DOT HS 810 580 March 2006 

 

 

Analysis of Red Light Violation Data Collected from 

Intersections Equipped with Red Light Photo 

Enforcement Cameras 

Research and 

Innovative Technology 

Administration 

Volpe National 

Transportation Systems Center, 

Cambridge, MA 02142-1093 

This document is available to the public through the National Technical Information Service, Springfield, VA 22161 
ii

NOTICE 

This document is disseminated under the sponsorship of the 

Department of Transportation in the interest of information 

exchange. The United States Government assumes no liability for 

its contents or use thereof. 
REPORT DOCUMENTATION PAGE Form Approved 

OMB No. 0704-0188 

Public reporting burden for this collection of information is estimated to average one hour per response, including the time for reviewing instructions, searching existing data 

sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other 

aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 

1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188), Washington, DC 

20503. 

1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE 

March 2006 

3. REPORT TYPE AND DATES COVERED 

Project Memorandum 

October 2003 – October 2005 

4. TITLE AND SUBTITLE 

Analysis of Red Light Violation Data Collected from Intersections Equipped with Red Light 

Photo Enforcement Cameras 

6. AUTHOR(S) 

C. Y. David Yang and Wassim G. Najm 

shes 

5. FUNDING NUMBERS 

PPA # HS-19 

. 

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 

U.S. Department of Transportation 

Research and Innovative Technology Administration 

John A. Volpe National Transportation Systems Center 

Cambridge, MA 02142 

8.PERFORMING ORGANIZATION 

REPORT NUMBER 

DOT-VNTSC-NHTSA-05-01 

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 

U.S. Department of Transportation 

National Highway Traffic Safety Administration 

400 7th Street SW. 

Washington, DC 20590 

10. SPONSORING/MONITORING 

 AGENCY REPORT NUMBER 

DOT HS 810 580

11. SUPPLEMENTARY NOTES 

12a. DISTRIBUTION/AVAILABILITY STATEMENT 

This document is available to the public through the National Technical Information Service, 

Springfield, Virginia 22161. 

12b. DISTRIBUTION CODE 

13. ABSTRACT (Maximum 200 words) 

This report presents results from an analysis of about 47,000 red light violation records collected from 11 intersections in the 

City of Sacramento, California, by red light photo enforcement cameras between May 1999 and June 2003. The goal of this 

analysis is to understand the correlation between red light violations and various driver, intersection, and environmental factors. 

Descriptive statistics suggest that younger drivers under 30 years of age are more likely to run red lights than drivers in other age 

groups. About 56 percent of the violators were traveling at or below the posted speed limit. Moreover, 94 percent of the 

violations occurred within 2 seconds after the onset of red light, and only 3 percent of the violations were recorded 5 seconds

after the onset of red light. Approximately 4 percent of the violators were repeat offenders. Logistic regression modeling shows 

that the predicted odds of a younger driver running a red light at speeds greater than the speed limit is about 1.5 times the odds of 

a middle-aged driver. In addition, older drivers have a higher probability of running a red light when the elapsed time since the 

onset of red light is more than 2 seconds compared to younger drivers. Finally, red light violations rates are estimated between 6 

and 29 violations per 100,000 intersection-crossing vehicles. 

15. NUMBER OF PAGES 

70 

14. SUBJECT TERMS 

Red light violations, signalized intersections, Intelligent Vehicle Initiative, red light photo 

enforcement cameras, cooperative signal violation warning system 16. PRICE CODE 

17. SECURITY CLASSIFICATION 

 OF REPORT 

 Unclassified

18. SECURITY CLASSIFICATION 

 OF THIS PAGE 

 Unclassified

19. SECURITY CLASSIFICATION 

 OF ABSTRACT 

 Unclassified

20. LIMITATION OF ABSTRACT 

NSN 7540-01-280-5500 Standard Form 298 (Rev. 2-89) 

Prescribed by ANSI Std. 239-18 

298-102
PREFACE 

The National Highway Traffic Safety Administration (NHTSA), in conjunction with the 

Research and Innovative Technology Administration’s Volpe National Transportation 

Systems Center (Volpe Center), conducted a preparatory analysis for a potential field 

operational test of a vehicle-intersection cooperative signal violation warning system that 

addresses crossing path crashes at signalized intersections. This analysis supports the 

Intelligent Vehicle Initiative (IVI) of the U.S. Department of Transportation. The IVI 

accelerates the development and deployment of vehicle-based and vehicle-infrastructure 

cooperative crash countermeasures using advanced technologies over several problem 

areas: rear-end, roadway departure, lane change, crossing paths, driver impairment, 

reduced visibility, vehicle instability, pedestrian, and pedalcyclist crashes. 

This report presents the results obtained from the data analysis of about 47,000 red light 

violation records collected by the City of Sacramento, California, using red light photo 

enforcement cameras at 11 intersections during the time period between May 1999 and 

June 2003. 

The authors of this report are C. Y. David Yang and Wassim G. Najm of the Volpe 

Center. 

The authors acknowledge the technical contribution of Dr. David L. Smith and Kerrin 

Bressant of NHTSA. Special thanks are extended to Robert Ferlis of the Federal 

Highway Administration and Matthew T. Schmitz at the California Division of the 

Federal Highway Administration for recommending and contacting the City of 

Sacramento regarding the red light violation data used for this study. The authors also 

want to thank Police Chief Albert Najera and Sergeant Eric Poerio of the Sacramento 

Police Department for their willingness to share the red light violation data with the 

Volpe Center. Moreover, Angie Louie Fong and her staff from the Sacramento 

Department of Public Works’ Traffic Engineering Services were very helpful in 

providing useful traffic and infrastructure information regarding the City of Sacramento’s 

signalized intersections. Finally, the authors would like to acknowledge John Flynn, Lon 

B. Ecklund, and their staff at Affiliated Computer Services, Inc. (ACS) for their diligent 

efforts in organizing and sanitizing Sacramento’s red light violation records so they could 

be used for this study. (Note: Sacramento Police Department hired ACS to handle and 

process its red light violation data.) 
ii

METRIC/ENGLISH CONVERSION FACTORS

ENGLISH TO METRIC METRIC TO ENGLISH

LENGTH (APPROXIMATE) LENGTH (APPROXIMATE) 

1 inch (in) = 2.5 centimeters (cm) 1 millimeter (mm) = 0.04 inch (in) 

1 foot (ft) = 30 centimeters (cm) 1 centimeter (cm) = 0.4 inch (in) 

1 yard (yd) = 0.9 meter (m) 1 meter (m) = 3.3 feet (ft) 

1 mile (mi) = 1.6 kilometers (km) 1 meter (m) = 1.1 yards (yd) 

 1 kilometer (km) = 0.6 mile (mi) 

AREA (APPROXIMATE) AREA (APPROXIMATE) 

1 square inch (sq in, in2) = 6.5 square centimeters (cm2) 1 square centimeter (cm2) = 0.16 square inch (sq in, in2) 

1 square foot (sq ft, ft2) = 0.09 square meter (m2) 1 square meter (m2) = 1.2 square yards (sq yd, yd2) 

1 square yard (sq yd, yd2) = 0.8 square meter (m2) 1 square kilometer (km2) = 0.4 square mile (sq mi, mi2) 

1 square mile (sq mi, mi2) = 2.6 square kilometers (km2) 10,000 square meters (m2) = 1 hectare (ha) = 2.5 acres 

1 acre = 0.4 hectare (he) = 4,000 square meters (m2) 

MASS - WEIGHT (APPROXIMATE) MASS - WEIGHT (APPROXIMATE) 

1 ounce (oz) = 28 grams (gm) 1 gram (gm) = 0.036 ounce (oz) 

1 pound (lb) = 0.45 kilogram (kg) 1 kilogram (kg) = 2.2 pounds (lb) 

1 short ton = 2,000 pounds (lb) = 0.9 tonne (t) 1 tonne (t) = 

= 

1,000 kilograms (kg) 

1.1 short tons 

VOLUME (APPROXIMATE) VOLUME (APPROXIMATE) 

1 teaspoon (tsp) = 5 milliliters (ml) 1 milliliter (ml) = 0.03 fluid ounce (fl oz) 

1 tablespoon (tbsp) = 15 milliliters (ml) 1 liter (l) = 2.1 pints (pt) 

1 fluid ounce (fl oz) = 30 milliliters (ml) 1 liter (l) = 1.06 quarts (qt) 

1 cup (c) = 0.24 liter (l) 1 liter (l) = 0.26 gallon (gal) 

1 pint (pt) = 0.47 liter (l) 

 1 quart (qt) = 0.96 liter (l) 

1 gallon (gal) = 3.8 liters (l) 

1 cubic foot (cu ft, ft3) = 0.03 cubic meter (m3) 1 cubic meter (m3) = 36 cubic feet (cu ft, ft3) 

1 cubic yard (cu yd, yd3) = 0.76 cubic meter (m3) 1 cubic meter (m3) = 1.3 cubic yards (cu yd, yd3) 

TEMPERATURE (EXACT) TEMPERATURE (EXACT) 

[(x-32)(5/9)] °F = y °C [(9/5) y + 32] °C = x °F 

QUICK INCH - CENTIMETER LENGTH CONVERSION

0 1 2 34 5

Inches

Centimeters 01 3 2 6 11 4 5 7 13 8 9 10 12

QUICK FAHRENHEIT - CELSIUS TEMPERATURE CONVERSION

 -40° -22° -4° 14° 32° 50° 68° 86° 104° 122° 140° 158° 176° 194° 212°

 

°F

 °C -40° -30° -20° -10° 0° 10° 20° 30° 40° 50° 60° 70° 80° 90° 100°

For more exact and or other conversion factors, see NIST Miscellaneous Publication 286, Units of Weights and 

Measures. Price $2.50 SD Catalog No. C13 10286. Updated 6/17/98
iii

TABLE OF CONTENTS 

EXECUTIVE SUMMARY....................................................................................... vii 

1. INTRODUCTION..................................................................................................... 1 

1.1. Background ............................................................................................................ 1 

1.2. Report Outline........................................................................................................ 2 

2. LITERATURE REVIEW ON RED LIGHT VIOLATION ........................... 3 

2.1. Definitions of Red Light Violation........................................................................ 3 

2.2. Effects of Driver, Intersection, and Environment on Red Light Violations.... 5 

2.3. Red Light Violation Countermeasures............................................................... 8 

3. STATISTICAL DESCRIPTION OF RED LIGHT VIOLATIONS ........... 9 

3.1. Overview of Sacramento's Red Light Running Program .................................. 9 

3.2. Picture Processing Procedures............................................................................ 10 

3.3. Descriptive Statistics............................................................................................ 11 

3.3.1. General Information on Red Light Violation Records ........................................................ 11

3.3.2. Information on RLPEC-Equipped Signalized Intersections................................................ 13

3.3.3. Frequency Distributions of Selected Variables................................................................... 14

3.4. Estimates of Red Light Violation and Crash Rates.......................................... 25 

4. STATISTICAL MODELING OF RED LIGHT VIOLATION DATA .... 30 

4.1. Logistic Modeling Method .................................................................................. 30 

4.2. Factors with Significant Influence on Violators’ Vehicle Speed ..................... 31 

4.3. Factors with Significant Influence on Time Elapsed since Red Light Onset . 35 

5. CONCLUDING REMARKS ............................................................................... 39 

5.1. List of Major Findings......................................................................................... 39 

5.2. Implications of Findings...................................................................................... 40 

6. REFERENCES......................................................................................................... 41 

Appendix A. Photos of City of Sacramento’s 11 RLPEC-Equipped 

Intersections............................................................................................................. 44 

Appendix B. Photos of Red Light Photo Enforcment Camera and Warning 

Sign ............................................................................................................................ 50 

Appendix C. GIS Map of a Selected RLPEC-Equipped Intersection in 

Sacramento............................................................................................................... 51 

Appendix D. Distributions of Red Light Violations by Speed at Individual 

11 RLPEC-Equipped Intersections.................................................................... 52 
iv

LIST OF FIGURES 

Figure 1. Location of Sacramento’s 11 RLPEC-Equipped Intersections ........................ 12 

Figure 2. Normalized Red Light Violation Values by Age Group .................................. 16 

Figure 3. Distribution of Red Light Violations by Time of Day ..................................... 17 

Figure 4. Distribution of Red Light Violation Records by Vehicle Year........................ 18 

Figure 5. Distribution of Red Light Violation Records by Vehicle Speed...................... 19 

Figure 6. Percentage Cumulative Distribution of Red Light Violation Records by Vehicle 

Speed......................................................................................................................... 19 

Figure 7. Percentage Distribution of Violation Records by [Vehicle Speed – PSL]....... 21 

Figure 8. Percentage Cumulative Distribution of Records by [Vehicle Speed – PSL] ... 21 

Figure 9. Distribution of Violation Records by Elapsed Time Since Red Light Onset 

(Vertical Axis in Logarithmic Scale)........................................................................ 22 

Figure 10. Percentage Distribution of Violation Records by Time Elapsed Since Red 

Light Onset................................................................................................................ 22 

Figure 11. Percentage Cumulative Distribution of Violations by Time Elapsed Since Red 

Light Onset................................................................................................................ 23 

Figure 12. Picture of Fair Oaks Boulevard and Howe Avenue Intersection.................... 44 

Figure 13. Picture of El Camino Avenue and Evergreen Street Intersection .................. 44 

Figure 14. Picture of Arden Way and Exposition Boulevard Intersection ...................... 45 

Figure 15. Picture of Mack Road and La Mancha Way/Valley Hi Drive Intersection.... 45 

Figure 16. Picture of Mack Road and Center Parkway Intersection................................ 46 

Figure 17. Picture of 30th Street and Capitol Avenue Intersection .................................. 46 

Figure 18. Picture of J Street and Alhambra Boulevard Intersection .............................. 47 

Figure 19. Picture of Broadway and 21st Street Intersection ........................................... 47 

Figure 20. Picture of W Street and 16th Street [Highway 50 Exit at 16th Street] 

Intersection................................................................................................................ 48 

Figure 21. Picture of Howe Avenue and College Town Drive Intersection.................... 48 

Figure 22. Picture of Power Inn Road and Folsom Boulevard Intersection .................... 49 

Figure 23. Picture of Red Light Photo Enforcment Camera in the City of Sacramento.. 50 

Figure 24. Picture of Red Light Photo Enforcment Camera Warning Sign in the City of 

Sacramento................................................................................................................ 50 

Figure 25. GIS Mapping of Fair Oaks Boulevard and Howe Avenue Intersection......... 51 

Figure 26. Frequency Distribution of Red Light Violations by Speed at Fair Oaks 

Boulevard and Howe Avenue Intersection ............................................................... 52 

Figure 27. Frequency Distribution of Red Light Violations by Speed at El Camino 

Avenue and Evergreen Street Intersection................................................................ 53 

Figure 28. Frequency Distribution of Red Light Violations by Speed at Arden Way and 

Exposition Boulevard Intersection............................................................................ 53 

Figure 29. Frequency Distribution of Red Light Violations by Speed at Mack Road and 

La Mancha Way/Valley Hi Drive Intersection ......................................................... 54 

Figure 30. Frequency Distribution of Red Light Violations by Speed at Mack Road and 

