Investigation of Red Light Running Factors

Transcription

1 Investigation of Red Light Running Factors Final Report July 2004 Prepared for: Southeast Transportation Center The University of Tennessee Knoxville, Tennessee Prepared by: Scott S. Washburn Assistant Professor Kenneth G. Courage Professor Department of Civil and Coastal Engineering University of Florida 365 Weil Hall, Box Gainesville, FL Task Order #: 00-UF-R1 Grant: DTRS99-G-0004 Account #: R UF Project #:

2 Table of Contents Introduction... 1 Objectives... 1 Tasks... 2 Literature Review... 2 Definitions... 2 Previous Studies... 3 Research Approach... 6 Study Site Selection... 6 Site Descriptions... 6 Data Collection Data Reduction Results and Analysis Red Light Running Frequency and Rate Red Light Running Severity Comparison of Yellow and All-Red Intervals Site-Specific Issues Effectiveness of the RLRAP Technology Conclusion Acknowledgement References Appendix A: Data and calculations results for yellow and all-red timings based on ITE method Appendix B: RLRAP Field Equipment Appendix C: RLRAP Lab Equipment Appendix D: RLRAP Users Guide Appendix E: Video Overlay Setup University of Florida, Civil Engineering i

3 List of Tables Table 1. Roadway, Traffic, and Signal Timing Characteristics for Archer & 34 th... 7 Table 2. Roadway, Traffic, and Signal Timing Characteristics for University & 34 th... 8 Table 3. Roadway, Traffic, and Signal Timing Characteristics for University & 17 th... 9 Table 4. Roadway, Traffic, and Signal Timing Characteristics for ISB & Williamson Table 5. Roadway, Traffic, and Signal Timing Characteristics for Clyde Morris & Big Tree. 11 Table 6. Example red light violation data reduction coding Table 7. Codes used for red light running data reduction Table 8. Red light running violation statistics for the morning peak period Table 9. Red light running violation statistics for the mid-day peak period Table 10. Red light running violation statistics for the afternoon peak period Table 11. Red light running rates by movement type and proportion of all violations by movement type; (a) morning peak period, (b) mid-day peak period, and (c) afternoon peak period Table 12. Severity statistics by type of movement Table 13. Severity statistics by movement direction Table 14. Yellow interval values by ITE calculation and FDOT guidelines Table 15. Comparison of yellow and all-red interval timings University of Florida, Civil Engineering ii

4 List of Figures Figure 1. Intersection of Archer Rd. and 34 th St. (looking north)... 7 Figure 2. Intersection of University Ave. and 34 th St. (looking west)... 8 Figure 3. Intersection of University Ave. and 34 th St.; (a) street level looking east and (b) aerial Figure 4. Intersection of International Speedway Blvd. and Williamson Avenue (a) street level looking northeast and (b) aerial Figure 5. Intersection of Clyde Morris Blvd. and Big Tree Road; (a) street level looking west and (b) aerial Figure 6: Screen capture from a videotape obtained from the field Figure 7: Screen capture of a processed videotape Figure 8. Red light running rate by vehicle type at ISB and Williamson Figure 9. Red light running rate by vehicle type at University and 34 th Figure 10. Red light running severity, left turns (ISB & Williamson) Figure 11. Red light running severity, through movements (ISB & Williamson) Figure 12. Red light running severity, left turns (University and 34 th ) Figure 13. Red light running severity, through movements (University and 34 th ) Figure 14. Red light running severity, left turns (Archer and 34 th ) Figure 15. Red light running severity, through movements (Archer and 34 th ) Figure 16. Red light running severity, left turns (University and 17 th ) Figure 17. Red light running severity, through movements (University and 17 th ) Figure 18. Westbound to eastbound U-Turn movement at ISB and Williamson Figure 19. Recommendation to reduce red light running through improved access management Figure 20. Signal controller cabinet with RLRAP field equipment Figure 21. Close-up view of field recording VCR in cabinet Figure 22. Close-up view of video monitor in cabinet Figure 23. Close-up view of RLRAP encoder in cabinet Figure 24. Laboratory RLRAP workstation Figure 25. RLRAP Connectivity Diagram University of Florida, Civil Engineering iii

5 Abstract Red light running has developed into a significant safety problem in the United States, as well as many locations worldwide. The running of red lights by motorists is one of the main causes of crashes at signalized intersections. This issue and its public perception are putting transportation and law enforcement agencies in the spotlight to address red light running concerns. There is still much that can be learned about engineering factors that influence red light running, but violation data that would support the development of countermeasures are very difficult to obtain because red light running is difficult to study by manual observation in the field. In this project, University of Florida researchers applied a custom developed red light running data collection system to study red light running behavior at several intersections with varying characteristics. This data collection system worked very well overall, and provided for the accurate collection of unbiased red light running data in a reasonably efficient manner. This study identified some factors that were likely correlated with the levels of red light running at the subject intersections. These included yellow interval timing, g/c ratio, progression quality, phase max-out vs. gap-out, approach grade, time of day (as a surrogate for trip purpose), driver population, access management, and of course the basic exposure variables of volume and cycle length. Left turns violation rates were found to be higher overall than through movement violation rates. It was also observed that a large percentage of the red light running at the study sites was deliberate, and this behavior is unlikely to be overcome by engineering measures alone. University of Florida, Civil Engineering iv

