Assessment of Red Light Running Cameras in Fairfax County, Virginia

Assessment of Red Light Running Cameras in Fairfax County, Virginia A study was conducted to assess the Red Light Running (RLR) cameras in Fairfax County, Virginia. The RLR camera enforcement program involved cameras that were installed around the county. Intersections were chosen based on police and community recommendations in high commuter traffic areas. Warning signs were also erected upstream from the monitored intersections. All cameras utilized the same basic setup. The typical setup included a dual loop system for detecting the vehicles entering the intersection and calculating their speed. Once the camera system detected a violator, it recorded two pictures from the rear of the vehicle, showing it enter and proceed through the intersection. These pictures were used to identify the registered owner by the license plate and give evidence for a citation. The automated detection system is designed for the through movements of traffic. Violation data, accident data, and traffic data were collected from the Virginia Department of Transportation, the Fairfax County Department of Transportation, and the Fairfax County Police Department. The data analysis has identified improvements in violation rates on the order of 36% over the first three months of automated enforcement and 69% reductions after six months of camera operation. The accident rates data also showed a reduction of 4% in accidents. This reduction in accidents, accounting for expenses of implementation and operation of the system, could lead to a benefit to the community of approximately $8.75 million over the next eight years. Public opinion in the Fairfax County jurisdiction was found to be strongly supportive of the automated enforcement. The results found in this study show that the implementation of red light running cameras in Fairfax County, Virginia, have been successful in providing benefits with respect to traffic, economy, and safety. by Daniel E. Ruby and Antoine G. Hobeika Red light running (RLR) cameras can provide a safe and consistent method of enforcing the traffic law as a means of reducing accidents, property loss, injury and death. These cameras, while controversial, utihze a relatively inexpensive technology to improve the safety at a dangerous place on the road, the signalized intersection, accounting for an average of 75 fatal accidents yearly.' Red light cameras are a relatively recent addition to the traffic law enforcement repertoire. They were first used in Victoria, Australia, in 983.-Their first implementation in the US first came in 993 in New York City.' Currently, the camera systems are used in states and the District of Columbia, including a total of at least 63 jurisdictions as of September 2.' As the technology continues to be refined and proven effective, and as long as public opinion continues to support their use, utilization of RLR cameras will likely continue to grow. Transportation Quarterly, Vol. 57, No.3, Summer 23 (3.5^8 23 Eno Transportation FoLindation, Inc., Washington, DC. 33

TRANSPORTATION QUARTERLY/SUMMER 23 Traditional Enforcement In 998, 986 deaths were attributed to red light running accidents in the United States.' Red light running accidents also contribute disproportionally to injury accidents, as Richard Retting, senior transportation engineer with the Insurance Institute for Highway Safety, states: "Occupant injuries occurred in 45 percent of the red-light running crashes studied compared with 3 percent for other crash types. One reason for high injury rates in this type of crash is that these collisions often involve side impacts at relatively high speeds, which can result in passenger compartment intrusion."^ This significant concern presents a substantial problem to solve by means of manual enforcement. Red light running crackdowns by law enforcement can use two officers working in concert. One is stationed inconspicuously at the intersection witnessing the violations and contacting another officer located on the downstream side of the intersection to stop the violator and issue the citation. This method is both manpower intensive and difficult to implement at many intersections due to geometric configurations and lack of safe places for either officer.' The Howard County, Maryland Police Department has calculated the personnel expense to the department for each red-light running citation issued is $25.4 using this procedure.^ Methods using only one officer would require him/her to be located at a place near the intersection where the signal can be clearly seen, oncoming traffic is not aware of his/her presence, and the officer would have to run the red light after the violator in order to cite the wrongdoer.' With these difficulties in mind, the simplicity and safety involved in the camera system make it a promising resolution. How a Red-Light Camera System Works While there are a number of variations in red light running camera systems, the basic configuration is similar. When the traffic signal has begun a red phase of the direction that is being monitored, a series of detection devices are activated.'" As a vehicle approaches an intersection, it passes over an induction loop that detects the vehicle's presence." A short distance later, at the stop bar of the intersection, the vehicle passes over a second induction loop.'^ By calculating the time that passes while traveling between the two, a vehicle speed is calculated. If the vehicle crosses the stop line during a red phase above a pre-set speed (typically 5 mph) during the red phase, the system determines that the vehicle is in violation." A grace period of a fraction of a second is programmed into the system to minimize unwarranted citations.''' Simpler, one-loop systems have also been installed, though with some complications. This one-loop system is placed at the stopline and activates the system when any vehicle crosses the loop after the signal has begun the red phase for the approach." Because this system doesn't take into account the speed of the vehicle in question, often vehicles making a right turn on red or emergency vehicles trigger the photo enforcement when undesired.'^ A camera, mounted atop a pole overlooking the intersection, will then take a series of pictures (typically two or three) capturing an image of the vehicle in the intersection with the red phase visible, another clearly showing the license plate" and, in some installments, a picture of the driver. The camera imprints data such as the date, time, vehicle speed, and time elapsed since the beginning of the red phase on the images of the vehicle in violation.'^ These images are processed, sometimes by the manufacturer of the camera system, and then given to the police for the issuance of a citation if it is deemed

