes volume data as noted in the second paragraph. The geometry data includes 

number of approaches, number of approaching lanes on each approach (without turn or 

through designation), and which approaches are stop-sign controlled. 

It is noted that the sources of different intersection inventory variables differed from jurisdiction 

to jurisdiction. In most cases, the basic data source was a paper file; however, in some cases, one 

or more of the jurisdictions did not have paper files, and alternative sources were found and used. 
55 

For example, in Baltimore, MD, electronic CADD drawings were not available for any 

intersections, and hard-copy drawings were only available for a limited number of intersections 

(i.e., less than 20 percent). There, aerial photographs were located in the city’s geographic 

information system (GIS) office, and project staff coded intersection geometrics from those 

photographs to the extent possible. Appendix A provides information on the basic source of data 

for each of the key variables in each jurisdiction. 

As indicated, annual traffic volume data for each approach at all intersections is important in the 

development of the SPFs and the subsequent analyses. As was the case with other inventory 

data, the full array of needed volume data did not exist in data files in the jurisdiction. Unlike 

State systems where traffic volumes (i.e., AADT) for each section of roadway are generally 

updated each year through a series of counts and estimating procedures, only limited volume 

data were found in city and county files. While there were a limited number of midblock traffic 

counts at set locations in some jurisdictions, most traffic data were collected in the form of 

intersection turning-movement counts, and done as-needed. Thus, the available counts were 

often either multiple-hour turning movements (e.g., 4-hour or 11-hour counts), or such counts 

had been converted by the jurisdiction to average weekday daily traffic (AWDT) counts. 

Thus, it became clear early in the data collection effort that estimates of annual approach 

volumes would need to be developed by the project team from the available count information. 

The types of available data, and thus the gaps in annual approach counts, varied from jurisdiction 

to jurisdiction. In all cases, the requirement was that there be at least one count for all 

approaches in either the before- or after-period, with the goal being to have at least one full count 

in each period. 

Following is a list of the methods used to develop AADTs and fill in gaps: 

• In limited cases, missing counts for city or county intersections were obtained from the 

State Department of Transportation (if the road was a State-system highway), or from 

local metropolitan planning organizations (MPOs). 

• When hourly turning movement counts or AWDTs were available, they were converted 

into AADTs using factors either supplied by the jurisdiction (often on the count record) 

or factors developed by the project team based on information provided by the 

jurisdiction or the State Department of Transportation for that jurisdiction. 

• When only one traffic count was available for the before- or after-period, that AADT was 

assigned to all years in that before-or after-period. (We chose not to use estimated 

growth factors between years because none were provided by the jurisdictions, and the 

project team felt that the presence of multiple intersections with counts in different years 

would allow the development of sufficient SPFs for the analysis.) 

• When counts in differing years in either the before- or after-period existed for a given 

approach, the approach counts were averaged and that average was used for all years in 

either the before- or after-period. 
56 

• When only one count was available for an intersection, the AADT developed from it was 

assigned to all years in the before-and after-periods. This occurred in less than 10 percent 

of the total intersections in the study. 

• When an approach count existed for one approach but not the opposing one (usually for 

the minor roads), the opposing approach was made equal to the existing count. 

• When a count of entering vehicles for the full intersection was found, but not individual 

approach counts, data from other years when approach-specific data were available at the 

same intersection were used to calculate a percentage at each approach, and the enteringvehicle counts were distributed using those percentages. (The project team analyzed one 

limited set of intersections where both entering vehicles and approach-specific counts 

were present for different years. The individual approach counts were predicted based on 

proportions from another year, and then compared to actual counts. There was fairly good 

agreement, with approach count errors usually between 5 percent and 20 percent. The 

larger percentage errors were usually associated with low counts, and thus did not 

represent large differences in frequencies. Given this level of agreement, the fact that 

most large errors seem to be for small counts, and the fact that the only alternative was to 

delete these intersections without suitable replacements, the project team felt this method 

was suitable.) 

The AADT data ultimately developed and used in these analyses cannot be considered as 

accurate as what might be found in some State files. All available sources for additional data 

were explored; however, the project team considered the AADT data to be sufficient for use. 
57 

X. DATA AND SPF PREPARATION 

Before the actual data analyses, preliminary efforts involving file merging and data quality 

checks were completed. 

Crash Data Linkage to Intersections 

In most State DOT crash and inventory files, as is the case with the State data in FHWA’s 

Highway Safety Information System (HSIS), crashes can be computer-linked to inventory and 

traffic volume data for roadway segments and intersections using location reference variables 

such as route or milepost on each record. This was possible in Howard and Montgomery 

Counties, where project staff were provided an electronic file of the milepost book used by 

police officers in the field. The treatment and reference intersections were identified and 

matched to crashes based on this milepost information. 