6 INTRODUCTION Red light running (RLR) has developed into a significant safety problem in the United States, as well as many locations worldwide. The running of red lights by motorists is one of the main causes of crashes at signalized intersections. According to estimates by the Federal Highway Administration (FHWA) for 2002 [1], there were 207,000 red-light running crashes at intersections. These crashes resulted in 178,000 injuries and 920 fatalities, and an economic loss estimated at $14 billion per year. The cost to society for Florida motorists running red lights in 2001 was approximately $375 million, based on 9,111 crashes with 107 fatalities and 13,680 injuries [2]. This issue and its public perception are putting transportation and law enforcement agencies in the spotlight to address red light running concerns. Furthermore, legislation has been introduced in a number of states to permit the use of automated technologies for the enforcement of red light running violations. As of May 2004, according to Insurance Institute of Highway Safety (IIHS) [3] more than 90 communities in 17 states and the District of Columbia have red light running automated enforcement systems in use. These states include Arizona, California, Colorado, Delaware, Georgia, Illinois, Maryland, New York, North Carolina, Ohio, Oregon, Rhode Island, South Dakota, Tennessee, Texas, Virginia and Washington. Opponents of these systems claim that red light running is preventable if the intersections are properly engineered. Consequently, traffic engineers have the responsibility to ensure that red light running problems at intersections are not due to engineering deficiencies, and to only recommend the implementation of an automated enforcement system if that appears to be the only reasonable alternative to mitigating the problem. There is still much that can be learned about engineering factors that influence red light running, but violation data that would support the development of countermeasures are very difficult to obtain because red light running is difficult to study by manual observation in the field. In this project, University of Florida researchers applied a red light running data collection system (developed under a previous STC grant) to study red light running behavior at several intersections with varying characteristics. Objectives There were two primary objectives for this project. In a precursor project to this one [4], a new technology for the collection of red light running data was developed and tested (in terms of reliability and accuracy). This data collection technology is referred to as the Red Light Running Analysis Package (RLRAP). Thus, one objective of this project was to assess the effectiveness of the RLRAP for conducting red light running studies. The other objective of this project was to utilize the output of this technology to study red light running behavior at several intersections and identify any factors that may be contributing to the frequency, rate, and severity of red light running events. University of Florida, Civil Engineering 1

7 Tasks Select signalized intersection sites for study Deploy red light running data collection technology to the selected intersections Collect traffic and signal timing data Process video tapes to obtain video with signal status information displayed simultaneously with traffic movements View processed video tapes and record essential information for each red light running event Analyze data and report results LITERATURE REVIEW A literature review was performed in two areas. The first area was a review of the legal guidance on permissible driver actions when faced with red and yellow signal indications. The second area was a review of previous studies on red light running for different techniques and technologies that have been developed and used for the collection of red light running data and the results of subsequent data analyses. Definitions Red Signal Indication Before commencing with a study of red light running, it is important to define it. This definition is not necessarily universal, as it can vary from one state to another. Definitions from several sources are given as follows. According to the FHWA [5], Red-light-running occurs when a driver enters an intersection after the traffic signal has turned red. A motorist, who is already in an intersection when the signal changes to red, such as when waiting to make a left turn, is not a red-lightrunner. According to the Insurance Institute for Highway Safety (IIHS) [6], A violation occurs when a motorist enters an intersection (often deliberately) some time after the signal light has turned red. Motorists inadvertently in an intersection when the signal changes to red when waiting to turn, for example aren t red light runners. According to the Uniform Vehicle Code (UVC) [7], a motorist facing a steady circular red signal shall stop at a clearly marked stop line, but if none, before entering the crosswalk on the near side of the intersection, or if none, then before entering the intersection and shall remain standing until an indication to proceed is shown (section ). The law as stated in the UVC is considered a permissive-yellow law, meaning that the driver can enter the intersection during the entire yellow interval and be in the intersection during the red indication as long as he/she entered the intersection during the yellow interval. However, some states employ one of two types of restrictive yellow laws: University of Florida, Civil Engineering 2