RED LIGHT RUNNING CAMERAS appropriate. The ticket is mailed to the person to whom the vehicle is registered.'^ As with any other traffic violation, the penalty may either be paid or appealed to a court of law. Fairfax County, Virginia The study was conducted in Fairfax County, Virginia, a Washington, DC suburb, that is 399 square miles with approximately 965, residents. Virginia state law enacted in 995 allows for local authorities to use automated enforcement to battle red light runners. Implementation followed that of camera systems in surrounding jurisdictions including Fairfax City, Arlington, Alexandria, Vienna, and the District of Columbia. The Fairfax County cameras utilize rear photography of the license plate and do not photograph the driver. Upon initial review by the vendor. Affiliated Computer Systems, and then the county police department, if the apparent violator is deemed guilty, a $5 citation (with no points) is issued to the vehicle's registered owner; otherwise, the event is rejected. If this person is not the driver of the vehicle at the time of the violation, the owner can simply appeal the ticket and sign an affidavit testifying to that point thus avoiding the penalty. The RLR camera enforcement began with the installation of one camera that was activated October, 2. Intersections were chosen based on police and community recommendations in high commuter traffic areas. No warning period was provided due to the exposure of the public to similar camera systems in adjacent areas and a thorough awareness campaign. The process of informing constituents included creating a detailed website, providing news releases, local television and radio interviews and literature distribution at public buildings with the cooperation and endorsement of AAA. Warning signs were also erected upstream from the monitored intersections. Two additionai cameras were activated on February 9,2, and since that time, seven additional cameras have been put into use, the most recent was activated October 6, 2, for a total of cameras actively enforcing RLR violations across the county. All cameras utilize the same basic setup. The typical setup includes a dual loop system for detecting the vehicles entering the intersection and calculating their speed. Minimum thresholds were established as 8-mph for roads with speed limits of 45-mph or less and 2-mph thresholds for roadways with higher speed limits. In addition, the first.2 seconds of the red phase is given as a grace period where the camera will not activate. Once the camera system detects a violator, it records two pictures from the rear of the vehicle, showing it enter and proceed through the intersection. These pictures are used to identify the registered owner by the license plate and give evidence for a citation. The cameras use 35mm film and are mounted on the top of a pole exclusively for the camera. Additional flashes are mounted at the intersection for night and poor weather conditions. Fach intersection was monitored for one direction only. The automated detection system is designed for the through movements. The number of lanes under surveillance varied between intersections. Data collection was limited to that which was held by Virginia Department of Transportation (responsible for the roadways and signals), the Fairfax County Department of Transportation (responsible for the RLR camera system), and the Fairfax County Police Department (collectors of accident data). Because no thorough data was collected on RLR violations prior to installation of cameras, the only violation data was that which was collected by the cameras themselves from activation through September 3, 2. Accident data through May 2 was available from the county police. The available data included average daily 35