This was not the case for the intersections analyzed in other jurisdictions. In Charlotte, NC, the 

1997 and later data included intersection control numbers for all intersections and crashes that 

could be used for file linkage. There were also control numbers in the pre-1997 data, but they 

differed from the later data. The Charlotte staff provided us with conversions between the new 

and old systems. 

For all data for the three California jurisdictions and Baltimore, MD, no such location system 

existed, and the crashes had to be manually linked to pertinent intersections based on the names 

of the crossing streets. The crashes were sorted by street names and an analyst matched the 

crash-report streets with the street names from the treated and comparison intersection file. All 

combinations of crash-report street names were checked to pick up possible misspellings by the 

investigating officer or coder. 

The project team was able to conduct a limited verification of both the completeness of the State 

CODES data files and the manual linkage procedures using El Cajon, CA, data. The local traffic 

engineer sent the project team crash summary reports for one treatment, one signalized reference, 

and one unsignalized control intersection. These summary reports contain a listing of all cases 

that have been coded to an intersection by city staff, using their own coding scheme. The 

comparison of these crashes to those identified and linked by the project team indicated that use 

of the State data resulted in minor differences with the local crash summaries. It was thus 

concluded that the State data is of sufficient quality.

Defining Red-Light-Running Crashes 

As indicated earlier, the basic analyses were to be focused on target crashes, those red-lightrunning crashes that could be affected by the RLC treatment. The analysis would also examine 

other intersection crashes to confirm that unanticipated effects were not present. Because there 

is no “red-light-running” crash category on most police crash forms, these target crashes must be 

defined based on variables on the form. Definitions could range from only crashes in which a 

citation for a traffic signal violation was given to all right-angle crashes and rear end crashes at 

or near the intersection. One could choose to include rear end crashes that were noted by the 

officer as “intersection-related” (where this variable was present), or to include all rear end 
58 

crashes approaching the intersection within a specified distance of the intersection. Depending 

on the distance (X) chosen, the assumption would be that the RLC would affect behavior of the 

lead vehicle or vehicles, which could result in rear end crashes X distance back in the 

approaching queue of vehicles. One could also choose to include left-turn opposite-approach 

crashes because some of these would be red-light-running crashes if a protected signal phase 

existed. To further complicate matters, the different jurisdictions use slightly different 

definitions of right-angle crashes on the report form. 

Based on definitions used in previous studies, available data variables in the current files, and 

project team discussions, the following general decisions were made by the project team: 

• In general, “RLC-related crashes” would include crashes in the intersection itself where 

one vehicle is “running the light,” plus intersection-related rear end crashes that could be 

affected by RLC systems, including those rear end crashes occurring in the approach 

queue. Clearly, neither of these two types of crashes is explicitly defined in crash data. 

Thus, the following definitions were used. 

• “Red-light-running” crashes at the intersection proper were defined as “angle,” 

“broadside,” or “right- or left-turning“ crashes involving two vehicles, with the vehicles 

entering the intersection from perpendicular approaches. “Perpendicular approaches” was 

defined using the compass directions of travel for each involved vehicle, a variable that 

was present in the data for all seven jurisdictions. In most jurisdictions, all crashes 

meeting these “crossing” criteria and occurring at or within 6.096 m (20 ft) of the 

intersection were captured. (A second definition of these RLR crashes includes crashes 

involving a left-turning and a through vehicle from opposite approaches on the same 

roadway. This would capture those vehicles running the red signal either during or 

before or after a protected signal phase.) 

• Rear end crashes used in the analyses were those defined as “rear end” by the crash type 

and occurring on any approach within 45.72 m (150 ft) of the intersection. Total 

intersection-related crashes were also analyzed. The definitions for each jurisdiction 

appear in table 15. 

As could be expected, available crash variables and codes differed between cities, making it 

impossible to have totally consistent definitions across all seven jurisdictions. For example, only 

the three Maryland jurisdictions had an “intersection-related” code that can be used to further 

screen rear end crashes occurring within 45.72 m (150 ft) of the intersection. Thus, all rear end 

crashes within 45.72 m (150 ft) were used in Charlotte, NC, and all three California databases. 