8 Vehicles can neither enter the intersection nor be in the intersection on red; or Vehicles must stop upon receiving the yellow indication, unless it is not possible to do so safely. As per the Florida Statute, Section (1)(c)1, F.S [8] requires Vehicular traffic facing a steady red signal shall stop before entering the crosswalk on the near side of the intersection or, if none, then before entering the intersection and shall remain standing until a green indication is shown. Yellow Signal Indication According to the Federal Highway Administration (FHWA) [9], the meaning of a yellow signal indication varies slightly from state to state. Typically, a steady yellow light or arrow warns that the light is about to change. If a driver has not entered the intersection, he/she should come to a stop, if they can do so safely. If the driver is already in the intersection, he/she should continue moving in order to clear it. Previous Studies The intent of this report is not to provide an all-inclusive review of studies on red light running, but rather to provide a sample of some different studies that have been performed and the data collection equipment/techniques used, as well as some overall results, if applicable. In a study done by Fahkry and Salaita [10], red light running data were collected by the use of an integrated system of inductance loops, 35-mm cameras, and computers that continuously monitored 82 sites. They also collected data using trained observers. They classified the violations into 3 types: stop and go, rolling stop, and at-speed. Fahkry and Salaita found that on average about 40% 80% of drivers in their study were traveling at least 10 mi/h above the posted speed limit. They also found that over half the violations were above the posted speed limit and 16% were 10 mi/h or more above the posted speed limit. The cameras observed 1.5 violations per 1,000 vehicles and the trained personnel observed 1.3 violations per 1,000 vehicles and Fahkry and Salaita decided that it was practically the same. In a study done by Farraher, et al. [11], red light running data were collected using a system they called Motion Image Recording System or MIRS. The MIRS system consisted of a model 36mST-MC red light camera and flash unit (manufactured by Gatsometer B.V. of Holland) and a series of three-turn saw-cut inductance loop detectors. The objective of this study was to evaluate the effectiveness of advanced warning flashers at reducing red light running at one intersection. They concluded that the advanced warning flashers were effective at this site, but that the number of violators and their speeds remained unacceptably high. In a study done by Schattler, et al. [12], red light running data were collected using video cameras. The paper, however, did not provide any further details on the video camera setup or data collection implementation. The objective of their study was to evaluate the effect University of Florida, Civil Engineering 3

9 of introducing all-red intervals on red light running and late exits (i.e., vehicles still crossing the intersection when an opposing movement receives the green indication) at three intersections. The mean red light running rate (in violations per hour) for the three intersections ranged from 0.18 to 6.61 in the before condition and from 0.43 to 2.32 after the implementation of revised all-red intervals. Mixed results were obtained for the red light running violations. However, the analysis revealed that the rate of late exits decreased significantly at all study intersections after the installation of test change and clearance intervals. In a study done by Lum and Wong [13], red light running data were collected using a data logger in conjunction with loop sensors to study the before and after effects of cameras at two T and one X intersections. They used an M660 type data logger to gather data such as volume, speed, distance to stop line at start of yellow, and timing and status of each signal. The approaches along the main road of each site were wired up with inductance loop sensors for field data collection. Sensors were installed in the middle and median lanes of the cameraviewed approach, and either in the middle or median lane of the opposite but non-camera approach. Loop sensors were not installed in the leftmost shared lane. The objective of their study was to determine the impact of an automated enforcement camera system on drivers stopping propensity (at the onset of a yellow indication). Violation rates (on a daily and per lane basis) at three study intersections (in Singapore) ranged from 12.9 to 356 in the original condition and from 12.1 to in the after (instrumented) condition. Yung and Lai [14] developed a new red light running data collection system based on video image analysis that does not require a wiring connection to the signal controller. Most simply, it extracts vehicle position/motion information like other video image analysis systems, but also extracts signal status information directly from the video field of view (assuming signal lights are within the field of view). This approach requires multiple cameras for an entire intersection. A very limited trial of the video equipment and the processing algorithm yielded promising results. The focus of this study was strictly on the development of the data collection technology and very limited red light running data were collected. The objective of a study by Tarko and Naredla [15] was to determine the effectiveness of a video image analysis system (Autoscope TM 2004) to measure red light running. The Autoscope system combines image processing with pattern recognition. It uses virtual loop detectors on video images and detects the movement of vehicles. Using this information in combination with signal status information, it can be determined when a vehicle entered the intersection during a red interval. With this approach, one camera per approach is typically employed; however, a portion of the intersection downstream of the approach s stop bar must be visible within the field of view. Out of 107 violations observed during five days, 55 were detected correctly (51%) by the Autoscope system. Thirty-four false detections (38%) were also reported. No significant difference in performance was observed between daytime and nighttime periods, but monitoring of through lanes was considerably more effective than of left-turn lanes. No red light running rate data were provided in this paper. University of Florida, Civil Engineering 4