TRANSPORTATION QUARTERLY/SUMMER 23 traffic (ADT) counts from nearby, permanent loop detectors. This information, combined with montbly accident reports and violation data, provides tbe basis for the effectiveness study. Geometric design for the intersections was collected as well as amber and all-red phase times. These variables were also included for correlation to camera effectiveness. Violation Data Violation data was examined, first on an intersection-by-intersection basis and then all nine intersections from which data was collected as a whole. The camera installed on October 6, 2 had no available violation data at the time of the study. By plotting the violation rates versus time and running a regression analysis to find the percentage of change in violations, the effect of the cameras was observed. Additionally, violations per, vehicles were plotted for analysis. Because of the lack of violation data prior to the activation of the cameras, it was assumed that the first month of use for each intersection represents statistics equivalent to "before" data. The following months demonstrated the effects of the cameras on violation rates. The accident data, on the other hand, were available from dates prior to camera operation and did provide "before" data, while it did not offer much data after the installation of the camera systems. Because of this information, it was possible to analyze the effects of the cameras at various monthly intervals after operation. The results of the intersections are shown in Table and total results have been calculated from the aggregation of each intersection. Total effects of the cameras have been grouped into two categories: three months after installation and six months after installation. "Initial" represents the first month of activation of the camera that is in all cases except for intersection been interpolated to Table : Violation Data for All Monitored Intersections Intersection Number of Violations Per Month (ADT) Dally Traffic violations/, Vehicles** % Change Violations/, Vehicles Initial 3 mo. after 6 mo. after Initial 3 mo. after 6 mo. after 3 mo. after 6 mo. after 393 372 53, 2.47 2.33.43-5.4% -42.% 2 373 93 78 6, 2.7.7.43-48.4% -79.% 3 36 3 3 B, 2..7.7-9.7% -9.7% 4* 48 74 66, 2.43,87-64.4% 5 795 54 24, n.o 7. -36,4% 6 552 382 58, 3.7 2,2-3.5% 7 53 56 5, 353 3.37-4,7% 8 84 47, 5.96 9 65 52, 3.87 Totals 4.93 2,62 336 47, 26.67/ 6.54" 7 2.3-36.2% -68.9% 'indicates the sum of initial violations that have violations data after 3-month and 6-month, respectively *' shows the via lotions/ vehicles after converting the daily traffic to a monthly traffic 36

RED LIGHT RUNNING CAMERAS represent a full month. The category of "3 months after" represents data collected three months after the activation of the individual camera, not the first camera in the county. This approach also applies to the "6 months after" category. Percent reduction is based on the intersections that had applicable data and is strictly the percentage change in violations per, vehicles as compared between the initial month and the later month. These values are not the results of a regression analysis. Intersection 4 data (marked with *), because of what appears to be aberrant results, considers the third month of activation to be the initial month and "3 months after" is from three months after that newly established initial month. The daily traffic volumes (ADT) were converted to a monthly volume by multiplying ADT by 3 in order to determine the rate of violations per, vehicles. The seven intersections that had data available for three months after implementation showed an improvement in violations of 36.2%. Of the three intersections that had data available for six months after camera activation, there was a 68.9% reduction in violations. Accident Data Accident data was used as control data because it represented statistics from intersections before automated enforcement was present. It had been broken down into three categories, according to severity, by the police department. The categories are property damage only (PDO), injury, and fatality. Also, the data provided by the Fairfax County Police Department was subcategorizcd into "at" (accidents within 5 feet of the intersection) and "near" (accidents considered at the intersection, but more than 5 feet from them). Because of the nature of accidents caused by red-light runners, only the "at" data was considered relevant for the purposes of this study. Accident data was collected for all intersections from January 2 to May 2. January 2 marks the first month of activation of the first camera. For the purposes of the study, the months have been numbered from to 7. Because of the time frames of the accident data collection and the activation of RLR cameras, the data logically fell into three categories. The first category is limited to the Leesburg Pike and Towleston Road intersection where the accident data was not available prior to implementation of the RLR camera. But it was available after the first month of activation. Therefore, this intersection has only "after" data. The second category of accident data is from three intersections that have both "before" and "after" data, even if the data following camera enforcement is short term. These are the three intersections that were activated between February and March 2 and have 4-5 months of "before" data and three to four months of "after" data. Changes in accident rates are most clearly evident in these data sets. The third group of data involves intersections that have only accidents prior to camera installation, thus providing control data for the study. Therefore, the second group of data evinces the potential change that can come to an intersection upon camera installation as an improvement from the control levels shown by the third group of intersections. Following the establishment of expected changes in accident rates, the financial benefits or costs can be predicted for additional intersections. The accident data can be simply broken down into two categories; intersections with and intersections without data after camera operation. Four intersections, through 4, have data recorded after camera implementation, while intersections 5 through 9 have only control data. Because there is no ADT information available regarding intersection, accident rates from this intersection camera cannot be determined and thus cannot be 37