In addition, we encountered significant problems with the distance-from-intersection data in 

Baltimore, MD. Approximately 10 percent to 15 percent of the data appear to have questionable 

distances such as distances of 0.03 m (0.1 ft) and 0.30 m (1.0 ft) from the intersection. The 

project team attempted to verify these distances by obtaining hardcopies, but found that the 

accident case numbers in the computerized CODES data were not the same as the Baltimore 

Police Department case numbers, and only Baltimore has hardcopies of the reports. Thus, in the 

Baltimore analyses, two sets of data were used, a first set containing only rear end crashes within 
59 

45.72 m (150 ft) where the distance data were believed to be accurate, and a fuller set that also 

included crashes coded as within 45.72 m (150 ft), where the distance measurements were 

questionable. The analyses of these two sets of data revealed no significant differences; therefore 

the full set including the questionable distances was used for the final analysis. 

The final set of criteria for each RLC-related crash type for each jurisdiction is listed in table 11. 

Table 11. Definitions of crash types used in the analyses for each jurisdiction. 

El Cajon, San Diego, San Francisco, CA 

Intersection-related —All crashes at or within 6.096 m (20 ft) of intersection; rear end crashes within 

45.72 m (50 ft). 

Right-angle 1 (RA1)—Broadside, head-on, or sideswipe where vehicles approach intersection from 

perpendicular directions. (California does not have “left-turn” or “right-turn” as a crash type. Because 

there could be crashes from perpendicular directions where one of the vehicles is turning, it is assumed 

that all turning crashes are coded as either broadside, head-on, or sideswipe.) 

Right-angle 2 (RA2)—Crashes in RA1 plus opposite direction left-turn. This may not be as precise a 

definition as RA1 because it could include non-RLR crashes in which the oncoming vehicle and the 

turning vehicle both had a green signal. That is, these are not restricted to locations with protected leftturn phases only. However, opposite direction left-turn crashes do include RLR crashes in which a 

vehicle turning left at the end of a green phase (referred to as a “sneaker” in traffic engineering 

terminology) is broadsided by a vehicle from the opposing direction that is technically running a red light. 

Rear end—All rear end crashes within 45.72 m (150 ft) of intersection. 

Charlotte, NC

Intersection related—All crashes at or within 6.096 m (20 ft) of intersection; rear end crashes within 

45.72 m (150 ft) of intersection. 

Right-angle 1 (RA1)—Angle, head-on, sideswipe, left-turn different roadways, right-turn different 

roadways where vehicles approach intersection from perpendicular directions. 

Right-angle 2 (RA2)—Those in RA1 plus opposite direction left-turn. 

Rear end —All rear end crashes within 45.72 m (150 ft) of intersection. 

Howard County, Montgomery County, MD 

Intersection-related—All crashes within 48.158 m (158 ft) and identified as “intersection” or “intersectionrelated.” 

Right-angle 1 (RA1)—Vehicles approach intersection from perpendicular directions, in any category of 

head-on, head-on left-turn, opposite direction sideswipe, straight movement angle, angle meets rightturn, angle meets left-turn, or angle meets left head-on. 

Right-angle 2 (RA2)—Those in RA1 plus opposite direction left-turn. 

Rear end—All rear end crashes within 48.158 m (158 ft) and identified as “intersection” or “intersectionrelated.” 
60 

Table 11. Definitions of crash types used in the analyses for each jurisdiction. Continued

Baltimore, MD 

Intersection–related—All crashes within 48.158 m (158 ft) and identified as “intersection” or “intersectionrelated.” 

Right-angle 1 (RA1)—Vehicles approach intersection from perpendicular directions, in any category of 

head-on, head-on left-turn, opposite direction sideswipe, straight movement angle, angle meets rightturn, angle meets left-turn, or angle meets left head-on. 

Right-angle 2 (RA2)—Those in RA1 plus opposite direction left-turn. 

Rear end—All rear end crashes within 48.158 m (158 ft) and identified as “intersection” or “intersectionrelated.” 

Development of Safety Performance Functions 

As indicated earlier, the study required the development of safety performance functions (SPFs) 

for signalized and stop-controlled intersections. A reference group of untreated signalized 

intersections was used to develop SPFs to account for traffic volume changes and regression to 

the mean using the empirical Bayes procedure. The unsignalized intersection SPFs were used to 

account in that procedure for time trends in crash counts unrelated to the RLC installation. 

Therefore, it was necessary to first ensure that the comparison group used to calibrate the SPFs 

was suitable for this purpose, that is, that it had similar crash trends to the treatment group over 

the years before RLC installation. To this end a comparability test as outlined in Hauer was 

performed.(4) This test confirmed the suitability of the comparison group. 