Center Parkway Intersection..................................................................................... 54 

Figure 31. Frequency Distribution of Red Light Violations by Speed at 30th Street and 

Capitol Avenue Intersection ..................................................................................... 55 
v

Figure 32. Frequency Distribution of Red Light Violations by Speed at J Street and 

Alhambra Boulevard Intersection............................................................................. 55 

Figure 33. Frequency Distribution of Red Light Violations by Speed at Broadway and 

21st Street Intersection............................................................................................... 56 

Figure 34. Frequency Distribution of Red Light Violations by Speed at W Street and 16th

Street [Highway 50 Exit at 16th Street] Intersection ................................................. 56 

Figure 35. Frequency Distribution of Red Light Violations by Speed at Howe Avenue 

and College Town Drive Intersection....................................................................... 57 

Figure 36. Frequency Distribution of Red Light Violations by Speed at Power Inn Road 

and Folsom Boulevard Intersection .......................................................................... 57 
vi

LIST OF TABLES 

Table 1. Definitions of Red Light Violation by Different Studies/Jurisdictions ............... 4 

Table 2. Effects of Driver, Intersection, and Environment on Red Light Violations ........ 5 

Table 3. Red Light Violation Rates Reported by Previous Studies ................................... 7 

Table 4. Suggested Red Light Violation Countermeasures ............................................... 8 

Table 5. Distribution of Red Light Violation Records and Their Collection Period ....... 13 

Table 6. Traffic and Infrastructure Characteristics of the 11 RLPEC-Equipped 

Intersections .............................................................................................................. 15 

Table 7. Distribution of Red Light Violation Records by Age Group............................. 16 

Table 8. Distribution of Red Light Violation Records by Gender................................... 17 

Table 9. Distribution of Violation Records by Vehicle Speed in Comparison to the 

Posted Speed Limit ................................................................................................... 20 

Table 10. Distribution of Red Light Violation Records by Selected Categories of Time 

Elapsed Since Red Light Onset................................................................................. 23 

Table 11. Percentage Cumulative Distribution of Violations by Elapsed Time Since Red 

Light Onset for Various Vehicle Speeds .................................................................. 24 

Table 12. Percentage Cumulative Distribution of Violations by Elapsed Time Since Red 

Light Onset for Various Delta Speeds ...................................................................... 24 

Table 13. Statistics on Repeat Red Light Offenders ........................................................ 25 

Table 14. Estimates of Red Light Violation Rates from 11 RLPEC-Equipped 

Intersections .............................................................................................................. 27 

Table 15. Collision Rates from Sacramento’s RLPEC-Equipped Intersections .............. 29 

Table 16. Dependent and Explanatory Variables Used in Logistic Regression Analysis 32 

Table 17. Estimation Results for the Binary Logit Model with “Speed2” as Dependent 

Variable..................................................................................................................... 33 

Table 18. Estimation Results from Binary Logit Model with “RedEla2” as Dependent 

Variable..................................................................................................................... 37 
vi

LIST OF ACRONYMS 

ACS Affiliated Computer Services 

ADT Average Daily Traffic 

CSVWS Cooperative Signal Violation Warning System 

FOT Field Operational Test 

GIS Geographic Information System 

ITE Institute of Transportation Engineers 

IVI Intelligent Vehicle Initiative 

LD Licensed Driver 

MUTCD Manual on Uniform Traffic Control Devices 

MVMT Million Vehicle Miles Traveled 

NHTSA National Highway Traffic Safety Administration

PSL Posted Speed Limit 

RLPEC Red Light Photo Enforcement Camera 

RLV Red Light Violation 
vii

EXECUTIVE SUMMARY 

The goal of the Intelligent Vehicle Initiative (IVI) of the U.S. Department of 

Transportation is to accelerate the development and deployment of vehicle-based and 

vehicle-infrastructure cooperative crash countermeasures using advanced technologies 

over several problem areas: rear-end, roadway departure, lane change, crossing paths, 

driver impairment, reduced visibility, vehicle instability, pedestrian, and pedalcyclist 

crashes. As part of the crossing path crash problem area, the National Highway Traffic 

Safety Administration has tasked the Volpe National Transportation Systems Center 

(Volpe Center) to conduct preparatory analysis for a potential field operational test of a 

vehicle-intersection cooperative signal violation warning system (CSVWS). It is 

envisioned that such system will provide advisory/warning messages to the driver of a 

moving vehicle who is about to run a red light because of failure to: 

− Recognize the presence and status of the traffic signal, or 

− Judge the adequate time to safely clear a signalized intersection. 

To develop, test, and design an effective CSVWS that can prevent red light violation, it is 

necessary to identify the causal factors and circumstances of red light violations. It is 

also important to understand the correlation between red light violations and various 

driver, intersection, and environmental factors. 

With assistance from the Police and Public Works Departments in the City of 

Sacramento, California, the Volpe Center has received data from four years of red light 

violation records that were gathered by red light photo enforcement cameras (RLPECs) at 

11 signalized intersections in the City of Sacramento. Volpe Center staff analyzed the 

red light violation data to identify factors with strong correlation to red light running 

behavior. Findings from this study are important to the estimation of safety benefits, and 

development of performance specifications and objective test procedures for the 

CSVWS. 

RLPECs at the City of Sacramento’s signalized intersections are activated to photograph 

potential violators when the following conditions are met: 

− Vehicle enters the intersection after the signal light had been red for a minimum 

elapsed time of 0.2 second, and 

− The measured speed of violating vehicle is more than 15 mph (on straight-through 

lane) or 13 mph (on left-turn lane). 

Several pictures are taken to capture the vehicle crossing the stop bar after the onset of a 

red signal, license plate number of the violating vehicle, and the person who is driving 

the violating vehicle. A review of these pictures is then conducted by the police to decide 

on whether or not a citation is warranted. No violation citation is issued if: 

− Pictures do not clearly show that the vehicle crossed the stop bar after the signal 

turned red. 
viii

− Registered owner of the violating vehicle does not match the driver who ran the 

red light. 

Based on one estimate, only 35 percent of the pictures taken by Sacramento’s RLPECs 

turn into violation citations. Red light violation records used in this analysis only include 

the actual violation citations issued by the Sacramento Police Department. Data from a 

total of 46,997 violation records (May 1999 to June 2003) were provided to the Volpe 

Center. The following variables are included in this data set: 

− Intersection (and intersection code) where the violation occurred 

− Date when the violation occurred 

− Time when the violation occurred 

− Age of the violator 

− Gender of the violator 

− Car (i.e., vehicle make) driven by the violator 

− Model year of the vehicle driven by the violator 

− Vehicle speed (i.e., measured speed) at the time of the violation 

− Elapsed time from the onset of the red signal until the time of the violation 

Descriptive statistics from the analysis of the red light violation data suggest that younger 

drivers under 30 years of age are more likely to run the red light than drivers in other age 

groups. In addition, most red light violations occur during the daytime (i.e., 7 a.m. to 7 

p.m.) with highest counts of red light violations during the period from 2:00 p.m. to 2:59 

p.m. The three most frequent vehicle speeds at the time of the violation were: 18 mph 

(1,762 records), 17 mph (1,715 records), and 19 mph (1,711 records). The average red 

light violation speed was 31.6 mph. It should be noted that the highest posted speed limit 

among these 11 RLPEC-equipped intersections is 45 mph. About 18 percent of the 

violators ran the red light at speeds higher than 45 mph. Moreover, about 56 percent of 

the violators were traveling at or below the posted speed limit (i.e., not speeding). The 

City of Sacramento’s RLPECs have captured drivers crossing the intersection from 0.2 

second (6,381 records) to more than 30 seconds (434 records) after the onset of the red 

light. Approximately 94 percent of the red light violations occurred within 2 seconds after 

the onset of the red light, and only 3 percent of the violations were recorded after 5 

seconds from the onset of the red light. As for repeat offenders, about 4 percent of the 

violators had more than one red light violation. 

Logistic regression modeling indicates that as the age of the red light violator increases, 

the probability of running the red light at vehicle speeds greater than the posted speed 

limit (i.e., speeding) decreases. The predicted odds of a younger driver running a red 

light while speeding is about 1.5 times the odds of a middle-aged driver doing so. 

Moreover, logistic regression modeling also shows that drivers who run red lights 

between 6 a.m. and 7 p.m. have a lower probability of speeding than violators in the time 

period between 7 p.m. and 6 a.m. Furthermore, red light violators at intersections with 

heavy traffic volumes have a lower probability of speeding. 
ix

Another logistic regression model shows that older drivers have a higher probability of 

running a red light when the elapsed time since the onset of red light is more than 2 

seconds compared to younger drivers. Also, drivers who run red lights between 6 a.m. 

and 7 p.m. have a lower probability of crossing intersections when the elapsed time from 

the onset of red light is more than 2 seconds compared to motorists who run red lights 

between 7 p.m. and 6 a.m. Another key finding from the logistic regression model is that 

motorists who run a red light at the intersection with the highest clearance interval (i.e., 

yellow time and all-red time) are more likely to drive through the intersection when the 

elapsed time since the onset of red light is more than 2 seconds. 

Finally, red light violation rates (calculated from issued violation citations) for the 11 

RLPEC-equipped intersections are estimated between 6 and 29 violations per 100,000 

crossing vehicles. In comparison to red light violation rates reported by other studies, 

these estimated red light violation rates are quite low. This is probably due to the fact 

that these estimated rates were based on actual police citations. It should be noted that 

only 35 percent of the photos taken by the City of Sacramento’s RLPECs become red 

light violation citations. The results of this study have demonstrated that drivers in 

different age groups exhibit diverse behavior when approaching signalized intersections. 

Hence, the experimental design for the CSVWS field operational test must examine the 

influence of driver age. The logistic regression models developed in this study suggest 

that several variations of the CSVWS warning algorithm and warning messages might be 

necessary for different time periods throughout the day. At certain time period(s) when 

drivers are susceptible to speeding through intersections or entering intersections late 

when the light changes, CSVWS warnings need to be issued earlier and warning 

messages need to be decisive to effectively encourage more drivers to stop for the red 

light. 
1

1. INTRODUCTION 

1.1. Background 

A total of 9,951 vehicles were involved in fatal crashes at traffic signals in 1999 and 2000 based 

on the Fatality Analysis Reporting System crash database, with 20 percent of these vehicles 

failing to obey the signals (Campbell et al., 2004). From 1992 to 1998, an estimated 1.5 million 

people were injured in crashes related to red light violation (Insurance Institute for Highway 

Safety, 2000). Intersections are among the most dangerous locations on U.S. roadways, and red 

light running is a major transportation safety challenge at signalized intersections. 

The Intelligent Vehicle Initiative (IVI) of the U.S. Department of Transportation (DOT) was 

established in 1998 to address a number of crash types by accelerating the development, 

availability, and use of driving assistance and control intervention systems. The IVI emphasizes 

the significant and continuing role of drivers in roadway safety, and aims at helping drivers 

process information, make decisions, and operate vehicles more safely. 

As part of the crossing path crash problem area, the National Highway Traffic Safety 

Administration (NHTSA) has tasked the Volpe National Transportation Systems Center (Volpe 

Center) to conduct preparatory analysis for a potential field operational test (FOT) of a vehicleintersection cooperative signal violation warning system (CSVWS). It is envisioned that such a 

system will provide advisory/warning messages to the driver of a moving vehicle who is about to 

run a red light because of failure to: 

− Recognize the presence and status of the traffic signal, or 

− Judge the adequate time to safely clear a signalized intersection. 

When warning messages are issued, the driver decides on the appropriate action to take in 

response to the warnings. Conceptually, the CSVWS will have an infrastructure-based 

component and a vehicle-based component. The infrastructure-based component establishes a 

communication channel between the operational units of a signalized intersection (e.g., controller 

and detection system) and a moving vehicle. The vehicle-based component of the CSVWS acts 

on the information provided by the intersection and vehicle sensors to detect the potential of a 

red light running activity and provide suitable warning messages to the driver. 

To develop, test, and design an effective CSVWS that can prevent red light violations, it is 

necessary to identify the causal factors and circumstances of red light violations. Specifically, 

the correlation between red light violations and various driver, intersection, and environmental 

factors needs to be studied so as to address the following questions: 

− Are there certain types of drivers with higher red light running tendency? 

− Are there some intersection characteristics that contribute to higher red light violation 

rates? 

− Are there certain environmental factors that lead to higher red light violation rates? 
2

With assistance from the Police and Public Works Departments in the City of Sacramento, 

California, the Volpe Center has received data from four years of red light violation records 

gathered by the red light photo enforcement cameras (RLPECs) at 11 signalized intersections in 

Sacramento. Volpe Center staff analyzed the data to identify factors with strong correlation to 

red light running behavior. Significant factors determined by this research effort are important to 

the estimation of safety benefits, and development of performance specifications and objective 

test procedures for the CSVWS. In addition, red light violation rates from these 11 RLPECequipped intersections are estimated and compared to violation rates reported by other 

researchers. 

1.2. Report Outline 

This report consists of five major sections. The first section presents the objectives of this study 

and related background information. The second section summarizes red light definitions 

adopted by other researchers, and lists the factors that showed strong correlation to red light 

violation as well as various red light violation rates reported by previous researchers. The third 

section introduces the red light violation data gathered by the RLPECs in the City of Sacramento, 

California. Moreover, Sacramento’s Red Light Running Program is described along with the 

procedures used to process red light violation citations. The third section also presents some 

descriptive statistics about red light violations. Furthermore, red light violation and crash rates at 

RLPEC-equipped intersections in Sacramento are also estimated. The fourth section delineates 

significant factors with strong correlation to red light violation, which were determined using 

statistical methods. Finally, the fifth section of this report summarizes and discusses the 

implications of key findings from the analysis of Sacramento’s red light violation data. 
3

2. LITERATURE REVIEW ON RED LIGHT VIOLATION 

This section summarizes results of previous studies on red light violation, which help to address 

the following questions: 

1. What constitutes a red light violation? 

2. What driver, intersection, and roadway characteristics are prevalent to red light 

violations? 

3. What are some of the strategies recommended for the reduction of red light violations? 

2.1. Definitions of Red Light Violation 

The Manual on Uniform Traffic Control Devices (December 2000), or MUTCD, defines traffic 

signal indications (i.e., green, yellow, and red signals) as follows: 

− Steady green signal indicates that vehicle “may proceed straight or turn right or left (at an 

intersection) except as such movement is modified by lane-use signs, turn prohibition 

signs, lane markings, or roadway design.” 

− Steady yellow signal indicates that vehicle “is thereby warned that the related green 

movement is being terminated or that a red signal indication will be exhibited 

immediately thereafter when vehicular traffic shall not enter the intersection”. 

− Steady red signal indicates that vehicle “shall stop at a clearly marked stop line, but if 

there is no stop line, traffic shall stop before entering the crosswalk on the near side of the 

intersection; or if there is no crosswalk, then before entering the intersection, and shall 

remain stopped until a signal indication to proceed is shown.” 