10 According to a report [16] by the Institute of Transportation Engineers (ITE), a 2-hour traditional enforcement effort at a high-volume intersection in Raleigh, NC resulted in 36 tickets, which is a rate of 18 violations per hour or an average of one violation about every 3.5 minutes. A study conducted over several months at a busy intersection (30,000 vehicles per day) in Arlington, VA revealed violation rates of one red-light runner every 12 min. During the morning peak hour, a higher rate of one violation every 5 min was reported. A lower volume intersection (14,000 vehicles per day), also in Arlington, had an average of 1.3 violations per hour and 3.4 in the evening peak hour. Also according to the ITE report [16], Bonneson et al. [17] reviewed many past studies regarding various intersection characteristics as they relate to red light running. Three intersection characteristics were highlighted as exposure factors, including flow rate, number of signal cycles and phase termination by max-out. Field studies support the logical conclusion that as more vehicles are exposed to the potential of red-light running, the violation rate increases. These intersection characteristics as described in the Bonneson report are as follows. Flow rate or volume: Every vehicle approaching the intersection at the onset of the yellow is exposed to the potential of red-light running. A decision must be made to stop or proceed through the intersection. As the number of approaching vehicles increases, the number of red-light runners will also likely increase. Number of signal cycles: The more times the yellow phase is displayed, the more potential for red-light running. Hence, researchers should report the violation rates normalized by the number of signal cycles. Phase termination by max-out: Actuated signal systems operate using green extension time as long as the approach is occupied. However, the green may reach its maximum limit and max-out forcing the green phase to end regardless of whether the approach is occupied. Conversely, the signal may gap-out because the approach has been unoccupied for a set period of time. There is greater potential for red-light running as the frequency of max-out increases. Hunter [18] conducted a study of red light running at several intersections in Rhode Island, using the RLRAP developed by the Transportation Research Center at the University of Florida. He found rates ranging from violations per hour at 20 intersections throughout the state, with an overall mean of 6.3 violations per hour. He also found that approximately 53% of the red light running violations occurred within 1 second of the signal turning red, while approximately 19% of the violations occurred after 2 seconds had elapsed. University of Florida, Civil Engineering 5

11 RESEARCH APPROACH For this study, the resources were not available to perform a large scale experimental design. Additionally, we found that there was resistance by some public agencies to make signal phasing and/or timing changes for testing purposes, mainly for liability reasons. Thus, the primary goal of this study, aside from assessing the effectiveness of the RLRAP technology for performing studies of this type, was to attempt to identify factors that were potentially contributing to the occurrences of red light running at each site, through a combination of inference of the summary statistics and specific site knowledge/experience. Study Site Selection Data were collected from five sites. Since the cooperation of the local agency was necessary for the installation of the red light running data collection hardware, the selected sites were confined to the cities of Gainesville, FL and Daytona Beach, FL due to previously established relationships. Sites were selected to provide a variety of traffic and roadway conditions. Three sites were in Gainesville and two in Daytona Beach, as follows: Archer Rd. & 34 th Street (Gainesville, FL) University Ave. & 34 th Street (Gainesville, FL) University Ave. & 17 th Street (Gainesville, FL) International Speedway Blvd. & Williamson Ave. (Daytona Beach, FL) Clyde Morris Blvd. & Big Tree Rd. (Daytona Beach, FL) With Gainesville being home to the University of Florida and Santa Fe Community College, the driving population is predominantly year old students. Daytona Beach has a wide range and more even distribution of driver demographics. Gainesville has a population of approximately 95,000. Daytona Beach has a population of approximately 65,000. Site Descriptions Details about each of the selected sites are given in this section. Archer Road & 34 th Street This is an intersection of two major arterials to the south and west of the University of Florida. This intersection serves much of the university-bound traffic, as well as a large amount of nonuniversity related traffic. This is a very high volume intersection, has a large area, and has high approach speeds. Pedestrian and bicycle activity at this intersection is negligible. All site specific characteristics are given in Table 1. A photo of this intersection is shown in Figure 1. University of Florida, Civil Engineering 6

12 Table 1. Roadway, Traffic, and Signal Timing Characteristics for Archer & 34 th Intersection: Camera Location: N-S street: E-W street: Peak Period: Archer Rd. & 34 th St. Southeast corner facing northwest 34 th St. Archer Rd. 4:30-5:30 PM NB SB WB EB L T R L T R L T R L T R Lanes 2 3 Shrd 2 3 Shrd 2 3 Shrd 2 3 Shrd Posted Speed (mi/h) Peak Hour Volume * NEMA Signal Phase Yellow Interval (sec) All-Red Interval (sec) * Right turn bay for this approach was not within camera field of view. Peak Hour Average Phase and Cycle Lengths (sec) Phase (Ring 1) Total Phase (Ring 2) Cycle Average Figure 1. Intersection of Archer Rd. and 34 th St. (looking north) University Avenue & 34 th Street This is an intersection of two major arterials to the north and west of the University of Florida. This intersection serves much of the university-bound traffic, as well as a large amount of nonuniversity related traffic. This is a high volume intersection, is moderate in area, and has medium approach speeds. Pedestrian and bicycle activity at this intersection is minimal. All University of Florida, Civil Engineering 7

13 site specific characteristics are given in Table 2. A photo of this intersection is shown in Figure 2. Table 2. Roadway, Traffic, and Signal Timing Characteristics for University & 34 th Intersection: Camera Location: N-S street: E-W street: Peak Period University Ave. & 34 th Street Southwest corner facing northeast 34 th St. University Ave. 4:30-5:30 PM NB SB WB EB L T R L T R L T R L T R Lanes Shrd 1 2 Shrd 1 2 Shrd Posted Speed (mi/h) Peak Hour Volume NEMA Signal Phase Yellow Interval (sec) All-Red Interval (sec) Peak Hour Average Phase and Cycle Lengths (sec) Phase (Ring 1) Total Phase (Ring 2) Cycle Average Figure 2. Intersection of University Ave. and 34 th St. (looking west) University Avenue & 17 th Street This is an intersection of a major arterial and a minor arterial on the north-central boundary to the University of Florida campus. This intersection serves much of the university-bound traffic, as well as a large amount of non-university related traffic. The minor street serves as University of Florida, Civil Engineering 8