To build the strongest possible SPFs, reference group data (i.e., data from the untreated 

signalized intersections) were combined for sets of jurisdictions, considering proximity and 

similarity in crash reporting practices. To this end, the three California cities of El Cajon, San 

Diego, and San Francisco were combined. Not only are these three cities in proximity, but they 

also do not have full reporting of PDO crashes, and the crash data all came from the State 

database maintained by the CHP. Howard and Montgomery Counties, MD, reference group data 

were combined because of their proximity and similarity in reporting practices. Baltimore, MD, 

and Charlotte, NC, were combined because of their high reporting of non-injury crashes. In each 

case where jurisdictions were combined, a jurisdiction-specific multiplier was calibrated and 

applied to account for any remaining differences in crash reporting. 

Development of the SPFs involved determining which explanatory variables should be used, 

whether and how variables should be grouped, and how variables should enter into the model, in 

other words, the best model form. Generalized linear modeling was used to estimate model 

coefficients using the software package GENSTAT and assuming a negative binomial error 

distribution, all consistent with the common recent research practice in developing these 

models.(29) 

In specifying a negative binomial error structure, the dispersion parameter, k, which relates the 

mean and variance of the regression estimate, is iteratively estimated from the model and the 

data. The value of k is such that the smaller its value, the better a model is for a given set of data. 
61 

For specific crash types at signalized intersections, a multiplier is applied to the model that is 

equal to the proportion of total crashes that each crash type makes up. A value of k was 

calculated for each crash type using a maximum likelihood process, as explained earlier. 

Similarly, although data for groups of jurisdictions were combined for SPF calibration, separate 

multipliers and k values were calculated for each jurisdiction. 

The inclusion of variables such as number of lanes rarely significantly affected the fit. This is not 

surprising because, as previous research has shown, much of the variation in crash experience is 

explained by the volume of traffic entering an intersection. The results of the SPF calibration for 

the signalized reference group are presented in table 12. The model forms used are tried and 

tested and, because of the limited datasets available, options on model forms and variables to 

include were so limited that a trial and error modeling approach, using published models as a 

guide, was realistic. In addition, fine tuning the model is not critical in EB analysis, especially 

because by weighting the observed count, one is accounting for omitted variables that may affect 

crash frequency. 
Table 12. Safety performance functions for the signalized intersections reference group

3-legged  El Cajon San Diego San Francisco Howard Co. Montgomery Co. Baltimore Model form crashes/year 

α(F1+F2)b α(F1+F2)bexp(minllane*e) α(F1)c(F2)dex

Ln(α) (s.e.) -5.240 (2.21) -5.651 (2.22) -5.240 (2.21) -6.970 

(1.800) 

-6.970 (1.800) -3.100 (1.240

B (s.e.) 0.580 (0.218) 0.580 (0.218) 0.580 (0.218) 0.709 

(0.183) 

0.709 (0.183) – 

C (s.e.) – – – – – 0.374 (0.119D (s.e.) – – – – – 0.136 (0.080E (s.e.) – – – 0.964 

(0.297) 

0.964 (0.297) – 

F (s.e.) – – – – – 0.264 (0.075Total α, k 1.00, 0.18 1.00, 0.28 1.00, 0.28 1.00, 0.30 1.00, 0.30 1.00, 0.56 Injury α, k 0.28, 0.13 0.31, 0.26 0.26, 0.26 0.12, 0.30 0.24, 0.21 0.15, 0.91 Right-angle α, k 

0.40, 0.67 0.35, 0.91 0.55, 0.91 0.35, 0.37 0.28, 0.14 0.44, 1.0 

Rear end α, k 0.41, 0.18 0.43, 0.25 0.22, 0.25 0.39, 0.63 0.44, 0.03 0.18, 1.1 4-legged  El Cajon San Diego San Francisco Howard 

County 

Montgomery 

County 

Baltimore 

Model form crashes/yr 

α(F1+F2)b exp(minrlane*e) α(F1)c(F2)d α(F1)c(F2)dex

Ln(α) (s.e.) -3.950 (2.010) -4.624 (2.021) -4.477 (2.021) -8.370 

(1.090) 

-8.370 (1.090) -3.100 (1.240) 

B (s.e.) 0.530 (0.197) 0.530 (0.197) 0.530 (0.197) C (s.e.) – – – 0.703 

(0.103) 

0.703 (0.103) 0.374 (0.119) 

D (s.e.) – – – 0.335 

(0.075) 

0.335 (0.075) 0.136 (0.080) 