Based on the definitions listed above, drivers should be prepared to stop when they see the 

yellow signal and stop completely when the signal changes to red. Unfortunately, many drivers 

fail to observe and comply with traffic signal changes. Consequently, red light violation is one 

of the major causes for crossing path crashes at signalized intersections. Research studies have 

reported various factors for red light violations such as timing of the traffic signal, driving 

environment, and driver characteristics. The probability of crossing path crashes occurring at a 

signalized intersection increases as the rate of red light violation increases. A clear definition of 

a red light violation by the MUTCD is “... when a driver fails to stop at the presence of a steady 

red signal indication.” However, the interpretation for red light violation changes among the 

various studies and jurisdictions as presented in Table 1. 

As seen in Table 1, there is no consistent definition of red light violation. Many factors such as 

engineering considerations, environmental variables, and driver demographics could all have an 

effect on a local jurisdiction’s policy for red light violation. For instance, the interpretation of a 

red light violation in a large city with hilly streets and a serious traffic congestion problem would 

probably be lax compared to a rural town with level streets and rare traffic congestion. 

The City of Sacramento’s RLPECs are activated to photograph potential violators when the 

following conditions are met: 
4

− Vehicle enters the intersection after the signal light had been red for a minimum elapsed 

time of 0.2 second, and 

− The measured speed of violating vehicle is more than 15 mph (on straight-through lane) 

or 13 mph (on left-turn lane). 

Table 1. Definitions of Red Light Violation by Different Studies/Jurisdictions 

Definition Reference Study Background 

Vehicle enters the intersection after the 

signal light had been red for more than 

0.2 second and the measured speed of 

vehicle is ≥ 18 mph for roads with 

speed limits of 45 mph or less and 20 

mph threshold for roadways with 

higher posted speed limits. 

Ruby and 

Hobeika, 2003 

This study assesses the Red Light Running camera 

enforcement program in Fairfax County, Virginia, 

where 10 cameras were installed at high commuter 

traffic intersections. 

Lum and Wong, 

2003 

This is a before-and-after study that evaluated the 

impacts of installing and operating red light 

cameras at two “T” and one “X” signalized 

intersections. 

Schattler et al., 

2002 

Video cameras collected data from 3 intersections 

in Oakland County, Michigan, for a before-andafter evaluation of the impacts of clearance 

intervals on red light violation rates. 

Vehicle moves across the stop bar after 

the signal phase changed from yellow 

to red. 

Kamyab et al., 

2002; Kamyab et 

al., December 

2000 

The Center for Transportation Research and 

Education at Iowa State University collected red 

light running data at 12 intersections from 7 Iowa 

communities. 

Retting et al., 

1999a 

Baseline red light violation data were collected at 

9 sites equipped with red light violation cameras 

in a red light camera enforcement study conducted 

in Oxnard, California. 

Vehicle enters the intersection after the 

signal light had been red for a 

minimum elapsed time of 0.4 second 

and the measured speed of vehicle is at 

least 15 mph. Retting et al., 

1999b 

Baseline red light violation data were collected at 

5 sites in Fairfax, Virginia, which have histories of 

crashes involving red light running. 

Vehicle enters the intersection any 

time after onset of the red signal and is 

traveling at least 15 mph. 

Retting et al., 

1998 

Red light cameras collected data at two busy 

intersections in Arlington, Virginia. 

Vehicle enters the intersection ≥ 0.5 

second after the onset of a red traffic 

signal. 

Retting and 

Williams, 1996 

An automated camera and trained observers 

collected red light running data at an intersection 

with an eight-lane east/west principal arterial and a 

four-lane north/south collector street in Arlington 

County, Virginia. 
5

2.2. Effects of Driver, Intersection, and Environment on Red Light Violations 

Table 2 presents various variables that showed some effects on drivers’ red light violation 

behavior. Table 3 lists red light violation rates reported in previous studies. 

Table 2. Effects of Driver, Intersection, and Environment on Red Light Violations

Element Variable Key Finding Reference 

The older age groups accounted for a relatively small 

portion of red light running crashes compared to the 

young age group. 

Kraus and Quiroga, 2004 

Younger drivers between the ages of 18 to 25 years old 

are more likely to run red lights compared to other age 

groups. 

Porter and Berry, 2001 Age 

Red light runners tend to be drivers under 30 years old. Retting et al., 1999; Retting 

and Williams, 1996 

Gender Red light runners are more likely than non-runners to be 

male. Retting et al., 1999 

Occupancy 

Drivers have a higher probability of running red lights 

when driving alone compared to when passengers are in 

their vehicles. 

Porter and Berry, 2001 

Safety Belt Red light runners are less likely to wear safety belts. Porter and England, 2000; 

Retting and Williams, 1996 

Red light runners are more likely than non-runners to be 

driving with suspended or revoked driver’s licenses. Retting et al., 1999 

Driver 

Driving 

Record Drivers with poor driving records and driving smaller 

and older cars have a higher tendency to run red lights. Retting and Williams, 1996 

The frequency of red light running increases when the 

yellow interval is less than 3.5 seconds. Brewer et al., 2002 

Signal 

Timing Longer yellow intervals will cause drivers to enter 

intersection later and lengthening the all-red intervals 

caters to red light violators. 

Eccles and McGee, July 

2001 

Stopping 

Distance 

Probability of a vehicle stopping for a traffic light 

decreases as its distance from the intersection decreases. Chang et al., 1985 

Approach 

Speed 

Probability of a driver stopping for a traffic light 

decreases as the approach speed to the intersection 

increases. 

Chang et al., 1985 

Grade 

Probability of a driver stopping for a traffic light 

increases as the grade of the approaching intersection 

increases (i.e., roadway becomes “steeper”). 

Chang et al., 1985 

Intersection 

Intersection 

Width 

Drivers tend to stop for traffic lights more at wider 

intersections than at narrower intersections. Chang et al., 1985 
6

Table 2. Effects of Driver, Intersection, and Environment on Red Light Violations (Cont.) 

Element Variable Key Finding Reference 

Higher red light running rates were observed in cities 

with wider intersections and higher traffic volumes. Porter and England, 2000 Approach 

Volume The red light running frequency increases as the 

approach traffic volume at intersections increases. Brewer et al., 2002 

Higher red light violations occur during the time period 

of 3:00 PM to 5:00 PM. 

Kamyab, et al., 2002; 

Kamyab, et al., December 

Time of 2000 

Day The average red light violations are higher during AM 

and PM peak hours compared to other times of the day. Retting et al., 1998 

Day of 

Week 

There are more red light violations on weekdays 

compared to weekends. 

Lum and Wong, 2003; 

Kamyab, et al., 2002; 

Kamyab, et al., December 

2000; Retting et al., 1998 

Traffic & 

Environment 

Weather The influence of rainfall on red light running behavior is 

insignificant. Retting et al., 1998 

Major observations made from the information presented in Table 2 and Table 3 include: 

− According to Table 2, factors ranging from age and gender of the driver, signal timing, 

and approach speed of the intersection, to time of day could all affect red light running 

behavior. Factors with strong influence on red light violation could be region-specific or 

location-specific. 

− A wide range of violation rates has been reported by different studies. Factors such as 

definitions of red light violation used in studies, methods used for data collection, and 

locations of studies could all contribute to variations in violation rates. Information 

presented in Table 3 confirmed that red light violation is a common problem in many 

communities; however, the effects of red light violation from one location to the next can 

vary considerably. 

− Studies that examined the before-and-after effect of implementing red light photo 

enforcement cameras suggest that such systems have a great potential to effectively 

reduce red light violations. However, some researchers pointed out that the full 

consequences of red light cameras are still being studied. For example, McGee (2002) 

indicated that there is still no conclusive answer yet about the effect of red light photo 

enforcement cameras on crash rates at signalized intersections due to the lack of 

comprehensive and statistically rigorous study designs. 
7

Table 3. Red Light Violation Rates Reported by Previous Studies 

Reference Rate Before Project Implementation Rate After Project Implementation 

Lum and Wong, 2003 

Average weekday red light violations ranging 

from 16.0 to 111.8 per day at two “T” 

intersections before implementation of red light 

cameras. 

Weekday red light violations reduced to 

13.4 to 58.6 per day at two “T” 

intersections after installation of red light 

cameras. 

Rudy and Hobeika, 

2003 

10 intersections with various red light violation 

rates ranging from 2.00 violations to 11.0 

violations per 10,000 vehicles. 

3 months after installation of red light 

running cameras, violation rates at these 

intersections were reduced to between 0.17 

violation and 7.0 violations per 10,000 

vehicles. 

Brewer et al., 2002 An overall average of 4.1 red light runners per 

1,000 vehicles. N/A 

Schattler et al., 2002 

3 intersections with various rates ranging from 0 

violations per hour to an average of 10.2 

violations per hour. 

Violation rates ranging from 0 violations 

per hour to an average of 4.6 violations per 

hour after the implementation of new 

clearance intervals per ITE guidelines. 

Fakhry and Salaita, 

2002 

An average of 1.3 red light violations per 1,000 

vehicles (manual observation). N/A 

Kamyab, et al., 2002; 

Kamyab, et al., 

December 2000 

13 intersections with various violation rates 

ranging from 0.45 violation per 1,000 entering 

vehicles to 38.50 violations per 1,000 vehicles. 

N/A 

Porter and England, 

2000 

At least one red light runner in 1,798 out of the 

5,112 observed light cycles (35.2%), roughly 10 

red light violations per observed hour. 

N/A 

Retting et al., 1999a 

12.9 violations per 10,000 vehicles at red light 

camera sites and 16.0 violations per 10,000 

vehicles at non-camera sites. 

7.7 violations per 10,000 vehicles at red 

light camera sites and 8.0 violations per 

10,000 vehicles at non-camera sites 4 

months after the implementation of red 

light cameras. 

Retting et al., 1999b 

36.3 violations per 10,000 vehicles at red light 

camera sites and 37.8 violations per 10,000 

vehicles at non-camera sites. 

20.4 violations per 10,000 vehicles at red 

light camera sites and 25.0 violations per 

10,000 vehicles at non-camera sites 1 year 

after the installation of red light cameras. 

Retting et al., 1998 

6,171 violations observed during 1,176 hours on 

Site 1 (5.2 runners/hour) and 1,950 violations 

observed during 1,518 hours on Site 2 (1.3 

runners/hour). 

N/A 

Retting and Williams, 

1996 

462 red light violations out of 1,373 observations 

made over 234 hours of data collection, about 2 

red light runners per hour of data collection. 

N/A 

ITE ≡ Institute of Transportation Engineers 
8

2.3. Red Light Violation Countermeasures 

Table 4 lists countermeasures suggested by researchers to address red light violations. As 

alternatives to photo enforcement cameras, some countermeasures focus on improving the traffic 

control system at intersections and the physical layout of the intersections. In certain situations, 

the use of “non-enforcement” countermeasures would be sufficient to effectively reduce red light 

violations at intersections. 

Table 4. Suggested Red Light Violation Countermeasures

Countermeasure Reference 

Use automated red light photo 

enforcement cameras. 

Rudy and Hobeika, 2003; Retting and Kyrychenko, 2002; BMI, 

December 2001; Retting and Kyrychenko, April 2001; Retting et al., 

1999; Retting et al., 1998; Retting and Williams, 1996 

Adjust the timing of traffic signals with 

adequate yellow clearance interval time/in 

accordance with recommendations made 

by ITE. 

Brewer et al., 2002; Milazzo et al., 2002; Schattler et al., 2002; BMI, 

December 2001; Milazzo et al., June 2001; Retting et al., September 

2000; Retting et al., 1999; Retting et al., 1998; Retting and Greene, 

1997; Retting and Williams, 1996; Retting et al., 1995; Zador et al., 

1985 

Increase signal and sign visibility. Brewer et al., 2002; BMI, December 2001; Retting et al., 1995 

Improve signal coordination among 

intersection groups. Brewer et al., 2002 

Use protected left-turn phases. BMI, December 2001 

Provide all-red intervals at intersections. Retting et al., 1995 

Increase sight distance. Retting et al., 1995 

ITE ≡ Institute of Transportation Engineers 

This section of the report provides a summary of literature on topics related to red light violation. 

This information forms the “baseline” knowledge in understanding red light violation definition 

and the effects of driver, intersection, and environment on red light violation. Sections 3 and 4 

of this report will present and discuss results from the analysis of red light violation data 

gathered from the City of Sacramento’s 11 RLPEC-equipped intersections. 
9

3. STATISTICAL DESCRIPTION OF RED LIGHT VIOLATIONS 

This section statistically describes the red light violation data obtained from the City of 

Sacramento’s Red Light Running Program. It is important to note that the violation records 

given to the Volpe Center contained no personal information of red light offenders. To protect 

the privacy of red light offenders, information such as offenders’ names, mailing addresses, and 

vehicle license plate numbers were all removed from the data set before its transfer to the Volpe 

Center. 

3.1. Overview of Sacramento's Red Light Running Program 

The Sacramento Police Department reported about 5,500 vehicle collisions at intersections in the 

City of Sacramento in 1998. Approximately 13 percent of these collisions were caused by 

running the red light, resulting in 494 injuries and a financial cost of more than $15 million to the 

local economy.1

 Upon reviewing these statistics, Sacramento’s City Council decided to 

implement a new program that aims to enhance safety and modify driver behavior at signalized 

intersections. 

After going through an evaluation process, the City of Sacramento decided to deploy RLPECs at 

selected signalized intersections to capture vehicles/drivers deliberately running the red light. 

On December 15, 1998, Sacramento’s City Council awarded a service contract to a private 

company to install cameras at 11 signalized intersections throughout the city. The selection of 

these intersections was based on three criteria: 

− Number of intersection collisions caused by red light violations 

− Total intersection traffic volumes 

− Inputs from the police department and the community 

RLPECs were installed at one or more approaches (or directions) of the following 11 

intersections in the City of Sacramento: 

1. Fair Oaks Boulevard and Howe Avenue 

2. El Camino Avenue and Evergreen Street 

3. Arden Way and Exposition Boulevard 

4. Mack Road and La Mancha Way/Valley Hi Drive 

5. Mack Road and Center Parkway 

6. 30th Street and Capitol Avenue 

7. J Street and Alhambra Boulevard 

8. Broadway and 21st Street 

9. W Street and 16th Street 

10. Howe Avenue and College Town Drive 

11. Power Inn Road and Folsom Boulevard 

 

1

 Data presented above can be found at 

http://www.cityofsacramento.org/transportation/engineering/trafficredlight.html
10

The intersection of Fair Oaks Boulevard and Howe Avenue was identified by a 2001 study as 

one of the 10 most dangerous intersections in the United States (American City & County, 

2001). Pictures for the 11 intersections listed above are provided in Appendix A. 

The City of Sacramento began to issue red light violation warning notices to offenders for a 

period of 30 days, as required by the California Vehicle Code, on May 26, 1999. On June 26, 

1999, the City began to issue red light violation citations to offenders. Sacramento’s Red Light 

Running Program is still ongoing. Appendix B presents pictures of a RLPEC and a warning sign 

notifying drivers that they are approaching an intersection equipped with enforcement cameras. 