14 one of several access points to the university campus. The major arterial (University) serves a high volume of traffic. The minor arterial (17 th ) generally serves a low volume of traffic except during peak campus entrance and exit times, at which time it serves a moderate volume of traffic. The intersection area is moderate and approach speeds are relatively low. This intersection also serves a very high volume of pedestrian and bicycle traffic, primarily university students, staff, and faculty. All site specific characteristics are given in Table 3. A photo of this intersection is shown in Figure 3. Table 3. Roadway, Traffic, and Signal Timing Characteristics for University & 17 th Intersection: Camera Location: N-S street: E-W street: Peak Period: University Ave. & 17th Street Southeast corner facing northwest 17 th Street University Ave. 4:00-5:00 PM NB SB WB EB L T R L T R L T R L T R Lanes 1 1 Shrd 1 1 Shrd 1 2 Shrd 1 2 Shrd Posted Speed (mi/h) Peak Hour Volume NEMA Signal Phase Yellow Interval (sec) All-Red Interval (sec) Peak Hour Average Phase and Cycle Lengths (sec) Phase(Ring 1) 1 2 X 4 Total Phase(Ring 2) Cycle Average N (a) (b) Figure 3. Intersection of University Ave. and 34 th St.; (a) street level looking east and (b) aerial. University of Florida, Civil Engineering 9

15 International Speedway Blvd. (ISB) & Williamson Ave. This is an intersection of a major arterial (International Speedway Blvd.) and a minor arterial (Williamson Ave.) in the immediate vicinity of the Daytona International Speedway. International Speedway Blvd. provides access to the greater Daytona Beach area and Interstate- 95, as well as the racetrack. This is a very high volume intersection, has a very large area, and has high approach speeds. Pedestrian and bicycle activity at this intersection is negligible. All site specific characteristics are given in Table 4. A photo of this intersection is shown in Figure 4. Table 4. Roadway, Traffic, and Signal Timing Characteristics for ISB & Williamson Intersection: Camera Location: N-S street: E-W street: Peak Period: International Speedway Blvd. & Williamson Ave. Southeast corner facing northwest Williamson Ave. International Speedway Blvd. 12:00-1:00 PM NB SB WB EB L T R L T R L T R L T R Lanes Posted Speed (mi/h) Peak Hour Volume * Phase Yellow Interval (sec) All-Red Interval (sec) * Right turn bay for this approach was not within camera field of view. Peak Hour Average Phase and Cycle Lengths (sec) Phase (Ring 1) Total Phase (Ring 2) Cycle Average University of Florida, Civil Engineering 10

16 N (a) (b) Figure 4. Intersection of International Speedway Blvd. and Williamson Avenue (a) street level looking northeast and (b) aerial. Clyde Morris Blvd. & Big Tree Road This is an intersection of a major arterial (Clyde Morris Blvd.) and a minor arterial (Big Tree Rd.) in the south-central part of Daytona Beach. Clyde Morris Blvd. serves as primary northsouth artery, providing access to Daytona International Airport and major east-west arterials. Big Tree Rd. primarily serves to provide access to local development. The major street traffic volume is moderate and the minor street traffic is very minimal. This intersection is moderate in area, and has high approach speeds for the major arterial. Pedestrian and bicycle activity at this intersection is negligible. All site specific characteristics are given in Table 5. A photo of this intersection is shown in Figure 5. Table 5. Roadway, Traffic, and Signal Timing Characteristics for Clyde Morris & Big Tree Intersection: Camera Location: N-S street: E-W street: Peak Period: Clyde Morris Blvd. & Big Tree Rd. Southwest corner facing north Clyde Morris Blvd. Big Tree Rd. 5:00-6:00 PM NB SB WB EB L T R L T R L T R L T R Lanes 1 2 Shrd 1 2 Shrd Shrd 1 1 Shrd 1 Shrd Posted Speed (mi/h) Peak Hour Volume Phase 1 6 Yellow Interval (sec) All-Red Interval (sec) NA University of Florida, Civil Engineering 11

17 Peak Hour Average Phase and Cycle Lengths (sec) Phase (Ring 1) 1 2 Total Phase (Ring 2) 5 6 Cycle Average N (a) (b) Figure 5. Intersection of Clyde Morris Blvd. and Big Tree Road; (a) street level looking west and (b) aerial. Data Collection Data Collection Technology To collect red light running data at the selected sites, the red light running analysis package (RLRAP) technology was utilized. This technology was developed at the Transportation Research Center (TRC) of the University of Florida. More details can be found in the final report for a previous STC project [4], an IEEE conference paper [19], as well as Appendices B- E of this report. This technology also allows for the automatic collection of signal phasing and timing data (output to a delimited text file). Collected Data Two to four weeks of data (two to six hours per day, three to five days a week) were collected for each intersection. The data were collected during the months of July 2001, May 2002, November 2002, December 2002, and January Data Reduction Figure 6 shows a screen capture of a video scene obtained from the field. After retrieving the videotape, it is processed using the RLRAP. This involves developing a signal graphic overlay arrangement for each intersection (one-time process). Traffic signal phasing and timing data are automatically recorded in a file during the processing. Each signal phase status graphic University of Florida, Civil Engineering 12