E (s.e.) -0.279 (0.129) -0.279 (0.129) -0.279 (0.129) – – – F (s.e.) – – – – – 0.264 (0.075) Total α, k 1.00, 0.19 1.00, 0.24 1.00, 0.24 1.00, 0.20 1.00, 0.20 1.00, 0.56 Injury α, k 0.26, 0.14 0.29, 0.10 0.26, 0.10 0.16, 0.20 0.25, 0.25 0.15, 0.91 Right-angle α, k 

0.48, 0.34 0.42, 0.38 0.55, 0.38 0.38, 0.36 0.48, 0.45 0.44, 1.0 

Rear end α, k 0.32, 0.33 0.39, 0.48 0.22, 0.48 0.40, 0.45 0.32, 0.24 0.18, 0.9 Table Legend: F1 = entering AADT on major road, F2 = entering AADT on minor road; minllane = number of left-turn lanes on the minor road; 

majllane = number of left-turn lanes on the major road; minrlane = number of right-turn lanes on the minor road; (s.e.) = standard error of the esti

k is a calibrated parameter relating the mean and variance used in the empirical Bayes estimation procedure.

62 
63 

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. 

Right-angle Rear end 

Total crashes (Definite)

injury 

Total crashes (Definite)

injury 

EB estimate of crashes expected in 

the after-period without RLC 

1,542 351 2,521 131

Count of crashes observed in the 

After-period 

1,163 296 2,896 163

Estimate of percentage change 

(standard error) 

- 24.6

(2.9)

- 15.7

(5.9)

14.9 

(3.0) 

24.0

(11.6)

Estimate of the change 

in crash frequency 

- 379 - 55 375 32

Note: A negative sign indicates a decrease in crashes. 

Table 14. Results for individual jurisdictions 

Jurisdiction Right-angle crashes Rear end crashes 

number 

(in random order)a Percentage change 

(standard error) 

Percentage change 

(standard error) 

1 - 40.0 (5.4) 21.3 (17.1) 

2 + 0.8 (9.0) 8.5 (9.8) 

3 - 14.3 (12.5) 15.1 (14.1) 

4 - 24.7 (8.7) 19.7 (11.7) 

5 - 34.3 (7.6) 38.1 (14.5) 

6 - 26.1 (4.7) 12.7 (3.4) 

7 - 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. 
64 

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 afterperiods 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 

(2.2) 

1.8 

(2.3) 

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 redlight-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 
65 

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 
66 

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. 
67 

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 

K $4,090,042 $3,781,989 

A 120,810 84,820 

B 103,468 27,043 

C 34,690 49,746 

O 

(standard deviation) 

8,673 

(1,285) 

11,463 

(3,338) 

K+A+B+C “injury crash” 

(standard deviation) 

$64,468 

(11,919) 

$53,659 

(9,276) 

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-lightcamera systems. 
68 

Table 17. Economic effects* including and excluding PDOs. 

 All severities combined PDOs excluded 

 Rightangle 

crashes 

Rear 

end crashes 

All crashes RightAngle 

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. 

Percentage in each Injury category Crash type Total 

frequency K A B C 

Right-angle 

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 afterperiod (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 afterperiod right-angle crashes may be slightly underestimated, even when all jurisdictions are 
69 

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: 
70 

 

(11) 

 

where α is the calibrated intercept and b1, b2, ... , bn are the estimated effects on Фcost per afterperiod 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 fineonly 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 
71 

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 
72 

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 rightangle 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.
73 

XII. DISCUSSION AND CONCLUSIONS 

Red-light running at signalized intersections is a significant problem in the United States; it 

results in more than 95,000 crashes and approximately 1,000 deaths per year. Red-light-camera 

systems aimed at reducing this problem have become a popular tool in local jurisdictions. Their 

use has not been without controversy, primarily related to the use of private firms to implement 

the program, and questions concerning changes in signal timing during program implementation. 

Part of the controversy has stemmed from the lack of sound research concerning the effects of 

RLCs on intersection crashes. Many studies of RLC effectiveness were conducted in 

jurisdictions outside the United States, and most of the U.S. and non-U.S. studies have 

experienced methodological problems, as was documented by the critical review of literature 

conducted in this effort. This current study was an attempt to overcome these methodological 

issues and to examine the crash-related effects in multiple U.S. jurisdictions to see if consistent 

results were found. 

At the beginning of the study, the FHWA oversight panel defined a series of first-priority 

questions to be addressed. Following is a list of questions, followed by the study findings for 

each. 

• What effect do RLCs have on intersection safety (i.e., intersection crashes) at monitored 

intersections versus intersection safety throughout the jurisdiction? 