3.2. Picture Processing Procedures 

This subsection describes the procedures by the Sacramento Police Department to process 

RLPEC pictures on potential red light offenders. As indicated earlier, RLPECs at the City of 

Sacramento’s signalized intersections are activated to photograph potential violators when the 

following conditions are met: 

− Vehicle enters the intersection after the signal light had been red for a minimum elapsed 

time of 0.2 second, and 

− The measured speed of violating vehicle is more than 15 mph (on straight-thru lane) or 

13 mph (on left-turn lane). 

Several pictures are taken to capture the vehicle crossing the stop bar after the red signal, license 

plate number of the vehicle, and the person who is driving the violating vehicle. The following 

steps are carried sequentially to process RLPEC pictures of a potential red light violator: 

− Field service technicians from Affiliated Computer Services (ACS) (the company that 

maintains the photo enforcement cameras and processes all the red light violation data for 

the City of Sacramento) pick up images and data from the cameras. 

− Pictures taken by the cameras are developed, digitized, and saved in a secure database, 

along with other information gathered by the cameras (e.g., date and time of the red light 

violation, vehicle speed of the violator, and elapsed time of the red signal). 

− California Department of Motor Vehicles furnishes information on the registered owner 

of the violating vehicle, using license plate number captured by the photo enforcement 

cameras. 

− Staff members at the Sacramento Police Department review pictures taken by the 

cameras and determine whether to issue violation citations. A violation citation will be 

issued only if evidence presented by the pictures indicates a definite red light violation. 

− If a red light violation citation is issued, a copy of the citation is mailed to the violator. In 

addition, violation photos and other related data are uploaded onto a secure (i.e., 

password-protected) Web site to be viewed by the violator. After information viewing is 

completed, the City of Sacramento collects a red light violation fine from the violator. 

Procedures described above reveal that not all pictures taken by the RLPECs lead to violation 

citations. No violation citation is issued if, for instance, pictures taken by the photo enforcement 

cameras do not clearly show that the vehicle crossed the stop bar after the onset of red signal, or 
11

the registered owner of the vehicle does not match the driver who ran the red light. Based on one 

estimate, only 35 percent of the pictures taken by Sacramento’s cameras result in violation 

citations. Red light violation records used for this analysis only include the actual violation 

citations issued by the Sacramento Police Department. Consequently, red light violation rates 

estimated from Sacramento’s data are likely to be lower than most violation rates reported by 

other studies (see Section 2). 

3.3. Descriptive Statistics 

This subsection presents all background statistics related to the Sacramento red light violation 

data used in this study. Pertinent information regarding the 11 signalized intersections equipped 

with RLPECs is also provided below. 

3.3.1. General Information on Red Light Violation Records 

With approval from the Sacramento Police Department, ACS prepared and provided to the Volpe 

Center data on more than 4 years (May 1999 to June 2003) of red light violation records 

collected from 11 RLPEC-equipped intersections. Figure 1 displays the relative locations of 

these 11 intersections in the City of Sacramento. Data from a total of 46,997 red light violation 

records were provided to the Volpe Center. Table 5 provides a distribution of these records and 

their collection periods (first and last red light violation record dates) by intersection location 

since not all 11 signalized intersections have a complete 4-year data set. 

The following variables are included in Sacramento’s red light violation data file: 

− Intersection (and intersection code) where the violation occurred 

− Date when the violation occurred 

− Time when the violation occurred 

− Age of the violator 

− Gender of the violator 

− Car (i.e., vehicle make) driven by the violator 

− Model year of the vehicle driven by the violator 

− Vehicle speed (i.e., measured speed) at the time of the violation 

− Elapsed time from the onset of red signal until the time of the violation 

All records in this data set have information on location of the violation, date of the violation, 

and time of the violation. However, information related to age and gender of the violator, type of 

vehicle driven by the violator, and year of the vehicle is missing from some violation records. 

Few records have no information on vehicle speed at the time of the violation.
12

Figure 1. Location of Sacramento’s 11 RLPEC-Equipped Intersections 
13

Table 5. Distribution of Red Light Violation Records and their Collection Period

Signalized Intersection No. of Red Light 

Violation Records

Date of the First 

Violation Record 

Date of the Last 

Violation Record

Fair Oaks Boulevard and Howe Avenue 11,134 6/10/1999 6/21/2003 

El Camino Avenue and Evergreen Street 6,167 6/4/1999 6/21/2003 

Arden Way and Exposition Boulevard 1,820 4/25/2000 9/8/2002 

Mack Road and La Mancha Way/Valley Hi Drive 2,408 5/26/1999 6/20/2003 

Mack Road and Center Parkway 1,882 12/17/1999 6/20/2003 

30th Street and Capitol Avenue 762 2/26/2000 7/12/2002 

J Street and Alhambra Boulevard 5,475 2/29/2000 6/23/2003 

Broadway and 21st Street 6,118 3/13/2000 12/4/2002 

W Street and 16th Street [Highway 50 Exit at 16th Street] 5,592 6/30/2000 6/20/2003 

Howe Avenue and College Town Drive 5,349 7/13/2000 8/9/2002 

Power Inn Road and Folsom Boulevard 290 2/28/2002 3/28/2002 

SUM 46,997 *** *** 

3.3.2. Information on RLPEC-Equipped Signalized Intersections 

The Traffic Engineering Services at Sacramento Department of Public Works provided the 

Volpe Center with information related to traffic and infrastructure characteristics of the 11 

RLPEC-equipped intersections. Table 6 quantifies these intersection characteristics. 

Various resources were used to gather the traffic and infrastructure information presented in 

Table 6: 

− Data on “Posted Speed Limit,” “Yellow Time Duration,” and “All Red Phase” were 

provided by ACS per direction from Sacramento’s Traffic Engineering Services. 

− Traffic volumes for the 11 intersections, based on the 24 hour average daily traffic (ADT) 

counts, were obtained from Sacramento Department of Public Works’ website. Traffic 

counts for these intersections were carried out at various times, ranging from within a 

year to several years ago. 3 of the 11 intersections do not have complete count data for 

all approaches. Sacramento’s traffic counts database can be accessed at 

www.pwsacramento.com/traffic/trafficcounts/index.cfm. 

− Using the Geographic Information System (GIS) maps provided by Sacramento’s Traffic 

Engineering Services, Volpe Center staff extracted the following information regarding 

these 11 intersections: “Average Intersection Gap,” “Total Number of Lanes,” and 
14

“Average Lane Width.” An example of the GIS map, for the intersection of Fair Oaks 

Boulevard and Howe Avenue, is shown in Appendix C with measurements of the 

intersection gap and lane width. 

3.3.3. Frequency Distributions of Selected Variables 

Frequency counts of the following variables are presented in this subsection: 

− Age of the violator 

− Gender of the violator 

− Time (in hours) when the violation occurred 

− Model Year of the vehicle driven by the violator 

− Measured vehicle speed at the time of the violation 

− Elapsed time from the onset of red signal until the time of the violation 

In addition, the distribution of repeat red light offenders (i.e., drivers with more than 1 violation 

record) is also presented. 

Age of the Violator: Distribution of red light violators, categorized by 7 age groups, is presented 

in Table 7. In addition, Table 7 includes data on the number of licensed drivers (LDs) in 

California (Federal Highway Administration, 2002), the total million vehicle miles traveled 

(MVMT) (Cerrelli, 1998), and relative ratios of red light violation (RLV) percentages by 

licensed driver percentages and total MVMT percentages. Relative ratios for % of LD

% of RLV and 

% of MVMT

% of RLV were plotted and presented in Figure 2. Both plotted lines in Figure 2 suggest that 

younger drivers under 30 years of age are more likely to run red lights compared to drivers in 

other age groups. The effect of age on red light violation behavior will be explored further in 

Section 4 of this report. 

Gender of the Violator: Table 8 presents the distribution of red light violators by gender. The 

number of male and female licensed drivers in California (Federal Highway Administration, 

2002), the total MVMT by male and female drivers (Cerrelli, 1998), and relative ratios of red 

light violation percentages by licensed driver percentages and total MVMT percentages are also 

listed in Table 8. Relative ratios of 

% of LD

% of RLV show that male drivers might have a slightly 

higher tendency to run red lights. In contrast, relative ratios of % of MVMT

% of RLV indicate that 

female drivers might be more likely to run red lights. The influence of gender on red light 

violation behavior will be further examined in Section 4 of this report.
15

Table 6. Traffic and Infrastructure Characteristics of the 11 RLPEC-Equipped Intersection

Total Num

Lanes 

Signalized Intersection Posted Speed Limit (mph)

Yellow 

Time 

Duration 1

(sec) 

All Red 

Phase 

(sec) 

Traffic 

Volume 

(ADT) 2

Average 

Intersection 

Gap (ft) NorthSouth E

W

Fair Oaks Boulevard and Howe Avenue 40 4.7 / 3.6 1.0 / 0.5 85,636 149 10 El Camino Avenue and Evergreen Street 35 3.9 0.0 29,563 84 3 Arden Way and Exposition Boulevard 40 4.7 0.5 83,765 118 8 Mack Road and La Mancha Way/Valley Hi Drive 35 to 40 3.6 0.0 43,765 3 142 9 Mack Road and Center Parkway 40 4.7 1.5 45,483 130 6 30th Street and Capitol Avenue 30 3.6 0.0 26,901 90 3 J Street and Alhambra Boulevard 25 3.6 0.0 38,793 83 4 Broadway and 21st Street 25 3.6 0.0 28,355 87 5 W Street and 16th Street [Highway 50 Exit at 16th Street] 35 [30 to 65] 3.9 0.0 46,473 3 92 5 Howe Avenue and College Town Drive 45 5.0 2.0 57,496 3 111 8 Power Inn Road and Folsom Boulevard 45 3.6 1.0 60,087 126 8 

1 Based on average value of recent yellow time durations that vary between peak and non-peak traffic hours. 2 ADT = Average Daily Traffic, 24-Hour Counts. 3 Estimated value based on ADT data from surrounding intersections.
16

Table 7. Distribution of Red Light Violation Records by Age Group

Age Group No. of 

RLVs 

% of 

RLVs No. of LDs 1 % of LDs % of RLVs/ % of LDs 

Total 

MVMT 2 % of MVMT % of RLVs/ % of MVMT

< or = to 19 1,668 4.27% 883,858 4.09% 1.05 83,169 3.96% 1.08 

20 to 29 9,769 25.02% 3,925,985 18.16% 1.38 412,282 19.65% 1.27 

30 to 39 9,448 24.20% 4,997,068 23.11% 1.05 539,014 25.68% 0.94 

40 to 49 8,390 21.49% 4,797,117 22.18% 0.97 503,354 23.99% 0.90 

50 to 59 5,381 13.78% 3,401,805 15.73% 0.88 288,915 13.77% 1.00 

60 to 69 2,410 6.17% 1,883,240 8.71% 0.71 170,488 8.12% 0.76 

> or = 70 1,979 5.07% 1,734,720 8.02% 0.63 101,386 4.83% 1.05 

Sub-Total 39,045 *** *** *** *** *** *** *** 

Missing Data 7,952 *** *** *** *** *** *** *** 

Total 46,997 100.00% 21,623,793 100.00% *** 2,098,608 100.00% *** 1

 Number of licensed drivers in California, 2001.

2

 Total vehicle miles of travel in the U.S., in millions, 1996.

0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

1.40

< or = to 19 20 to 29 30 to 39 40 to 49 50 to 59 60 to 69 > or = 70

Age Group (Year)

Relative Ratio

% of RLV / % of LD

% of RLV / % of MVMT

Figure 2. Normalized Red Light Violation Values by Age Group
17

Table 8. Distribution of Red Light Violation Records by Gender

Gender No. of 

RLVs 

% of 

RLVs No. of LDs 1 % of LDs % of RLVs/ % of LDs 

Total 

MVMT 2

% of 

MVMT 

% of RLVs/ 

% of MVMT

Male 24,798 55.51% 11,208,831 51.84% 1.07 1,317,941 62.80% 0.88 

Female 19,876 44.49% 10,414,962 48.16% 0.92 780,667 37.20% 1.20 

SubTotal 44,674 *** *** *** *** *** *** *** 

Missing 

Data 2,323 *** *** *** *** *** *** *** 

Total 46,997 100.00% 21,623,793 100.00% *** 2,098,608 100.00% *** 1

 Number of licensed drivers in California, 2001.

2

 Total vehicle miles of travel in the U.S., in millions, 1996.

Violation Time: Figure 3 illustrates the distribution of Sacramento’s red light violations by 

violation time (in hours). The overall trend shown in this figure is consistent with the 

expectation – most of red light violations occurred during the daytime hours when most urban 

driving is done (i.e., 7 a.m. to 7 p.m.). However, the highest count of red light violations during 

the time period from 2:00 p.m. to 2:59 p.m. is somewhat surprising. The relationship between 

time of day and red light violations will be studied further in the next section. 

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

Number of Red Light Violations

12:00

AM

1:00

AM

2:00

AM

3:00

AM

4:00

AM

5:00

AM

6:00

AM

7:00

AM

8:00

AM

9:00

AM

10:00

AM

11:00

AM

12:00

PM

1:00

PM

2:00

PM

3:00

PM

4:00

PM

5:00

PM

6:00

PM

7:00

PM

8:00

PM

9:00

PM

10:00

PM

11:00

PM

Time of the Day

Figure 3. Distribution of Red Light Violations by Time of Day
18

Model Year of Violating Vehicle: About 99.1 percent of the 46,997 red light violation records 

from the City of Sacramento had information on model year of the violating vehicle. Figure 4 

presents the distribution of red light violations per vehicle year. 

0

5,000

10,000

15,000

20,000

25,000

30,000

Number of Red Light Violations

< or = 1969 1970 to 1979 1980 to 1989 1990 to 1999 2000 to Present

Vehicle Age

Figure 4. Distribution of Red Light Violation Records by Vehicle Year 

Vehicle Speed at Time of Violation: A frequency distribution of the measured vehicle speeds at 

the time of red light violation is shown in Figure 5. A total of 46,993 out of the 46,997 records 

had data on measured vehicle speed at violation, ranging from 11 mph (2 records) to 87 mph (1 

record). The three most frequent vehicle speeds at the time of violation were: 18 mph (1,762 or 

3.7% of records), 17 mph (1,715 or 3.6% of records), and 19 mph (1,711 or 3.6% of records). 

The average red light violation speed was 31.6 mph. More than half (51.1%) of the drivers ran 

the red light at speeds of 30 mph or less. The highest posted speed limit among the 11 RLPECequipped intersections in Sacramento is 45 mph, and only 13.7 percent of the violators ran the 

red light at speeds higher than that limit. Figure 6 shows the percentage cumulative distribution 

of violations records by vehicle speed. 

To examine speeding by the violating vehicles, data from violation records were grouped by 

individual intersection and posted speed limit (PSL). Speeds of violating vehicles were 

subtracted from corresponding PSLs at each intersection. Table 9 presents the distribution of red 

light violation records by “violation speed ≤ PSL” (i.e., not speeding) and “violation speed > 

PSL” (i.e., speeding) for the 11 RLPEC-equipped intersections. In addition, Figure 7 and Figure 

8 illustrate respectively the percentage distribution and percentage cumulative distribution of red 

light violation records by vehicle speed minus the PSL (i.e., speeding measure). Appendix D 
19

presents these figures for individual intersections. Overall, about 56 percent of the violating 

vehicles were not speeding through the intersections at the time of the violation. 