18 (line) was typically placed at the outer edge of the crosswalk. Figure 7 shows a screen capture of a processed videotape that contains the signal graphics overlaid on the traffic video, as well as the signal phasing and timing information. Figure 6: Screen capture from a videotape obtained from the field. Figure 7: Screen capture of a processed videotape. University of Florida, Civil Engineering 13

19 The processed videotapes are reviewed manually to obtain data on red light running events. The red light running data were reduced as shown in Table 6. In this table, Violation time is the time of day at which the red light violation took place, Elapsed time is the time, in seconds, of vehicle entry into the intersection after the onset of the red indication, and Red time is the total all-red time in seconds for that phase. The code definitions for the Vehicle Type, Vehicle Movement, and Vehicle Direction columns are shown in Table 7. Table 6. Example red light violation data reduction coding. VIOL TIME ELAPSED TIME RED TIME VEH TYPE VEH MVMNT VEH DIRECT 9:07: :51: :09: :14: :17: :27: :06: :09: :09: :10: :10: :18: :29: :33: :35: University of Florida, Civil Engineering 14

20 Table 7. Codes used for red light running data reduction. Vehicle Classification Code Type 1 Regular sedan 2 Sports car 3 Pickup truck 4 SUV 5 Mini-van 6 Full size van 7 Bus (School / Transit) 8 Medium size heavy truck (Dump truck / UPS) 9 Semi-truck 10 Motorcycle / Scooter 11 Police / Emergency (Siren lights On / Off) Vehicle Movement (On Red) Code Movement 1 Left turn 2 Thru-left lane 3 Thru-right lane 4 Right turn Vehicle Direction Code Direction 1 Eastbound 2 Westbound 3 Northbound 4 Southbound University of Florida, Civil Engineering 15

21 RESULTS AND ANALYSIS For each site, peak periods were identified as Morning Peak (7:30 AM 9:30 AM), Mid-Day Peak (11:30 AM 1:30 PM) and Afternoon Peak (4:30 PM 6:30 PM). For each peak period of two hours, only one hour was used for the analysis. At the intersection of Clyde Morris Blvd. and Big Tree Road, it was found that red light running was negligible; hence it was not included in the results for the red light running frequency, rate, and severity, as follows. However, there is some discussion later in the paper as to why red light running is virtually non-existent at this intersection. Red Light Running Frequency and Rate Frequency (i.e., the overall number of red light running violations) is one indicator of the extent of the problem; however, the rate of red light running is often a more meaningful measure because it takes into account the number of drivers running red lights relative to the number of the vehicles that actually have the opportunity to run. Thus, even though a smaller intersection will often have a lower frequency of red light running, it could still have a higher rate, which might indicate a more severe problem for that intersection. Three different rate measures were calculated and reported for this study. The first two rate measures are presented in equations 1 and 2. N R 100 RLR Rate(%) = [1] V N R 1, 000 RLR Rate(MEV) = [2] V Where: RLR Rate (%) = Average percentage of hourly volume running red light RLR Rate (TEV) = Average number of red light runners per thousand entering vehicles N R = Average number of red light runners in analysis hour V = Average analysis hour volume The third rate is simply the average number of red light running vehicles over the average number of cycles during the analysis period, as calculated by equation 3. Where: Avg = N R. RLRs per Cycle [3] N C N R = Average number of red light runners in analysis hour, and N C = Average number of cycles in analysis hour. University of Florida, Civil Engineering 16

22 The number of red light running violations per hour was also determined from the data set. This statistic is somewhat meaningless, however, as red light running is highly correlated with the number of yellow and red intervals per hour (which is a function of cycle length), and the number of cycles per hour can vary greatly from one site to another. The only reason for including this statistic was just for comparison purposes, as some other studies on red light running have reported this measure. The following tables present the results for the rates of red light running for the three peak periods. Table 8. Red light running violation statistics for the morning peak period. RLR Rate (%) RLR Rate (TEV) Number of RLRs / Cycle Number of RLRs / Hour International Speedway Blvd. & Williamson Ave. EB 0.13% WB 0.11% NB 0.28% SB < 0.1% < < 0.01 < University Ave. & 34th Street EB 0.55% WB 0.44% NB 0.41% SB 0.34% Archer Rd. & 34 th Street EB 0.34% WB < 0.1% < < 0.01 < NB 0.28% SB 0.39% University of Florida, Civil Engineering 17