TRANSPORTATION QUARTERLY/ SUMMER 23 included in this study. Using the reduction in accidents at the first four intersections and the control data from the other five locations, a baseline can be established and a projected change in accident rates can be predicted. Of the sites that have post-camera installation data, only one has more than three months of data; therefore, the effects are limited to those observed at three months after implementation. Of the accidents occurring in this time span at these intersections, none were fatal. Because of the small amount of data available, it will be assumed that the reduction in total accidents observed will affect each of the three categories of accident at the same rate. Control data for accident rates in table 2 shows the distribution of accident types as observed at intersections 5 through 9 over a 7-month period prior to camera activation. The total ADT for these 5 intersections is 23,. The average daily traffic volume {ADT) for each camera is converted to a 7-month traffic volume; and consequently accidents per, vehicles are determined. The first four intersections together provide an indication of accident reduction as a result of RLR automated enforcement implementation. The results show a 4% reduction in accidents after three months of camera operation. While these are preliminary results, the data will serve as an estimate of results to be found at each of the intersections. Future study of a greater number of intersections for a greater amount of time is required to be fully confident in these preliminary findings. Table 2: Control Data for Accident Rates Control Intersections Intersection 5 PDO 9 Injury a Fatal ADT 24, Total 27 Intersection 6 4 2 58, 26 Intersection 7 2 7 5, 9 intersection 8 2 8 47, 39 intersection 9 2 52, 24 Totais 67 55 3 23, 25 % Total Accidents 53.6% 44.% 2.4%,% Accidents/, veh..57.47.25.6 Table 3: Change in Number of Accidents Variable Intersections Initial mo. after 2 mo. after 3 mo. after % Reduction intersection 3 intersection 2 Intersection 3 Intersection 4 2 Totals 5 2 2 3-4.%

RED LIGHT RUNNING CAMERAS By applying these results to the traffic flowing through the nine intersections that have ADT data and applying values for the costs of accidents of various severities, the benefit of the cameras can be enumerated. Cost Benefit Analysis The costs and benefits of the automated enforcement program in Fairfax County can be estabhshed through an analysis of the reduction in accidents of various severities when given appropriate values. Expenses for accidents are derived from the National Highway Traffic Safety Administration's publication, The Economic Cost of Motor Vehicle Crashes, 994. The NHTSA values include productivity losses, property damage, medical costs, rehabilitation costs, travel delay, legal and court costs, emergency service costs, insurance administration costs, premature funeral costs, and costs to employers. The values for injury accidents are broken down intofivecategories according to the maximum abbreviated injury scale (MAIS), but have been averaged using weighted values for frequency of the accident types. The accident costs are shown in Table 4. An interest rate of 7% was used for all monetary conversions to provide crash costs in year 2 dollars. The life cycle was considered to be eight years. ADT data was extrapolated from year 2 data. Traffic was assumed to grow at an annual rate of 3 %. The ADT data, and the average yearly traffic are shown in Table 5. Lamera Costs The costs incurred by the county are reportedly $7 million over thefirstthree years of the eight-year life cycle. Assuming an extension of funding for those areas that would require it beyond thefirstthree years, these values are included and assumed to increase by 7% each year. Of the expenses, approximately onethird is attributed to personnel expenses. These would be present for all eight years, changing only with the interest rate. Another one-third of the funding is for equipment. Assuming that this is equal for the intersections, one-tenth of the money is spent in the year 2, and the rest in the year 2. Because the accident rate and reduction were unavailable for intersection, its equipment cost has not been included in this cost analysis. Thefinalone-third of the funding is made in payments to the vendors for their services of collecting and developing the film for the cameras. The cost breakdowns are shown in Table 6. The costs are converted to the 2 dollars by assuming an interest rate of 7%. The Vendors costs and the personnel costs are also assumed to grow at 7%. Accident Reduction Savings The accident rates, as reported previously in Table 2, were found to be.57 PDO Table 4: Costs of Accidents Costs per Accident (in yr. 2 SI Prop Damage Only Injury Accidents Fatal Accidents $ 2,496 $ 7,55 $,248,486 Table 5: ADT and Yearly Traffic Data for the Eight-year Life Cycle Year 2 2 22 23 24 25 26 27 ADT 455,9 47, 484, 498,623 53,582 528,989 544,858 56,23 Average Yearly Traffic 66,43,5 7,55, 76,696,5 8,997,395 87,457,36 93,8,36 98,873,467 24,839,67 39