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90

Vehicle Speed at the Tme of Violation (mph)

Number of Red Light Violation Records

Figure 5. Distribution of Red Light Violation Records by Vehicle Speed 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

15 20 25 30 35 40 45 50 55 60 65 > 65

Vehicle Speed at Time of Violation (mph)

% of Violations

Figure 6. Percentage Cumulative Distribution of Red Light Violation Records by Vehicle Speed 
20

Table 9. Distribution of Violation Records by Vehicle Speed in Comparison to the Posted Speed 

Limit 

Violation Speed ≤ PSL Violation Speed > PSL 

Signalized Intersection PSL (mph)

Count % of Total Count % of Total 

Total Red 

Light 

Violations 

Fair Oaks Boulevard and Howe Avenue 40 10,535 94.6% 598 5.4% 11,133 

El Camino Avenue and Evergreen 

Street 35 1,622 26.3% 4,545 73.7% 6,167 

Arden Way and Exposition Boulevard 40 1,262 69.3% 558 30.7% 1,820 

Mack Road and La Mancha 

Way/Valley Hi Drive 35 1,367 56.8% 1,039 43.2% 2,406 

Mack Road and Center Parkway 40 526 27.9% 1,356 72.1% 1,882 

30th Street and Capitol Avenue 30 396 52.0% 366 48.0% 762 

J Street and Alhambra Boulevard 25 2,263 41.3% 3,212 58.7% 5,475 

Broadway and 21st Street 25 1,193 19.5% 4,925 80.5% 6,118 

W Street and 16th Street [Highway 50 

Exit at 16th Street] 35 4,460 79.8% 1,131 20.2% 5,591 

Howe Avenue and College Town Drive 45 2,458 46.0% 2,891 54.0% 5,349 

Power Inn Road and Folsom Boulevard 45 290 100.0% 0 0.0% 290 

Total *** 26,372 56.1% 20,621 43.9% 46,993 

Examining the information presented in Table 9 and Figure 26 through Figure 36 (see Appendix 

D), the following is noted: 

− Percentage distribution of “violation speed ≤ PSL” versus “violation speed > PSL” for 

the intersection of Mack Road and La Mancha Way/Valley Hi Drive is similar to the 

“overall” distribution for all 11 intersections. 

− With limited amount of data, vehicle speeds for all red light runners at the intersection of 

Power Inn Road and Folsom Boulevard are less than or equal to the PSL. 

− Intersections of Fair Oaks Boulevard and Howe Avenue, Arden Way and Exposition 

Boulevard, and W Street and 16th Street all have relatively low percentages of vehicles 

that ran the red light at speeds higher than the PSLs compared to the remaining 

intersections. According to Table 6, traffic volumes at these three intersections are all 

fairly high. 

Additional analysis results regarding vehicle speed at the time of violation will be presented in 

Section 4. 
21

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

-25 -20 -15 -10 -5 0 5 10 15 20 25 > 25

[Vehicle Speed - PSL] (mph)

% of Violations

Figure 7. Percentage Distribution of Violation Records by [Vehicle Speed – PSL] 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

-25 -20 -15 -10 -5 0 5 10 15 20 25 > 25

[Vehicle Speed - PSL] (mph)

% of Violations

Figure 8. Percentage Cumulative Distribution of Records by [Vehicle Speed – PSL] 

Elapsed Time from Onset of Red Signal until Time of Violation: Figure 9 presents a frequency 

distribution plot (on the logarithmic scale) of “elapsed time since red light onset” for all violation 

records. Information on red light elapsed time is available for every record in this Sacramento 

data set. This elapsed time is recorded to the nearest 0.1 second. RLPECs have captured drivers 

crossing intersections after the red light was initiated, from 0.2 second (minimum elapsed time to 

activate the enforcement camera, 6,381 records) to more than 30 seconds (434 records). Records 

with elapsed time greater than 30 seconds were not plotted in Figure 9. Figure 10 and Figure 11 

illustrate respectively the percentage distribution and percentage cumulative distribution of red 

light violation records by elapsed time since red light onset. 
22

0.1

1.0

10.0

100.0

1,000.0

10,000.0

0.0 5.0 10.0 15.0 20.0 25.0 30.0

Time Elapsed After the Red Light Signal (sec)

Number of Red Light Violation Records

Figure 9. Distribution of Violation Records by Elapsed Time Since Red Light Onset (Vertical 

Axis in Logarithmic Scale) 

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

55%

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 > 5

Time Elapsed since Red Light Onset (sec)

% of Violations

Figure 10. Percentage Distribution of Violation Records by Time Elapsed Since Red Light 

Onset 

Lum and Wong (2003) separated red light violators into three groups according the “after-red 

time” (i.e., elapsed time when a driver goes through the intersection after the onset of red light): 

up to 2 seconds of after-red times, 2 to 5 seconds of after-red times, and more than 5 seconds of 
23

after-red times. Using the same categories, Table 10 lists the corresponding number of violation 

records from Sacramento’s data set. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 > 5

Time Elapsed since Red Light Onset (sec)

% of Violations

Figure 11. Percentage Cumulative Distribution of Violations by Time Elapsed Since Red Light 

Onset 

Table 10. Distribution of Red Light Violation Records by Selected Categories of Time Elapsed 

Since Red Light Onset

After-Red Time No. of Violation 

Records 

Percent of 

Distribution 

Cumulative Violation 

Records Cumulative Percent 

≤ 2.0 seconds 44,294 94.2% 44,294 94.2% 

2.1 to 5.0 seconds 1,270 2.7% 45,564 97.0% 

> 5.0 seconds 1,433 3.0% 46,997 100.0% 

Information presented in Table 10 shows that more than 94 percent of red light violations in the 

City of Sacramento occurred within 2 seconds after the onset of the red light. The influence of 

the “dilemma zone” is probably one of the major reasons for such a high percentage of red light 

violators in this first group. 

Vehicle Speed versus Time Elapsed since Red Light Onset: Table 11 provides values for the 

percentage cumulative distribution of violation records by time elapsed since red light onset for 

various vehicle speeds at time of violation. Over 95 percent of the violators who were traveling 

at speeds between 35 and 65 mph ran the red light within 2 seconds of red light onset. However, 

only 85 percent of the violators at speeds over 65 mph ran the red light within 2 seconds. 

Moreover, about 12 percent of the violators at speeds over 65 mph ran the red light after 5 

seconds of its onset. About 95 percent of the violators traveling at speeds below 35 mph ran the 

red light within 2.5 seconds of its onset. 

Vehicle Speed Minus Speed Limit versus Time Elapsed since Red Light Onset: Table 12 provides 

values for the percentage cumulative distribution of violation records by time elapsed since red 
24

light onset for various delta speeds (vehicle speed minus PSL) at time of violation. Over 95 

percent of the violators traveling between 5 and 25 mph over the speed limit ran the red light 

within 2 seconds of its onset. Similar to the observation in Table 11, about 12 percent of the 

violators who were traveling higher than 25 mph over the speed limit ran the red light after 5 

seconds of its onset. As seen in Table 11 and Table 12, there is no trend between “speeding” and 

time elapsed since red light onset except at very high speeds. The reader is cautioned, however, 

to the small sample of violation records at these very high speeds (i.e., vehicle speed > 65 mph 

and delta speed > 25 mph). 

Table 11. Percentage Cumulative Distribution of Violations by Elapsed Time since Red Light 

Onset for Various Vehicle Speeds 

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 > 5

15 37.6% 67.7% 85.5% 93.6% 96.9% 97.8% 98.1% 98.1% 98.4% 98.5% 100.0% 1,334 

20 44.8% 75.2% 87.8% 92.5% 94.7% 95.7% 96.1% 96.3% 96.5% 96.7% 100.0% 8,227 

25 51.4% 81.4% 91.3% 94.2% 95.4% 95.9% 96.3% 96.6% 96.8% 96.9% 100.0% 7,763 

30 55.7% 83.6% 92.0% 94.1% 94.9% 95.4% 95.7% 95.9% 96.1% 96.2% 100.0% 6,702 

35 56.9% 84.1% 92.4% 94.8% 95.7% 96.2% 96.5% 96.8% 96.9% 97.1% 100.0% 5,851 

40 56.0% 83.7% 92.3% 95.0% 96.0% 96.4% 96.6% 96.8% 97.0% 97.3% 100.0% 5,542 

45 54.1% 82.9% 92.0% 95.0% 96.2% 96.7% 96.8% 97.1% 97.3% 97.3% 100.0% 5,123 

50 54.1% 83.3% 92.6% 95.5% 96.5% 96.8% 97.0% 97.2% 97.4% 97.4% 100.0% 3,546 

55 53.1% 82.4% 92.7% 95.2% 96.3% 96.5% 96.7% 96.8% 96.8% 97.0% 100.0% 1,902 

60 54.6% 82.2% 92.5% 93.7% 94.7% 95.0% 95.5% 95.8% 95.9% 96.1% 100.0% 663 

65 48.7% 83.6% 93.5% 95.7% 97.0% 97.8% 97.8% 98.3% 98.3% 98.3% 100.0% 232 

> 65 36.1% 68.5% 78.7% 85.2% 88.0% 88.0% 88.0% 88.0% 88.0% 88.0% 100.0% 108 

Vehicle 

Speed 

(mph)

Time Elapsed since Red Light Onset (sec) No. 

Records 

by Speed

Table 12. Percentage Cumulative Distribution of Violations by Elapsed Time since Red Light 

Onset for Various Delta Speeds 

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 > 5

-25 38.8% 69.4% 86.5% 94.4% 97.6% 98.5% 98.8% 98.8% 99.0% 99.1% 100.0% 1,430 

-20 42.3% 73.3% 87.9% 93.8% 96.5% 97.4% 97.9% 98.1% 98.2% 98.3% 100.0% 4,774 

-15 47.3% 77.2% 87.4% 91.2% 92.9% 93.9% 94.5% 94.8% 95.0% 95.2% 100.0% 5,528 

-10 49.9% 79.3% 89.8% 92.7% 93.7% 94.3% 94.8% 95.1% 95.4% 95.6% 100.0% 3,933 

-5 50.6% 78.1% 87.7% 90.8% 92.3% 93.2% 93.6% 93.8% 94.2% 94.8% 100.0% 4,306 

0 52.8% 81.6% 90.7% 93.4% 94.7% 95.3% 95.6% 96.0% 96.3% 96.5% 100.0% 6,401 

5 56.9% 84.5% 93.1% 95.5% 96.3% 96.7% 96.8% 97.0% 97.2% 97.2% 100.0% 8,419 

10 58.5% 87.0% 95.1% 97.0% 97.6% 97.9% 97.9% 98.0% 98.0% 98.1% 100.0% 7,102 

15 59.0% 87.4% 95.5% 97.6% 98.1% 98.3% 98.4% 98.4% 98.5% 98.5% 100.0% 3,480 

20 54.7% 85.1% 95.5% 97.9% 98.7% 98.8% 99.1% 99.1% 99.1% 99.1% 100.0% 1,191 

25 51.5% 82.4% 93.8% 96.7% 98.0% 98.0% 98.0% 98.0% 98.0% 98.0% 100.0% 307 

> 25 32.5% 66.7% 77.5% 83.3% 87.5% 87.5% 87.5% 88.3% 88.3% 88.3% 100.0% 120 

Delta 

Speed 

(mph)

Time Elapsed since Red Light Onset (sec) No. Records 

by Delta 

Speed

Delta Speed= Vehicle speed – speed limit 
25

Repeat Red Light Offenders: Table 13 shows the distribution of repeat red light offenders. ACS 

determined these counts by matching vehicle license plate numbers. To protect the identity of 

red light offenders, vehicle license plate numbers were not part of the data given to the Volpe 

Center. Consequently, no further analysis on repeat offenders could be performed except for the 

information presented in Table 13. Even though there are close to 2,000 repeat red light 

violators, such number is still a relatively small fraction or 4 percent of the entire Sacramento 

data set. 

Table 13. Statistics on Repeat Red Light Offenders

No. of Repeated 

Offenses 

No. of Repeat Offenders 

(via Vehicle Plate 

Matching) 

Repeat Offenders vs. 

Total No. of Violators 

No. of Red Light 

Violation Records 

Repeated Violation 

Records vs. Total 

Violation Records 

2 1,704 3.79% 3,408 7.25% 

3 137 0.30% 411 0.87% 

4 17 0.04% 68 0.14% 

5 2 0.004% 10 0.02% 

7 1 0.002% 7 0.01% 

Total 1,861 4.14% 3,904 8.31% 

Total Number of Red Light Violations from the Database = 46,997 

Number of Violators with 1 Red Light Violation = 43,093 

Total Number of Violators from the Database = 44,954 

Percent of Violators with 1 Red Light Violation = 95.86% 

3.4. Estimates of Red Light Violation and Crash Rates 

Table 14 presents estimates of red light violation rates from the 11 RLPEC-equipped 

intersections in the City of Sacramento. These violation rates were calculated as a function of 

the following factors based on available information gathered for this study: 

− Number of red light violation records: Red light violation records for each intersection 

were tabulated on a monthly basis. Some intersections have incomplete monthly 

violation records, possibly caused by malfunction of the red light camera. Monthly red 

light violation rates were calculated for each intersection. Values listed in Table 14 were 

the average rates from the sum of monthly rates, excluding those with incomplete 

monthly records. 

Traffic volumes at intersections: Monthly traffic volumes at each intersection were 

calculated using the 24 hour ADT counts, obtained from Sacramento Department of 

Public Works’ website. The City of Sacramento conducted traffic counts at these 

intersections during various times; some count data were relatively recent while others 

were collected several years ago. It is assumed that ADTs at these intersections remained 

similar in the past several years. Three intersections do not have complete counts; hence, 
26

approximate ADT values for these intersections were generated based on available traffic 

counts from surrounding intersections. Monthly traffic volumes, from May 1999 to June 

2003, were calculated based on the number of weekdays and weekends in each month. 

Weekend ADTs (Saturday and Sunday) were assumed at 25 percent of the weekday 

ADTs for all intersections. 
27

Table 14. Estimates of Red Light Violation Rates From 11 RLPEC-Equipped Intersections 

Records Used to Estimate Violation Rates

Location 

Code Signalized Intersection No. of RLVs ADT 1 2 Duration of the Records 3 No. of Records

Violation 

Estima

 from Is

Citations 

1,000 Veh

361304 Fair Oaks Boulevard and Howe Avenue 11,134 85,636 04/2000 - 09/2002 9,667 0.15362301 El Camino Avenue and Evergreen Street 6,167 29,563 06/1999 - 08/2002 5,384 0.19363201 Arden Way and Exposition Boulevard 1,820 83,765 01/2000 - 12/2000 1,538 0.06363302 Mack Road and La Mancha Way/Valley Hi Drive 2,408 43,765 06/1999 - 06/2000 2,031 0.14364301 Mack Road and Center Parkway 1,882 45,483 01/2000 - 06/2000 & 01/2001 - 12/2001 1,609 0.08365101 30th Street and Capitol Avenue 762 26,901 03/2000 - 06/2000 & 11/2001 - 02/2002 681 0.13365301 J Street and Alhambra Boulevard 5,475 38,793 03/2000 - 08/2002 4,713 0.16365401 Broadway and 21st Street 6,118 28,355 04/2000 - 08/2002 5,789 0.29366402 W Street and 16th Street [Highway 50 Exit at 16th Street] 5,592 46,473 07/2000 - 08/2002 5,049 0.17367102 Howe Avenue and College Town Drive 5,349 57,496 08/2000 - 06/2002 5,042 0.15367601 Power Inn Road and Folsom Boulevard 290 60,087 03/2002 280 0.19

1 ADT at each intersection is used to estimate the monthly traffic volume. 2 ADT during the weekend (Saturday and Sunday) is assumed to be 25 percent of the ADT during weekdays (Monday to Friday) wh

traffic volume. 