23 Table 9. Red light running violation statistics for the mid-day peak period. RLR Rate (%) RLR Rate (TEV) Number of RLRs / Cycle Number of RLRs / Hour International Speedway Blvd. & Williamson Ave. EB 0.26% WB 0.11% NB 0.22% SB 0.96% University Ave. & 34th Street EB 0.33% WB 0.65% NB 0.48% SB 0.21% Archer Rd. & 34th Street EB 0.83% WB 0.31% NB 0.97% SB 0.59% Table 10. Red light running violation statistics for the afternoon peak period. RLR Rate (%) RLR Rate (TEV) Number of RLRs / Cycle Number of RLRs / Hour International Speedway Blvd. & Williamson Ave. EB 0.30% WB < 0.1% < < 0.01 < NB 0.53% SB 0.29% University Ave. & 34th Street EB 0.45% WB 0.51% NB 0.47% SB 0.19% University Ave. & 17th Street EB 0.16% WB 0.27% NB 0.40% SB 0.45% Archer Rd. & 34th Street EB 1.34% WB 0.16% NB 1.34% SB 0.96% University of Florida, Civil Engineering 18

24 Table 11. Red light running rates by movement type and proportion of all violations by movement type; (a) morning peak period, (b) mid-day peak period, and (c) afternoon peak period RLR Rate % of RLRs ISB & Williamson Left 0.30% 42.86% Through 0.08% 57.14% University Ave. & 34 th Street Left 1.31% 44.23% Through 0.29% 55.77% Archer Rd. & 34 th Street Left 0.64% 51.85% Through 0.18% 48.15% (a) RLR Rate % of RLRs ISB & Williamson Ave. Left 0.95% 75.34% Through 0.08% 24.66% University Ave. & 34th Street Left 0.82% 30.49% Through 0.41% 69.51% Archer Rd. & 34th Street Left 1.91% 83.12% Through 0.15% 16.88% (b) RLR Rate % of RLRs ISB & Williamson Ave. Left 0.74% 83.33% Through 0.04% 16.67% University Ave. & 34th Street Left 1.02% 48.21% Through 0.27% 51.79% University Ave. & 17th Street Left 0.46% 23.44% Through 0.21% 76.56% Archer Rd. & 34th Street Left 2.24% 78.72% Through 0.23% 21.28% (c) University of Florida, Civil Engineering 19

25 Rate by Intersection and Time of Day Archer and 34 th has the highest rates of red light running, ranging from violations per cycle ( violations per hour) across the three peak periods. University and 17 th and ISB and Williamson generally have the lowest rates of red light running. The red light violation rates were generally higher for the mid-day and afternoon peak periods than for the morning peak period. This essentially correlates with the exposure factors of traffic volume and number of cycles. More detailed discussion on intersection specific violation rates is presented under the Site Specific Issues section. Rate and Frequency by Movement Type Overall, the rate of red light running is considerably higher for left turn movements than for through movements. The frequency of red light running is generally higher for the through movements, which is expected due to the higher through volumes. There are a couple of exceptions, which will be discussed later under the Site Specific Issues section. The much higher rate for red light running by left turning vehicles may be due to a couple of factors. One is the yellow interval timing, and this issue is discussed further for the Archer and 34 th intersection under the Site Specific Issues section. Another reason may be the pack mentality. Left turning vehicles are usually traveling slower than the adjacent through vehicles, and consequently these drivers are more comfortable closing the gap (i.e., bunching up) with the vehicle in front of them. These drivers may feel that as long as they are part of the pack (even though they are at the back of the pack and personally did not enter the intersection before the onset of red) it is socially acceptable to run the red in this situation. These people may also feel that they are less likely to be involved in an accident for this type of situation versus running the red light when traveling straight through the intersection at a higher rate of speed and with a larger gap to the previously entering vehicle. It was commonly observed from the videos that left turning vehicles arriving at the very end of the yellow interval would speed up and try to join the pack of slower moving vehicles (due to turning) that had already entered the intersection on green or yellow. Rate by Vehicle Type In order to gain insight as to whether drivers of certain vehicle types may be more prone to running red lights, the red light running rate was calculated by vehicle type. It simply divides the number of red light runners of a particular vehicle type by the total number of the vehicles passing through the intersection of the same vehicle type. The following figures present these rates for the intersections of ISB and Williamson and University and 34 th. University of Florida, Civil Engineering 20

26 Figure 8. Red light running rate by vehicle type at ISB and Williamson. Figure 9. Red light running rate by vehicle type at University and 34 th. University of Florida, Civil Engineering 21

27 The small passenger vehicle category consisted of vehicle classifications 1 and 2 (from Table 7), and the large passenger vehicle category consisted of vehicle classifications 3-6. At these intersections, drivers of semi-trucks and motorcycles/scooters are more likely to run a red light than the drivers of other vehicle classifications. This is not a surprising result for semi-trucks, given the heavy loads, poor braking capabilities, and schedule pressures. They are probably even more likely to run a red light on a downgrade approach (the intersections for the above two graphs are very flat). In general, motorcycle riders can be considered to be more willing to take risks; thus, a likely greater propensity to run red lights. Scooter riders are almost always younger people, whom overall probably have less respect for traffic control devices than older drivers. Red Light Running Severity Another indicator of the significance of red light running at an intersection is the average time after the onset of the red indication for which vehicles enter the intersection. Obviously, the later a vehicle enters the intersection after the red, the more likely a conflicting traffic movement will be moving through the intersection, and the more likely a collision is to occur. This measure is referred to as severity in this report, and is measured in seconds (i.e., number of seconds after which a vehicle enters the intersection after the red signal indication has been displayed for its movement). The average, maximum, and standard deviation values of this measure, aggregated across all three peak periods, are presented in the following tables. The minimum values are not presented as they are not very meaningful (i.e., many vehicles run in the first couple of tenths of a second after the light turns red). Table 12 presents these measures by type of movement and Table 13 presents these measures by movement direction. Table 12. Severity statistics by type of movement. Time of Entry After Onset of Red (seconds) Site Movement Average Maximum Standard Dev. ISB and Williamson LEFT THRU University and 34th LEFT THRU Archer and 34th LEFT THRU University and 17th LEFT THRU University of Florida, Civil Engineering 22