The results of empirical Bayes crash-frequency analyses at the treated intersections 

indicate that RLCs have effects similar in direction but somewhat smaller in magnitude 

than those indicated in past studies. Right-angle crashes (the surrogate for “red-lightrunning” crashes) decrease significantly and rear end crashes increase. Table 17 shows 

the combined results from all seven jurisdictions, indicating a 24.6 percent reduction in 

total right-angle crashes and a 15.7 percent reduction in right-angle (definite) injury 

crashes. Total rear end crashes increased by 14.9 percent, and rear end (definite) injury 

crashes increased by 24 percent. While the results varied some across the seven 

jurisdictions, the direction and degree were remarkably consistent, particularly given the 

differences in crash-reporting practices between jurisdictions. 

The results were not as clear for effects at other signalized intersections in the same 

jurisdiction, known as the “spillover” effects. Here, a modest decrease in right-angle 

crashes was seen (table 15), but, because the results did not show the expected 

companion increase in rear end crashes, there is some question of whether the observed 

difference is to the result of “spillover,” the difficulty in defining the before-and afterperiods for these untreated signalized intersections (because the treatments in all 

jurisdictions stretched across multiple years), or to other changes at these untreated 

locations during the study period. It appears that a well-designed prospective study will 

be needed to more confidently establish any spillover effect from RLCs. 

Finally, because the decrease in right-angle crashes was coupled with an increase in rear 

end crashes, because there can be more rear end crashes at intersections, and because the 

severities of the two crash types differ, it was important to combine both frequency- and 
74 

severity-related effects into one analysis to determine the overall effect of RLCs. This 

was estimated as the difference between the economic costs of crashes expected and 

observed in the after-period at the treated intersections, with the former cost based on the 

empirical Bayes expected crash frequency. Updated estimates of comprehensive and 

human capital costs per crash were developed for 22 crash types in this project, and those 

defined for right-angle, rear end, and other signalized intersection crashes were used in 

this analysis. The combined results from the seven jurisdictions indicated a positive 

aggregate economic benefit of approximately $39,000 per site per year when propertydamage-only (PDO) crashes are included and $50,000 per site per year when PDO 

crashes are excluded (table 21). These results indicate that the increase in rear end crash 

costs (due to the increase in frequency, with a lower severity) do not negate the savings in 

right-angle crash costs. 

 

• What is the relationship of signal timing (i.e., length of the yellow interval, length of the 

all-red interval, and various combinations of the yellow interval and all-red interval) 

with safety at intersections with RLCs? Later discussion indicated that the key factors of 

interest are yellow interval, all-red interval, cycle length, and signal coordination. The 

basic issue related to yellow time is the nature of the yellow phase length—e.g., a 

standard length, length based on ITE recommendations related to approach speeds and 

other factors, or some variation of these. The basic question for all-red phases is 

whether or not there is one (i.e., presence or absence of all-red phase). Cycle length is 

needed both to provide some measure of the number of red phases (and thus the number 

of opportunities for red-light-running) in a given time period, but also because longer red 

phases might “induce” more red-light-running. With respect to signal coordination, the 

issue is whether the treated signal approach is part of a set of coordinated signals that 

lead to queuing of vehicles (but not any additional details of the level of coordination). 

The analysis efforts here were focused on identifying signal-related factors that could 

increase or decrease RLC effects. Because such factors can affect both angle and rear 

end crashes, the outcome was based on the aggregate economic effects per year for a 

given treatment site. Univariate analyses identified factors associated with higher 

economic benefit, and regression models were used to verify the direction of univariate 

effects. (It was not possible to use the models to define either relative size or statistical 

significance of the individual effects, because this would require a much larger database 

and the ability to more precisely link a given signal attribute with a given location. In the 

existing database, as would be the case for almost all signalized intersections, signal 

timing changes both across years and even within a given day.) In general, it appears that 

the aggregate economic benefit increases with total entering AADT, an increasing ratio 

of right-angle crashes to rear end crashes, an increasing proportion of total traffic being 

on the major road, shorter cycle lengths, and shorter intergreen periods, and is greater for 

locations with one or more protected left-turn phases as opposed to intersections without 

such protection. Other factors such as traffic signal actuation, signal coordination, 

presence of turn restrictions, major road speed limit, and number of approach legs were 

also investigated; for these, the inability to detect a clear-cut effect may have been caused 

by the small samples for one level of the factor. 
75 

It is again noted that determining the relative importance of each of these factors was not 

possible, partly because of the noted modeling issues, but also because many of the 

variables are highly correlated with each other. It is further noted that these findings, 

even though not as detailed as might be desired, do provide guidance for implementers 

who want to maximize the potential benefit of RLC programs through good site choice. 