3 Violation rate calculations included only those months where red light violation records were available for the entire month.4 This red light violation rate is calculated based on the data set presented in this report. 5 This red light violation rate is generated assuming only 35 percent of the photos taken by Sacramento’s red light enforcement cameras 

citations (see Subsection 3.2).
28

− Violation rate estimates from issued citations versus violation rate estimates from photos 

taken by the red light cameras: Two sets of red light violation rates were shown in Table 

14. The first group of violation rates was calculated based on the red light running 

records used in this study. The second group of violation rates was approximated based 

on the assessment that only approximately 35 percent of the photos taken by 

Sacramento’s cameras turn into violation citations. 

The red light violation rates in Table 14, as calculated from issued violation citations, range from 

0.064 violation per 1,000 crossing vehicles to 0.294 violation per 1,000 crossing vehicles. In 

comparison to red light violation rates reported by other studies (see Table 3), these estimates of 

red light violation rates are quite low, probably due to the following reasons: 

− The 46,997 red light violation records used to estimate the violation rates were actual 

citations issued by Sacramento Police Department. As discussed in subsection 3.2, 

Sacramento Police Department carefully reviews all photos taken by the enforcement 

cameras and would only issue violation citations when certain criteria were met. As a 

result, approximately 35 percent of the photos taken by Sacramento’s RLPECs result in 

violation citations. Consequently, estimates of red light violation rates from this data set 

are lower and more conservative. 

− Sacramento’s red light violation records analyzed in this study are an accumulation of 

several years of data instead of observations made in few weeks or months. These 

violation records were collected from an ongoing red light violation prevention program 

instead of a temporary/pilot study. Therefore, the Sacramento data set is comprehensive 

and stable compared to data gathered from a short-term study (brief data collection period 

and from few intersections with frequent red light running activities). 

− Some assumptions made in this study might have been too conservative and thus resulted 

in low estimates of red light violation rates. 

No obvious trend has been observed between the posted speed limit, yellow time, all-red time, 

traffic volume, intersection gap, number of lanes, and lane width versus the red light violation 

rates. Red light violation rates (calculated from issued violation citations) for these 11 

intersections can be categorized as follows: 

− Two of the intersections (Arden Way and Exposition Boulevard & Mack Road and 

Center Parkway) have violation rates that are lower than 0.1 violation per 1,000 crossing 

vehicles. These two intersections have the same PSL, similar clearance intervals, and 

similar number of lanes. One intersection has larger ADT but smaller average 

intersection gap. 

− Eight of the intersections have red light running rates ranging from 0.1 violation per 

1,000 crossing vehicles to 0.2 violation per 1,000 crossing vehicles. These intersections 

have various PSLs, clearance intervals, traffic volumes, average intersection gaps, and 

number of lanes. 

− Only one intersection was estimated to have a violation rate greater than 0.2 violation per 

1,000 crossing vehicles (Broadway and 21st Street). This intersection has a PSL of 25 

mph with 3.6 seconds of yellow time and no all-red phase. This can be considered as one 

of the “smaller” intersections from the eleven examined in this report with low ADT, 

average intersection gap, and number of lanes. 
29

Finally, Table 15 summarizes vehicle collision records at the 11 RLPEC-equipped intersections. 

These collision records were checked against the respective red light violation records and 

violation rates but no obvious relationship could be established. Excluding the intersection with 

zero collision due to small sample of violation records, crash rate estimates vary between 1 and 9 

crashes per 1,000 red light violations. Overall, the crash rate is estimated at about 5 crashes per 

1,000 red light violations based on this Sacramento data set. 

Table 15. Collision Rates from Sacramento’s RLPEC-Equipped Intersections 

Records Used to Estimate Violation Rates

Location 

Code Duration of Records 1 No. of Records 

Violation Rate 

Estimates from 

Issued Citations 2

(Per 1,000 

Vehicles) 

No. of 

Collision 

Records 3 4

Collision 

Normalized by 

Red Light 

Violation Records

361304 04/2000 - 09/2002 9,667 0.157 54 0.0056 

362301 06/1999 - 08/2002 5,384 0.195 49 0.0091 

363201 01/2000 - 12/2000 1,538 0.064 5 0.0033 

363302 06/1999 - 06/2000 2,031 0.149 18 0.0089 

364301 01/2000 - 06/2000 & 

01/2001 - 12/2001 1,609 0.082 13 0.0081 

365101 03/2000 - 06/2000 & 

11/2001 - 02/2002 681 0.133 6 0.0088 

365301 03/2000 - 08/2002 4,713 0.169 8 0.0017 

365401 04/2000 - 08/2002 5,789 0.294 15 0.0026 

366402 07/2000 - 08/2002 5,049 0.174 5 0.0010 

367102 08/2000 - 06/2002 5,042 0.159 41 0.0081 

367601 03/2002 280 0.198 0 0.0000 

Total *** 41,783 *** 214 *** 

1

 Violation rates included only those months where red light violation records were available for the entire month.

2

 This red light violation rate is calculated based on the data set presented in this report. 

3

 Sacramento's Traffic Engineering Services provided information from the "Traffic Collision History Report". 4

 Only collisions that were in the "Duration of the Records" were counted. 
30

4. STATISTICAL MODELING OF RED LIGHT VIOLATION DATA 

This section presents results from statistical modeling of Sacramento’s red light violation data. 

Two logistic regression models were developed to examine the influence of age, gender, 

violation time, vehicle year, and violation location on two dependent variables: (1) vehicle speed 

at the time of violation and (2) elapsed time between the onset of red signal and the time of 

violation. 

4.1. Logistic Modeling Method 

Logit models were developed to assess the effect of age, gender, violation time, vehicle year, and 

violation location on two dependent variables: (1) violators’ vehicle speed when they ran the red 

light (i.e., ≤ PSL versus > PSL) and (2) elapsed time since red light onset (i.e., ≤ 2 seconds 

versus > 2 seconds). Since the input values for the two dependent variables (usually denoted as 

yi) are dichotomous, logistic regression analysis is employed to understand the effect of various 

independent (or explanatory, usually denoted as xi) variables on the dependent variables. 

The general expression for the logit equation is: 

( ... )

( ... )

1 1 2 2

1 1 2 2

1 i i k ik

i i k ik

x x x

x x x

i

e

e

p α β β β

α β β β

+ + + +

+ + + +

+ = (1)

where 

pi = Probability of yi equals to 1 (1 = violator’s vehicle speed > PSL or elapsed time since 

red light onset > 2 seconds when violator ran the intersection in this study) 

i = 1, ..., n individuals 

α = Constant term 

β β k ... 1 = Coefficients corresponding to explanatory variables k x ...x 1

k x ...x 1 = Explanatory variables 

Equation (1) can be simplified further by dividing both the numerator and denominator by the 

numerator itself: 

( ... ) 1 1 2 2 1

1

i i k ik i x x x e

p − α + β + β + + β + = (2)

One very useful piece of information that can be easily generated from the logistic regression 

analysis is odds ratios. The odds of an event is the ratio of the expected number of times that an 

event will occur to the expected number of times it will not occur. The relationship between 

probabilities and odds can be expressed as: 

i

i

i

p

p O − = 1 (3)

where 

Oi = Odds of an event 
31

Logistic regression analysis results, including odds ratios, regarding the influence of various 

explanatory variables on violators’ vehicle speed at time of violation and elapsed time since red 

light onset are presented respectively in subsections 4.2 and 4.3 below. Logistic regression 

analysis of the Sacramento red light violation data was performed using the SAS software. 

4.2. Factors with Significant Influence on Violators’ Vehicle Speed 

This subsection presents the results of logistic regression analysis using “vehicle speed at time of 

violation” as the dependent variable. This dependent variable has two categories of alternatives 

(or binary responses): (1) vehicle speed less than or equal to PSL (not speeding) and (2) vehicle 

speed greater than PSL (speeding). 

Table 16 lists the dependent and explanatory variables used in the logistic regression analysis of 

vehicle speed at time of red light violation. Few items are worthy to point out regarding the 

explanatory variables shown in Table 16: 

− After several trials using various formats, the breakdown for the age variable was set as: 

younger drivers between 15 to 29 years old, middle-age drivers between 30 to 59 years 

old, and older drivers at 60 years old or more. 

− Variable codes “Time 1” to “Time 4” are the 4 dummy variables representing the four 

time periods when drivers ran the red light. In the logistic analysis, “Time 4” (from 8 

p.m. to 5 a.m.) was selected as the reference variable during the data regression process 

because this is the “off-peak” period for driving with low traffic volume. In addition, red 

light violations from 8 p.m. to 5 a.m. were considerably lower compared to other hours of 

the day, according to Figure 3. 

− The following logic was used to divide the “VehYr” variable into three categories: cars 

made 10 or more years ago were considered as “old cars,” cars made on or after year 

2000 were considered as “new cars,” and any cars made before year 2000 but less than 10 

years were placed in the “medium-age car” category. 

− Variable codes “LocCode 1” to “LocCode 11” are the dummy variables representing the 

eleven Sacramento intersections used in the data analysis. “LocCode 4” (Mack Road and 

La Mancha Way/Valley Hi Drive) is used as the reference variable during the logistic 

regression analysis. “LocCode 4” is selected as the reference because the distribution of 

“violations occurred at speeds ≤ PSL” versus “violations occurred at speeds > PSL” at 

this intersection is similar to the overall distribution for the entire dataset (see Table 9). 

Using “Speed2” as the dependent variable and the explanatory variables listed in Table 16, 

numerous runs of logistic regression were carried out to find a most suitable logit model that 

portrays the relationship between “vehicle speed at time of violation” and significant 

explanatory variables. In addition, several statistical tests were conducted to ensure things such 

as multicollinearity (occurs when there are strong linear dependencies among the explanatory 

variables) do not affect the robustness of the final model. 
32

Table 16. Dependent and Explanatory Variables Used in Logistic Regression Analysis

Variable Description Variable 

Code 

Variable 

Type Data Format SAS Code

Speed of the Vehicle at Violation Speed ≤ Posted Speed Limit 0 

Time of Violation, MPH Speed2 Dependent Violation Speed > Posted Speed Limit 1 

Younger, 15 to 29 Years Old 1 

Middle-Age, 30 to 59 Years Old 2 Age of the Red Light 

Violator Age Explanatory

Older, 60 Years or Older 3 

Gender of the Red Light Male 0 

Violator Gender Explanatory Female 1 

Time 1 6 a.m. to 9 a.m. 1 

Time 2 10 a.m. to 3 p.m. 2 

Time 3 4 p.m. to 7 p.m. 3 

Time of the Red Light 

Violation 

Time 4 

Explanatory

8 p.m. to 5 a.m. 4 

Old, Made in or Before 1993 1 

Medium, Made from 1994 to 1999 2 Age of the Vehicle Driven 

by Violator VehYr Explanatory

New, Made in or After 2000 3 

LocCode 1 Fair Oaks Boulevard and Howe Avenue 1 

LocCode 2 El Camino Avenue and Evergreen Street 2 

LocCode 3 Arden Way and Exposition Boulevard 3 

LocCode 4 Mack Road and La Mancha Way/Valley Hi Drive 4 

LocCode 5 Mack Road and Center Parkway 5 

LocCode 6 30th Street and Capitol Avenue 6 

LocCode 7 J Street and Alhambra Boulevard 7 

LocCode 8 Broadway and 21st Street 8 

LocCode 9 W Street and 16th Street [Highway 50 Exit at 16th 

Street] 9 

LocCode 10 Howe Avenue and College Town Drive 10 

Location Code for the 

Signalized Intersection 

LocCode 11 

Explanatory

Power Inn Road and Folsom Boulevard 11 

After numerous iterations of logistic regression model runs, testing various combinations of 

explanatory variables (including some interaction terms such as “Age*Gender” and “Age*Time 

1”), a final logit model was selected with “Age,” “Time,” and “LocCode” included in the model 

as the explanatory variables. Main effects “Gender” and “VehYr” did not show any significant 

influence on “Speed2” (p-values > 0.05); hence, they were not included in the final model. In 
33

addition, none of the interaction terms showed significant effects toward “Speed2”; 

consequently, they were not part of the final model. 

Estimated coefficients, standard errors, p-values, and odds ratios for all the explanatory variables 

in the final logit model are presented in Table 17. Out of the possible 46,997 records, 39,042 

records were used for this logistic regression analysis (7,955 records were not used due to 

missing values for either the dependent or the explanatory variables). The generalized R2

 and 

max-rescaled R2 values for this model, both measuring the predictive power of the model, are 

0.338 and 0.453 respectively. The R2

 values are not high but adequate, considering the fact that 

close to 40,000 data points were utilized in the logistic regression. 

Table 17. Estimation Results for the Binary Logit Model with “Speed2” as Dependent Variable

Variable Code Estimated 

Coefficient 

Standard 

Error p-value Odds Ratio 1/Odds Ratio 

Age -0.3883 0.0217 < 0.0001 0.678 1.475 

Time 1 -0.1986 0.0490 < 0.0001 versus Time 4 0.820 1.220 

Time 2 -0.8645 0.0392 < 0.0001 versus Time 4 0.421 2.375 

Time 3 -0.7142 0.0417 < 0.0001 versus Time 4 0.490 2.043 

LocCode 1 -2.4797 0.0709 < 0.0001 versus LocCode 4 0.084 11.938 

LocCode 2 1.4583 0.0605 < 0.0001 versus LocCode 4 4.299 0.233 

LocCode 3 -0.4206 0.0775 < 0.0001 versus LocCode 4 0.657 1.522 

LocCode 5 1.2514 0.0776 < 0.0001 versus LocCode 4 3.495 0.286 

LocCode 6 0.3613 0.0973 0.0002 versus LocCode 4 1.435 0.697 

LocCode 7 0.7995 0.0589 < 0.0001 versus LocCode 4 2.224 0.450 

LocCode 8 1.8422 0.0620 < 0.0001 versus LocCode 4 6.311 0.158 

LocCode 9 -0.9924 0.0623 < 0.0001 versus LocCode 4 0.371 2.698 

LocCode 10 0.5550 0.0586 < 0.0001 versus LocCode 4 1.742 0.574 

LocCode 11 -13.7451 80.5027 0.8644 

The estimated coefficient for variable “Age” is a negative value, signifying that as the age of the 

violator increases, the probability of running the red light while speeding decreases. The inverse 

value of the odds ratio for “Age,” 1.475, implies that the predicted odds of a younger driver 

running a red light while speeding is about 1.5 times the odds of a middle-age driver running a 

red light while speeding. The same odds ratio also applies when the age category goes from 

“middle-age” to “older.” 
34

Analyzed results for the “Age” variable are consistent with our expectation. Research results 

from other studies have found that younger motorists drive more aggressively and are more 

likely to take driving risks compared to older drivers. Hence, we would expect that there are 

more younger drivers who would attempt to “beat the red light” by going through the 

intersection at high speeds that are greater than the posted speed limit. 