28 Table 13. Severity statistics by movement direction. Time of Entry After Onset of Red (seconds) Site Movement Average Maximum Standard Dev. ISB and Williamson EB WB NB SB University and 34th EB WB NB SB Archer and 34th EB WB NB SB University and 17th EB WB NB SB Consistent with the earlier findings on rates, Archer and 34 th also has the highest entry severity, on average. All average values, however, are below one second, which indicates that, overall there are not large percentages of red light runners entering the intersection well after the onset of the red indication. Left turning vehicles had considerably higher average severity values at each of the intersections. This may be an indication that drivers who violate are generally more comfortable running the red light for a left turn movement than for a through movement. The most severe (by the time definition) red light running was done by left turning drivers that treated a red indication for a protected left turn movement as a permitted movement, either deliberately or mistakenly. This type of violation occurred relatively infrequently, and was more common at the small intersections. These violations were not included in the severity statistics of the above tables. Vehicles running the red light by a second or less are usually not a safety concern, since start-up delays for conflicting movements usually still provide a margin of safety. The very late red light runners are the major safety problem. However, any red light running (even if just barely) should be enforced, as this potentially leads to more severe violators. If drivers know that running by a second is OK, they may start pushing it to two seconds or more. A more complete picture of red light running severity is provided by a frequency graph (also called a histogram) of the time of entry after the onset of red. Figures present these frequency graphs for four intersections and two movement types. University of Florida, Civil Engineering 23

29 Frequency More Entry after red (sec) Figure 10. Red light running severity, left turns (ISB & Williamson). Frequency More Entry after red (sec) Figure 11. Red light running severity, through movements (ISB & Williamson). University of Florida, Civil Engineering 24

30 30 25 Frequency More Entry after red (sec) Figure 12. Red light running severity, left turns (University and 34 th ). Frequency More Entry after red (sec) Figure 13. Red light running severity, through movements (University and 34 th ). University of Florida, Civil Engineering 25

31 Frequency More Entry after red (sec) Figure 14. Red light running severity, left turns (Archer and 34 th ). Frequency More Entry after red (sec) Figure 15. Red light running severity, through movements (Archer and 34 th ). University of Florida, Civil Engineering 26

32 Frequency More Entry after red (sec) Figure 16. Red light running severity, left turns (University and 17 th ) Frequency More Entry after red (sec) Figure 17. Red light running severity, through movements (University and 17 th ). As expected, these graphs generally follow a negative exponential distribution. The bulk of the violations occur between 0.5 and 2.0 seconds after the onset of red, and entries more than 3 seconds after the red are rare. University of Florida, Civil Engineering 27

33 Comparison of Yellow and All-Red Intervals Yellow and all-red interval times at all the intersections were compared to the values calculated with the Institute of Transportation Engineers (ITE) method and the Florida Department of Transportation (FDOT) guidelines. The Institute of Transportation Engineers [20] recommends that yellow and all-red intervals be determined by the following equations. V Y = t r + 2a + 2gG [4] Where: Y = yellow time in seconds, t r = driver perception/reaction time, usually taken as 1.0 second, V = speed of approaching traffic in ft/s, a = deceleration rate for the vehicle, usually taken as 10.0 ft/s 2 g = acceleration due to gravity (32.2 ft/s 2 ), and G = percent grade divided by 100. AR = w + l V [5] Where: AR = all-red time in seconds, w = width of the cross street in ft, l = length of the vehicle, usually taken as 20 ft, and V = speed of approaching traffic in ft/s. The ITE calculated timings (yellow plus all-red times) are intended to avoid a dilemma zone and the possibility of a vehicle being in the intersection when a conflicting movement receives a green signal indication. Note that the speed (V) used in equations 4 and 5 was the posted limit plus 5 mi/h. The Florida Department of Transportation publishes their own guidelines of recommended yellow and all-red interval times [21]. Their recommended yellow interval times are based on equation 4, with values sometimes rounded up slightly. FDOT recommends using the higher of the posted speed limit or 85 th percentile speed for the approach speed. The yellow interval guidelines are shown in Table 14, along with the ITE calculated values for comparison purposes. FDOT recommends an all-red interval of 1 second for approach speeds up to 50 mi/h, and 2 seconds for approach speeds above 50 mi/h. University of Florida, Civil Engineering 28