Given a set of potential RLC locations, 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 crashes to rear end crashes. 

• Are there certain improvements (e.g., signal timing, signage, geometric changes, etc.) 

done in conjunction with RLC installation that make the automated enforcement program 

more or less effective? Later discussion of the signage issue indicated that the key 

question has to do with presence or absence of “warning” signage, whether the sign is 

located at the intersection or away from the intersection (e.g., at the edge of town or at 

the beginning of a corridor), and whether informational signs providing data on the 

number of violations issued are used (because such signs have been shown to increase 

the effect of seatbelt enforcement programs and perhaps RLC programs in some cities). 

Using the same aggregate-economic-effect analysis methodology described in the 

preceding bullet, it appears that warning signs located at both the treated intersection and 

at the city limits were associated with a larger benefit than warning signs at intersections 

only. 

• If other improvements are made during the installation of RLCs, what portion of the 

change in intersection crashes is due to these improvements and what portion is due to 

the RLC? 

It was not possible to differentiate RLC effects from the effects of other changes at the 

time of treatment simply because there were virtually no sites where other changes were 

made at the same time according to information provided to the project team. 

• What effect does a “good” public information program have on safety at intersections 

with RLCs? 

Again, using the same methodology, high publicity level was found to be associated with 

a greater benefit than medium publicity level. 

• What is the effectiveness of “fine only” (i.e., owner liability) program versus “fine and 

points” (i.e., driver liability) program? (Second-level priority.)
76 

Again using the same methodology, “fine and points” was found to be associated with a 

greater benefit than “fine only.” 

In summary, the multijurisdiction database developed and the crash-based and economic 

analyses used made it possible to answer most of the questions posed by FHWA. This economic 

analysis represents the first attempt in the known literature to combine the positive effects of 

right-angle crash reductions with the negative effects of rear end crash increases, and to identify 

factors that might further enhance the effects of RLC systems. Larger crash sample sizes would 

have added even more information. The following primary conclusions are based on these 

current analyses: 

• Even though the positive effects on right-angle crashes of RLC systems is partially offset 

by negative effects related to increases in rear end crashes, there is still a modest to 

moderate economic benefit of between $39,000 and $50,000 per treated site year, 

depending on whether one examines only injury crashes or includes PDOs, and on 

whether the statistically non-significant shift to slightly more severe right-angle crashes 

remaining after treatment is, in fact, real. 

• Even if modest, this economic benefit is important. In many instances today, the RLC 

systems pay for themselves through red-light-running fines generated. However, in many 

jurisdictions, this differs from most safety treatments where there are installation, 

maintenance, and other costs that must be weighed against the treatment benefits. 

• The modest benefit per site is an average over all sites. As the analysis of factors that 

impact showed, this benefit can be increased through careful selection of the sites to be 

treated (e.g., sites with a high ratio of right-angle to rear end crashes as compared to other 

potential treatment sites) and program design (e.g., high publicity, signing at both 

intersections and jurisdiction limits). 

The authors close with two additional findings from the study. First, safety studies related to 

local road systems, as opposed to State highway systems, will be more expensive and difficult to 

conduct, and this should be considered in the research planning process. While most State DOTs 

have developed computerized databases containing crash, roadway inventory, and traffic data 

that can be used in research (including FHWA’s multi-State Highway Safety Information 

System), this study has further documented that such files (particularly historical data other than 

crash data) are not usually available in local jurisdictions. If available, the data are often not 

computerized, they are in multiple formats, and they are maintained by different offices; thus, 

jurisdiction choice is a critical part of such research planning, with the availability, types, and 

quality of data being key issues. 

Second, research involving signalization parameters such as yellow interval and cycle length is 

difficult. The difficulties surface not only because historic signal-change data may be difficult to 

acquire, but the research may be made more difficult by adaptive signal systems where the 

parameters change within a day. This is particularly true if traffic volumes by intersection 

approach cannot be linked to the specific sets of signal parameters; AADT is not usually 
77 

sufficient. Research on these topics is important, but careful planning will be required to ensure 

its success. 

APPENDIX A. SOURCES OF DATA BY JURISDICTION 

Table 19. Data sources. 