The estimated coefficients for “Time 1,” “Time 2,” and “Time 3” are all negative values. The 

negative coefficients suggest that drivers who run the red light in “Time 1,” “Time 2,” and “Time 

3” have a lower probability of speeding through intersections than those who commit red light 

violations in “Time 4.” Hours represented by “Time 4” are from 8 p.m. to 5 a.m., typically 

considered as non-peak hours with low traffic volumes. Red light violations occurring in this 

time period are relatively low, especially from midnight to 5 a.m.. However, a combination of 

non-peak hour and low traffic volume may encourage violators to run the red light at speeds 

greater than the posted speed limit, especially the intentional violators. 

According to the inverse values of the odds ratio presented in Table 17, red light violators in 

“Time 4” are 1.2, 2.4, and 2.0 times more likely to go through the intersection at speeds above 

the posted speed limit than those who ran a red light in “Time 1,” “Time 2,” and “Time 3,” 

respectively. “Time 2,” from 10 a.m. to 3 p.m., has the highest number of total red light 

violations of any time periods and “Time 1” and “Time 3” represent the a.m. and p.m. peak 

hours respectively; however, it appears that many drivers who ran red light in these three time 

periods have done so at speeds lower than the posted speed limit. Possible reasons for such 

phenomenon include: 

− Violators might be “forced” to cross the intersection to avoid being rear-ended; 

− Violators might try to race through the intersection but could not due to heavy 

surrounding traffic or don’t want to get caught speeding; 

− A combination of shorter signal timing cycle and high traffic volume might have created 

many dilemma zones that caused indecisive drivers to violate the red light. 

The estimated coefficients for “LocCode 1” through “LocCode 11” are a mix of positive and 

negative values. “LocCode 11” is considered as an insignificant variable because its p-value is 

greater than 0.05. However, the estimated value for “LocCode 11” was still presented in Table 

17 for informational purpose. 

Compared to the reference variable, “LocCode 4,” motorists who ran red light at “LocCode 1,” 

“LocCode 3,” and “LocCode 9” have a lower probability of speeding through these intersections. 

Positive coefficients for “LocCode 2,” “LocCode 5,” “LocCode 6,” “LocCode 7,” “LocCode 8,” 

and “LocCode 10” suggest that red light violators at these locations are more likely to be 

speeding through intersections compared to “LocCode 4.” 

Examining the characteristics of these intersections, a common factor for “LocCode 1,” 

“LocCode 3,” and “LocCode 9” is identified: high traffic volumes pass through these 

intersections on a daily basis. “LocCode 1” and “LocCode 3” are two key junctions in 

Sacramento and one approach of “LocCode 9” is the off-ramp of a highway; hence, many 

vehicles pass through these intersections daily. Motorists who ran the red light at these three 

intersections might have had difficulty “speeding through” due to heavy surrounding traffic; 
35

consequently, violators at these intersections are less likely to run red light at speeds higher than 

the posted speed limit compared to the reference intersection, “LocCode 4.” 

A logistic regression model presented in this subsection illustrated the influence of “Age,” 

“Time,” and “LocCode” on the speed of the violating vehicle. The results of this model allow us 

to better understand certain aspects of driver behavior around intersections, which will be useful 

to the design and development of effective vehicle-intersection cooperative signal violation 

warning systems. 

4.3. Factors with Significant Influence on Time Elapsed since Red Light Onset 

Lum and Wong (2003) suggested that most violators who ran through intersections after the 

onset of red light for more than 2 seconds were deliberately running the red light. Information 

presented in Table 10 and Figure 11 showed that more than 94 percent of Sacramento’s red light 

violations occurred within 2 seconds after the onset of red light. 

In this subsection, a logistic regression model is developed to illustrate the relationship between 

the dependent variable “elapsed time since red light onset” and a group of explanatory variables. 

The dependent variable, “RedEla2,” has two categories of alternatives: 

1. Drivers who ran through intersections when the elapsed time since red light onset is ≤ 2 

seconds (SAS Code = 0), and 

2. Drivers who ran through intersections when time elapsed since onset of red light is > 2 

seconds (SAS Code =1). 

Indecisive reaction when caught in “dilemma zone” could be one of the major reasons that 

drivers in the first group ran through the red light. When a driver is caught in the dilemma zone 

where he could not cross or stop comfortably, a delayed attempt to go through the intersection 

would trigger the red light photo enforcement camera. What could be some of the reasons that 

drivers still go through the intersection after the traffic light has turned red for more than 2 

seconds? Some drivers might be intentionally violating the traffic law; however, there could be 

other drivers, especially older motorists, who might not react to the light change quick enough to 

stop their vehicles. Moreover, some drivers of all ages might be distracted or inattentive and 

thus unaware of the red light. 

The explanatory variables used in this logistic regression analysis are same as those examined 

earlier in subsection 4.2. 

Table 16 listed all available explanatory variables for this analysis. Two items should be noted 

on the explanatory variables: 

− In the logistic analysis, “Time 4” (from 8 p.m. to 5 a.m.) was again selected as the 

reference variable during the data regression process. “Time 4” is the “off-peak” period 

for traveling with low traffic volume and red light violations in this time period are 

considerably lower compared to other hours of the day (see Figure 3). 

− Variable codes “LocCode 1” to “LocCode 11” are the dummy variables representing the 

eleven Sacramento intersections used in the data analysis. “LocCode 10” (Howe Avenue 

and College Town Drive) is used as the reference variable for this logistic regression 
36

analysis. “LocCode 10” is selected because this intersection has the highest clearance 

interval (5 seconds of yellow time + 2 seconds of all-red time, see Table 6) compared to 

the other ten intersections used in the analysis. As mentioned in a report by Eccles and 

McGee (2001), longer clearance interval will cause drivers to enter intersection later and 

generate disrespect for the traffic signal. 

Using “RedEla2” as the dependent variable and all explanatory variables listed in Table 16, 

several runs of logistic regression were conducted so a fitting logit model that describes the 

relationship between “time elapsed since red light onset” and significant explanatory variables 

can be identified. Once again, necessary statistical tests were carried out to ensure assumed 

statistical properties are valid and the final model is robust. 

After testing various combinations of explanatory variables, including some interaction terms 

such as “Age*Gender,” a logit model was decided with “Age,” “Time,” and “LocCode” again as 

the explanatory variables. Main effects “Gender” and “VehYr” did not have any significant 

effect on “RedEla2” with p-values greater than 0.05 and were not included in the final model. 

None of the interaction terms tested showed significant influence on “RedEla2;” consequently, 

were not part of the final model. 

Table 18 presents the estimated coefficients, standard errors, p-values, and odds ratios for all the 

explanatory variables in the final logit model where “RedEla2” is the dependent variable. Of the 

46,997 red light violation records from Sacramento, a total of 39,045 records were used in this 

logistic regression analysis (7,952 records were not used because of missing values for either the 

dependent or the explanatory variables). The generalized R2

 and max-rescaled R2 values for this 

model, both measuring the predictive power of the model, are 0.0456 and 0.1277 respectively. 

Unlike the result for the “Speed2” model, the estimated coefficient for variable “Age” in the 

“RedEla2” model is a positive value. A positive coefficient indicates that the probability of 

running the red light increases as the age of the violator increases when the time elapsed since 

red light onset is greater than 2 seconds. The odds ratio for “Age” suggests that the predicted 

odds for an older driver running the red light when the time elapsed since red light onset is more 

than 2 seconds is about 1.2 times the odds of a middle-age driver. 

At first glance, results for the “Age” variable may not be what we would normally expect since 

younger motorists tend to drive more aggressively and one would expect that a greater number 

younger drivers would run the red light when the time elapsed since red light onset is greater 

than 2 seconds compared to older drivers. However, researchers have found that older drivers 

require longer time to react to changes and their abilities to divide attention are not as good as 

younger drivers. Consequently, it is reasonable to see that the probability of an older driver 

running the red light when the time elapsed since red light onset is greater than 2 seconds would 

be higher than a younger driver, due to slower reaction time or unawareness of signal change 

when looking at the road ahead. 
37

Table 18. Estimation Results from Binary Logit Model with “RedEla2” as Dependent Variable

Variable Code Estimated 

Coefficient 

Standard 

Error p-value Odds Ratio 1/Odds Ratio 

Age 0.2139 0.0374 < 0.0001 1.238 0.807 

Time 1 -1.3499 0.0756 < 0.0001 versus Time 4 0.259 3.857 

Time 2 -1.6397 0.0567 < 0.0001 versus Time 4 0.194 5.153 

Time 3 -1.8910 0.0688 < 0.0001 versus Time 4 0.151 6.626 

LocCode 1 -0.8471 0.0688 < 0.0001 versus LocCode 10 0.429 2.333 

LocCode 2 -1.0491 0.0804 < 0.0001 versus LocCode 10 0.350 2.855 

LocCode 3 -1.5960 0.1742 < 0.0001 versus LocCode 10 0.203 4.933 

LocCode 4 -1.2175 0.1218 < 0.0001 versus LocCode 10 0.296 3.379 

LocCode 5 -1.9659 0.1597 < 0.0001 versus LocCode 10 0.140 7.141 

LocCode 6 -1.2600 0.2337 < 0.0001 versus LocCode 10 0.284 3.526 

LocCode 7 -2.2337 0.1268 < 0.0001 versus LocCode 10 0.107 9.334 

LocCode 8 -0.8705 0.0763 < 0.0001 versus LocCode 10 0.419 2.388 

LocCode 9 -0.4435 0.0704 < 0.0001 versus LocCode 10 0.642 1.558 

LocCode 11 -2.1148 0.5843 0.0003 versus LocCode 10 0.121 8.288 

 

The negative estimated coefficients for “Time 1,” “Time 2,” and “Time 3” suggest that drivers 

who run the red light in these three time periods have a lower probability of entering the 

intersection when the time elapsed since red light onset is more than 2 seconds compared to 

motorists who run the red light in “Time 4”. The inverse odds ratios shown in Table 18 indicate 

that violators in “Time 4” are 3.9, 5.2, and 6.6 times more likely to run the red light later than 2 

seconds since red light onset compared to violators in “Time 1,” “Time 2,” and “Time 3,” 

respectively. These results are consistent with the results for the “Speed2” model. “Time 4,” 

from 8 p.m. to 5 a.m., is the off-peak travel time, usually with relatively low traffic volumes. 

Total number of red light violations occurring during this time period is low, especially from 

midnight to 5 a.m. However, the situational factors in “Time 4,” off-peak hours and light traffic, 

seem to create an atmosphere where more drivers are likely to run red light when the time 

elapsed since red light onset is more than 2 seconds and with vehicle speed higher than the 

posted speed limit. 

Compared to the reference intersection, “LocCode 10,” motorists who run the red light at 

“LocCode 1” to “LocCode 9” and “LocCode 11” all have a lower probability of entering these 
38

intersections when the time elapsed since red light onset is more than 2 seconds (negative 

estimated coefficients). Of the eleven Sacramento intersections studied in this report, “LocCode 

10” has the highest yellow time at 5 seconds and all-red time at 2 seconds (see Table 6). In the 

field of traffic engineering, it is well recognized that when the yellow interval is too long, drivers 

will become accustomed to challenging the yellow time, enter the intersection later, and 

subsequently run the red light. The situation becomes even worse when there is an additional 

all-red phrase. Logistic regression results presented in this subsection support the belief of many 

traffic engineering professionals – “LocCode 10” has the longest clearance interval and most 

violators who ran the red light after 2 seconds of its onset. 

The logit model for “RedEla2” presented in this subsection linked the effect of “Age,” “Time,” 

and “LocCode” to the time when violators enter the intersections after the onset of the red light. 

Results presented in this subsection gave us another level of understanding about red light 

running behavior. Implications of these results will be discussed in subsection 5.2. 
39

5. CONCLUDING REMARKS 

5.1. List of Major Findings 

Notable findings from this study of Sacramento’s red light violation data are highlighted below:

1. Most red light violations occurred during the normal work hours (i.e., 7 a.m. to 7 p.m.). 

Highest counts of red light violations occurred during the period from 2:00 p.m. to 2:59 p.m.

2. The average red light violation speed was 31.6 mph. More than half (51%) of the drivers ran 

the red light at speed of 30 mph or less. About 14 percent of the violators ran the red light at 

speeds higher than 45 mph.

3. More than 94 percent of the violations occurred within 2 seconds of red light onset.

4. As the age of the red light violator increases, the probability of running the red light while 

speeding decreases. The predicted odds of a younger driver running a red light while speeding is about 1.5 times the odds of a middle-age driver. The same odds ratio also applies 

when the age category goes from “middle-age” to “older.” 

5. Violators in time periods of “6 a.m. to 9 a.m.,” “10 a.m. to 3 p.m.,” and “4 p.m. to 7 p.m.” 

have a lower probability of running the red light while speeding than those who commit red 

light violations in the time period of “8 p.m. to 5 a.m.” Red light violators in this time period 

are 1.2, 2.4, and 2.0 times more likely to be speeding than those who run the red light in time 

periods “6 a.m. to 9 a.m.,” “10 a.m. to 3 p.m.,” and “4 p.m. to 7 p.m.,” respectively. A 

combination of off-peak hours and low traffic volume may encourage violators to run the red 

light at speeds greater than the posted speed limit in the time period of “8 p.m. to 5 a.m.”

6. Compared to the reference intersection for the “Speed2” logit model, “LocCode 4,” violators 

at “LocCode 1,” “LocCode 3,” and “LocCode 9” have a lower probability of running the red 

light while speeding due to their heavy traffic volumes. Violators at remaining locations are 

more likely to run the red light while speeding than “LocCode 4.” (See Table 16 for complete information regarding “LocCode 1” to “LocCode 11”). The probability of running the 

red light after 2 seconds of its onset increases as the age of the violator increases, according 

to the “RedEla2” logit model. The odds ratio for variable “Age” suggests that the predicted 

odds for an older driver running the red light after 2 seconds of its onset is about 1.2 times 

the odds of a middle-age driver. Older drivers require longer time to react to changes and 

their abilities to divide attention are not as good as younger drivers. Violators in time periods 

of “6 a.m. to 9 a.m.,” “10 a.m. to 3 p.m.,” and “4 p.m. to 7 p.m.” have a lower probability of 

running the red light after 2 seconds of its onset than violators in the time period of “8 p.m. 

to 5 a.m.” Violators in the time period of “8 p.m. to 5 a.m.” are 3.9, 5.2, and 6.6 times more 

likely to run the red light after 2 seconds of its onset than violators in time periods of “6 a.m. 

to 9 a.m.,” “10 a.m. to 3 p.m.,” and “4 p.m. to 7 p.m.,” respectively.

7. Most violations after 2 seconds of red light onset occurred at the intersection with the highest 

yellow plus all-red time.

8. Red light violation rates (calculated from issued violation citations) for the 11 Sacramento 

intersections ranged from 0.064 violation per 1,000 crossing vehicles to 0.294 violation per 

1,000 crossing vehicles. In comparison to red light violation rates reported by other studies, 

these estimates of red light violation rates are quite low. A probable reason is because the red 

light violation records used to calculate these rates are actual citations issued and only 

approximately 35 percent of the photos taken by Sacramento’s photo enforcement cameras 

lead to red light violation citations.
40

5.2. Implications of Findings 

Implications of findings from this analysis are presented here from the perspective of designing 

and planning a CSVWS FOT: 

1 The experimental design for the CSVWS FOT must examine the influence of driver age. 

Drivers in different age groups exhibit diverse behavior when approaching signalized 

intersections based on the findings of this study. How will older drivers respond to the 

warnings issued by CSVWS versus younger drivers? Will CSVWS warnings create different 

levels of distraction to drivers at different age groups? 

2 Logistic regression analyses conducted in this study did not find significant relationship 

between red light violators’ gender and their driving behavior approaching signalized 

intersections. Consequently, “gender” is a variable that could be excluded from the 

experimental design of the CSVWS FOT, especially when there are constraints on budget 

and time. 

3 The logit models developed in this study suggest that the time period of “8 p.m. to 5 a.m.” 

seems to have a combination of environmental and situational factors that would encourage 

red light violators to run through intersections at speeds higher than the posted speed limit or 

after the red light has elapsed for more than 2 seconds. When recruiting study participants 

for the CSVWS FOT, enough subjects must be included who would often travel in the offpeak period (e.g., 8 p.m. to 5 a.m.) so ample driver data from that time period can be 

gathered. Accordingly, comparison of driver reaction towards the CSVWS warnings at 

different time periods can be made. 

4 Findings from the logit models presented in this report also suggest that several variations of 

the CSVWS warning algorithm and warning messages might be necessary for different time 

periods throughout the day. At certain time period(s) when drivers are susceptible of 

speeding through intersections or entering the intersections late when light changes, CSVWS 

warnings need to be issued earlier and warning messages need to be “decisive” to effectively 

encourage more drivers to stop for the red light. 

5 Logistic regression results indicated that traffic volumes and duration of clearance intervals 

(yellow time and all-red phase) at intersections seem to have certain influence on red light 

violators’ decisions to go through intersections at speeds greater than the posted speed limit 

or enter intersections when the elapsed time since red light onset is more than 2 seconds. In a 

CSVWS FOT, signalized intersections with various characteristics must be included in the 

experimental design so the effect of factors such as traffic volume and clearance interval on 

CSVWS can be closely studied.
41

6. REFERENCES 

American City & County (2001). State Farm Names the 10 Most Dangerous 

Intersections. Available from 

www.americancityandcounty.com/news/government_state_farm_names/index.html. 

BMI (December 2001). Infrastructure-Based Intersection Collision Avoidance Concept Study, 

Technical Memorandum 2 (Contract No. DTFH61-96-C-00077). Silver Spring, Maryland: BMI. 

Campbell, B. N., Smith, J. D., and Najm, W. G. (September 2004). Analysis of Fatal Crashes 

Due to Signal and Stop Sign Violations (DOT-VNTSC-NHTSA-02-09, DOT HS 809 779). 

Washington, DC: U.S. Department of Transportation. 

Chang, M.-S., Messer, C. J., and Santiago, A. J. (1985). Timing Traffic Signal Change Intervals 

Based on Driver Behavior. Transportation Research Record 1027, 20-30. Transportation 

Research Board, Washington, DC. 

Cerrelli, E. C. (January 1998). Crash Data and Rates for Age-Sex Groups of Drivers, 1996

(Research Note, National Highway Traffic Safety Administration, U.S. Department of 

Transportation). Available from www.nhtsa.dot.gov/people/ncsa. 

Brewer, M. A., Bonneson, J., Zimmerman, K. (2002). Engineering Countermeasures to RedLight-Running. Proceeding of the ITE 2002 Spring Conference and Exhibit (CD-ROM). 

Washington, DC: Institute of Transportation Engineers. 

Eccles, K. A., and McGee H. W. (July 2001). A History of the Yellow and All-Red Intervals for 

Traffic Signals. Washington, DC: Institute of Transportation Engineers. 

Fakhry, S. M. and Salaita, K. (2002). Aggressive Driving: A Preliminary Analysis of a Serious 

Threat to Motorists in a Large Metropolitan Area. Journal of Trauma, 52(2), 217-224. 

Federal Highway Administration (2002). Highway Statistics 2001. Washington DC: U.S. 

Department of Transportation. 

Insurance Institute for Highway Safety (2000). Red Light Running Factors into More than 800 

Deaths Annually; More than Half of Those Who Die are Hit by Red Light Violators. Available 

from www.iihs.org/news/2000/iihs_news_071300.pdf. 

Kamyab, A., McDonald, T., and Stribiak, J. J. (2002). The Scope and Impact of Red Light 

Running in Iowa. Preprint CD-ROM of the 81st Annual Meeting of the Transportation Research 

Board, Washington, DC. 

Kamyab, A., McDonald, T., Stribiak, J. J., and Storm, B. (December 2000). Red Light Running 

in Iowa: The Scope, Impact, and Possible Implications. Ames IA: Center for Transportation 

Research and Education, Iowa State University. 

Kraus, E. and Quiroga, C. (2004). Red Light Running Trends in Texas. Preprint CD-ROM of 

the 83rd Annual Meeting of the Transportation Research Board, Washington, DC. 
42

Lum, K. M. and Wong, Y. D. (2003). Impacts of Red Light Camera on Violation 

Characteristics. Journal of Transportation Engineering, November/December, 648-656.

McGee, H. W. (2002). Safety Impact of Red Light Camera Enforcement Program. Proceeding 

of the ITE 2002 Spring Conference and Exhibit (CD-ROM). Washington, DC: Institute of 

Transportation Engineers. 

Milazzo II, J. S., Hummer, J. E., and Prothe, L. M. (June 2001). A Recommended Policy for 

Automated Electronic Traffic Enforcement of Red Light Running Violations in North Carolina. 

Raleigh, North Carolina: Institute for Transportation Research and Education, North Carolina 

State University. 

Milazzo II, J. S., Hummer, J. E., Rouphail, N. M., Prothe, L. M., and McCurry, J. B. (2002). The 

Effect of Dilemma Zones on Red Light Running Enforcement Tolerances. Preprint CD-ROM of 

the 81st Annual Meeting of the Transportation Research Board, Washington, DC. 

Porter, B. E. and Berry, T. D. (2001). A Nationwide Survey of Self-Reported Red Light 

Running: Measuring Prevalence, Predictors, and Perceived Consequences. Accident Analysis 

and Prevention, 33, 735-741. 

Porter, B. E. and England, K. J. (2000). Predicting Red-Light Running Behavior: A Traffic 

Safety Study in Three Urban Settings. Journal of Safety Research, 31(1), 1-8. 

Retting, R. A., Chapline, J. F., and Williams, A. F. (September 2000). Changes in Crash Risk 

Following Re-Timing of Traffic Signal Change Intervals. Arlington, Virginia: Insurance Institute 

for Highway Safety. 

Retting, R. A. and Green, M. A. (1997). Influence of Traffic Signal Timing on Red-Light 

Running and Potential Vehicle Conflicts at Urban Intersections. Transportation Research 

Record 1595, 1-7. Transportation Research Board, Washington, DC. 

Retting, R. A., and Kyrychenko, S. Y. (April 2001). Crash Reductions Associated with Red 

Light Camera Enforcement in Oxnard, California. Arlington, Virginia: Insurance Institute for 

Highway Safety. 

Retting, R. A., and Kyrychenko, S. Y. (2002). Reductions in Injury Crashes Associated with 

Red Light Camera Enforcement in Oxnard, California. Preprint CD-ROM of the 81st Annual 

Meeting of the Transportation Research Board, Washington, DC. 

Retting, R. A. and Williams, A. F. (1996). Characteristics of Red Light Violators: Results of a 

Field Investigation. Journal of Safety Research, 27(1), 9-15. 

Retting, R. A., Ulmer, R. G., and Williams, A. F. (1999). Prevalence and Characteristics of Red 

Light Running Crashes in the United States. Accident Analysis and Prevention, 31, 687-694. 

Retting, R. A., Williams, A. F., Farmer, C. M., and Feldman, A. F. (1999a). Evaluation of Red 

Light Camera Enforcement in Oxnard, California. Accident Analysis and Prevention, 31, 169-

174. 
43

Retting, R. A., Williams, A. F., Farmer, C. M., and Feldman, A. F. (1999b). Evaluation of Red 

Light Camera Enforcement in Fairfax, Virginia. ITE Journal, Vol. 69, No. 8, 30-34. 

Retting, R. A., Williams, A. F., and Greene, M. A. (1998). Red-Light Running and Sensible 

Countermeasures: Summary of Research Findings. Transportation Research Record 1640, 23-

26. Transportation Research Board, Washington, DC. 

Retting, R. A., Williams, A. F., Preusser, D. F., and Weinstein, H. B. (1995). Classifying Urban 

Crashes for Countermeasure Development. Accident Analysis and Prevention, 27(3), 283-294. 

Ruby, D. E. and Hobeika, A. G. (2003). Assessment of Red Light Running Cameras in Fairfax 

County, Virginia. Transportation Quarterly, Vol. 57, No. 3, 33-48. 

Schattler, K. L., Hill, C., and Datta, T. K. (2002). Clearance Interval Design and Red Light 

Violations. Proceeding of the ITE 2002 Spring Conference and Exhibit (CD-ROM). 

Washington, DC: Institute of Transportation Engineers. 

Zador, P., Stein, H., Shapiro, S., and Tarnoff, P. (1985). Effect of Signal Timing on Traffic Flow 

and Crashes at Signalized Intersection. Transportation Research Record 1010, 1-8. 

Transportation Research Board, Washington, D.. 
44

Appendix A. Photos of City of Sacramento’s 11 RLPEC-Equipped 

Intersections 

Figure 12. Picture of Fair Oaks Boulevard and Howe Avenue Intersection 

Figure 13. Picture of El Camino Avenue and Evergreen Street Intersection
45

Figure 14. Picture of Arden Way and Exposition Boulevard Intersection

Figure 15. Picture of Mack Road and La Mancha Way/Valley Hi Drive Intersection
46

Figure 16. Picture of Mack Road and Center Parkway Intersection

Figure 17. Picture of 30th Street and Capitol Avenue Intersection
47

Figure 18. Picture of J Street and Alhambra Boulevard Intersection 

Figure 19. Picture of Broadway and 21st Street Intersection
48

Figure 20. Picture of W Street and 16th Street (Highway 50 Exit at 16th Street) Intersection

Figure 21. Picture of Howe Avenue and College Town Drive Intersection
49

Figure 22. Picture of Power Inn Road and Folsom Boulevard Intersection
50

Appendix B. Photos of Red Light Photo Enforcment Camera and Warning 

Sign 

Figure 23. Picture of Red Light Photo Enforcment Camera in the City of Sacramento

Figure 24. Picture of Red Light Photo Enforcment Camera Warning Sign in the City of 

Sacramento 
51

Appendix C. GIS Map of a Selected RLPEC-Equipped Intersection in 

Sacramento 

Figure 25. GIS Mapping of Fair Oaks Boulevard and Howe Avenue Intersection
52

Appendix D. Distributions of Red Light Violations by Speed at Individual 11 

RLPEC-Equipped Intersections 

0

100

200

300

400

500

600

700

800

900

1,000

1,100

-35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40 45 50

(Speed at Time of Violation) - (Posted Speed Limit)

Red Light Violation Count

Figure 26. Frequency Distribution of Red Light Violations by Speed at Fair Oaks Boulevard and 

Howe Avenue Intersection
53

0

50

100

150

200

250

300

350

400

-30 -20 -10 0 10 20 30 40 50 60

(Speed at Time of Violation) - (Posted Speed Limit)

Red Light Violation Count

Figure 27. Frequency Distribution of Red Light Violations by Speed at El Camino Avenue and 

Evergreen Street Intersection

0

20

40

60

80

100

120

140

-30 -20 -10 0 10 20 30 40 50

(Speed at Time of Violation) - (Posted Speed Limit)

Red Light Violation Count

Figure 28. Frequency Distribution of Red Light Violations by Speed at Arden Way and 

Exposition Boulevard Intersection
54

0

20

40

60

80

100

120

-30 -20 -10 0 10 20 30 40 50 60

(Speed at Time of Violation) - (Posted Speed Limit)

Red Light Violation Count

Figure 29. Frequency Distribution of Red Light Violations by Speed at Mack Road and La 

Mancha Way/Valley Hi Drive Intersection

0

10

20

30

40

50

60

70

80

90

100

-30 -20 -10 0 10 20 30 40 50

(Speed at Time of Violation) - (Posted Speed Limit)

Red Light Violation Count

Figure 30. Frequency Distribution of Red Light Violations by Speed at Mack Road and Center 

Parkway Intersection
55

0

10

20

30

40

50

60

-20 -10 0 10 20 30 40 50 60

(Speed at Time of Violation) - (Posted Speed Limit)

Red Light Violation Count

Figure 31. Frequency Distribution of Red Light Violations by Speed at 30th Street and Capitol 

Avenue Intersection

0

50

100

150

200

250

300

350

-20 -10 0 10 20 30 40 50 60 70

(Speed at Time of Violation) - (Posted Speed Limit)

Red Light Violation Count

Figure 32. Frequency Distribution of Red Light Violations by Speed at J Street and Alhambra 

Boulevard Intersection
56

0

50

100

150

200

250

300

350

400

450

-20 -10 0 10 20 30 40 50 60 70

(Speed at Time of Violation) - (Posted Speed Limit)

Red Light Violation Count

Figure 33. Frequency Distribution of Red Light Violations by Speed at Broadway and 21st Street 

Intersection

0

50

100

150

200

250

300

350

400

450

500

-30 -20 -10 0 10 20 30 40 50 60

(Speed at Time of Violation) - (Posted Speed Limit)

Red Light Violation Count

Figure 34. Frequency Distribution of Red Light Violations by Speed at W Street and 16th Street 

(Highway 50 Exit at 16th Street) Intersection
57

0

50

100

150

200

250

300

350

-40 -30 -20 -10 0 10 20 30 40 50

(Speed at Time of Violation) - (Posted Speed Limit)

Red Light Violation Count

Figure 35. Frequency Distribution of Red Light Violations by Speed at Howe Avenue and 

College Town Drive Intersection

0

5

10

15

20

25

30

35

-40 -30 -20 -10 0 10 20 30 40 50

(Speed at Time of Violation) - (Posted Speed Limit)

Red Light Violation Count

Figure 36. Frequency Distribution of Red Light Violations by Speed at Power Inn Road and 

Folsom Boulevard Intersection

DOT-VNTSC-NHTSA-05-01 

DOT HS 810 580 

March 2006 

Research and 

Innovative Technology 

Administration 

Volpe National 

Transportation System Center 

Cambridge, MA 02142-1093