Data Item Baltimore, MD Charlotte, NC El Cajon, CA How

Crash data 

Maryland CODES data from National Study Center for Trauma and MS University of Maryland—Baltimore 

Charlotte Department of Transportation (DOT) 

California Highway Patrol Crash Database (SWITRS) 

Maryland National Trauma UniversitBaltimor

Intersection traffic counts 

From Baltimore Traffic Management Centers (TMCs), Maryland State Highway Administration counts, MPO (BMC) counts, and vendor loop data 

Database of total intersection entering volumes, hardcopied TMCs, midblock counts, and approach volumes on signal plans 

El Cajon turning movement counts and permanent station counts Link volumes from the area MPO (SANDAG) 

Howard midblock Maryland Administand midb

Number of approach lanes, medians, turn lanes 

Aerial photos from Baltimore GIS data 

Signalized: Most are from detailed signal plans, a few from aerial photographs Unsignalized: Estimated from aerial photos 

Signalized: Signal plans Unsignalized: Aerial photos, signal warranting study reports, and drawings of adjacent intersections 

SignalizeMDSHA photos 

Intersection “change” data 

Baltimore historical timing sheets 

No signal timing change data available. 

El Cajon historical signal timing sheets 

From hisand log mCounty s

Traffic signal phasing data 

Baltimore printouts of current signal timing Charlotte signal timing system El Cajon printouts of current signal timing Howard 

signal pl

LEDs and backplates No data available From signal drawings Used by El Cajon, but no formal installation dates were available From log 

Howard 

RLC installation date 

City of Baltimore Traffic Engineering Division Charlotte DOT El Cajon Traffic Engineering Division and Police Howard 

RLC signing City of Baltimore policy Charlotte DOT policy El Cajon Traffic Engineering Division policy Howard RLC ticket protocol 

City of Baltimore Traffic Engineering Division Charlotte DOT El Cajon Traffic Engineering Division Howard 

79
Table 19. Data sources. Continued. 

Data item Montgomery County, MD San Diego, CA San Franc

Crash data 

Maryland CODES data from National Study Center for Trauma and MS University of Maryland—Baltimore 

California Highway Patrol Crash Database (SWITRS) 

California Highway PaDatabase (SWITRS) 

Intersection traffic counts 

Montgomery County ADTs and TMCs 

San Diego machine counts and turning movement counts 

San Francisco ADT fileturning movement coucounts State-maintained ADT

Number of approach lanes, median presence, presence of turn lanes 

Signalized: Signal plans supplemented with aerial photos Unsignalized: Intersection drawings on TMCs 

Signalized: Signal plans (outdated), aerial photographs, non-scale drawings from report Unsignalized: Work-order requests, sketches on turning movement counts, and coders assumptions based on volume 

Signal plans, pavemendrawings, and aerial p

Intersection “change” data 

Montgomery County historical signal timing files San Diego historical timing files San Francisco historic

Traffic signal phasing data 

Montgomery County signal timing files San Diego signal timing files San Francisco signal t

LEDs and backplates These data are not maintained Requested but never received from City of San Diego maintenance staff Although the city uses 

able to provide installa

RLC installation date Montgomery County Department of Public Works (DPW) City of San Diego Traffic Engineering Division San Francisco Departm

Traffic (DPT) 

RLC signing Montgomery County DPW provided general information City of San Diego Traffic Engineering Division policy San Francisco signing RLC ticket protocol Montgomery County DPW City of San Diego Traffic Engineering Division San Francisco DPT 

80
81 

APPENDIX B. NUMBER OF TREATED, REFERENCE, AND 

COMPARISON SITES IN EACH JURISDICTION. 

Table 20. Sites by jurisdiction. 

Jurisdiction Treated 

Sites 

Signalized 

Reference 

and Spillover 

Analysis Sites 

Unsignalized 

Comparison 

Sites 

Baltimore, MD 19 86 46 

Charlotte, NC 31 74 42 

El Cajon, CA 6 53 38 

Howard County, MD 18 34 38 

Montgomery County, MD 21 55 40 

San Diego, CA 19 54 44 

San Francisco, CA 18 52 48 

Total 132 408 296 

83 

ACKNOWLEDGMENTS 

This study was funded by the Federal Highway Administration and would not have been possible 

without the enthusiastic cooperation by officials in the study jurisdictions in assembling the 

database. These jurisdictions include: Baltimore, MD; Charlotte, NC; El Cajon, CA; Howard 

County, MD; Montgomery, MD; San Diego, CA; and San Francisco, CA. The fundamental 

research that led to some of the methodological ideas used in the study was funded under an 

operating grant to Bhagwant Persaud (Ryerson University) from the National Sciences and 

Engineering Research Council of Canada (NSERC). The photo on the cover of the document is 

used with permission from TRIMARC/Northrop Grumman Mission Systems.

85 

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