TRANSPORTATION QUARTERLY/SUMMER 23 accidents per, vehicles,.47 injury accidents per, vehicles and.25 fatality accidents per, vehicles. Also, it was found that accidents were reduced by 4% after camera activation. Thus, the new rates become.34,.28 and.5, respectively. The results of accident savings are shown in Tables 7, 8 and 9. With the costs and savings computed, the projected savings of the camera implementation can be determined. The sum of the savings from reduction of accidents is found to be nearly $24. million. The expense of the automated enforcement was found to be roughly $5.4 million. Therefore, the savings is projected at approximately $8.75 million over the eight-year life cycle of the system. Table 6: Costs Incurred by the County for Camera Enforcement Expenses Year 2 2 22 23 24 25 26 27 Totalin2$ Personnel $726,894 $777,777 $832,22 $89,477 $952,8 $,9,57 $,9,872 $,67,234 $6,222,26 Equipment $28,68 $,866,666 $ $ $ $ $ $2,33,88 Vendor $42,793 $945,84 $,2,49 $,82,892 $,58,695 $,239,83 $,326,59 $,49,45 $8,598,2 Grand Total $5,48,592 (in 2) Discount Factor at 7%.7..935.873.86.763.73.666 Table 7: Projected PDO Accidents and Savings per Year Year 2 2 22 23 24 25 26 27 Yearly Traffic Count 66,43,5 7,55, 76,696,5 8,997,395 87,457,36 93,8,36 98,873.467 24,839,67 Totals Projected PDO 94.8 97.7.7 3.7 6.8. 3,3 6.7 843.7 Projected PDO w/ Camera Reduction 56,5 58,2 6. 6.8 63.7 65.6 67.6 69.6 53. Value per Accident Savings PDO Accidents Prevented 38,3 39.5 4.7 4.9 43. 44,4 45.7 47. 34.7 $2,496 $85,387

REO LIGHT RUNNING CAMERAS Table 8: Projected Injury Accidents and Savings per Year Year Yearly Traffic Count Projected Injury Projected Injury w/ Camera Reduction Injury Accident Prevented 2 66,43,5 78.2 46.6 3,6 2 7,55, 8.6 48. 32.6 22 76,696,5 83 49.4 33.6 23 8,997,395 85.5 5.9 34.6 24 87,457,36 88. 52.4 35.6 25 93,8,36 9.6 54. 36.6 26 98,873,467 93.3 55.6 377 27 24,839.67 96. 57.2 38.8 Totals 695.2 44. 28. Value per Accident SI 7.55 Savings $5,798,285 Table 9: Projected Fatal Accidents and Savings per Year Year Yearly Traffic Count Projected Fatality Projected Fatality vu/ Camera Reduction Fatal Accidents Prevented 2 66,43,5 4.6 2.5.66 2 7,55, 4.28 2.57.7 22 76,696,5 4.4 2.64.75 23 8,997.395 4.53 2.72.8 24 87.457,36 4.66 2.8.86 25 93,8,36 4.8 2.88.92 26 98,873,467 4.94 2.96.97 27 24,839,67 5.8 3.5 2,3 Totals 36.85 22.2 4.73 Value per Accident $,248.486 Savings 8,39,98 4

TRANSPORTATION QUARTERLY/SUMMER 23 Table : Total Savings and Expense Summary Accident Type # Accidents Saved Cost/ Accident Cost Savings PDO Savings 34.7 S2,496 $85,387 Injury Savings 28. $7,55 $4,933,586 Fatality Savings 4.73 $,248.486 $8,48,592 Total Accident Savings 685. $24,74,7 Total Expense -$5,48,592 Overall Savings $8,755,579 Comparison to Other Jurisdictions Using the data documented earlier in this study, the results from Fairfax County can be compared with those found in other jurisdictions to see how this RLR camera system fared. Two categories have been displayed for Fairfax County because the six-month data is based on only three intersections, while the three-month data is for a more statistically substantial seven intersections. The results are shovi^n in Table. The violation reduction results found in Fairfax County appear to be in line with results from other jurisdictions. While the three-month data is more reliable because of the number of intersections involved, it is also fair to assume that the full effect of the cameras have not been realized in that amount of time. Thus, the 36% improvement is on the low end of the scale of results found in other locations. While the 69%., six-month improvement may be less accurate, it would still be within the range found elsewhere, namely Charlotte. These results would indicate that Fairfax County has seen violation reductions as well as any other jurisdiction that was documented and has reason to claim that the project is successful in reducing violations. Accident rate changes are not as widely available and, therefore, there are fewer jurisdictions to compare the results of Fairfax County. What results is available show that Fairfax County has had greater success in reducing accidents at intersections with RLR cameras. Fairfax County appears to have achieved success in reducing accidents, the original reason for implementing RLR cameras in the first place. The results can also be analyzed by variable, namely ADT and speed limit/camera speed tolerance. Of the intersections analyzed in this study, seven had at least three months of violation data to compare the change in violations. Average Daily Traffic For the examination of the effect of ADT on RLR camera results, the intersections were grouped into two classifications of "high" and "low" ADT that have recorded violations after installation. The high ADT group includes intersections 2, 3, 4, and 6 that all have an average of at least 58, vehicles per day. The remaining three intersections have 53, or fewer vehicles per day. The results, shown in Table 2, indicate a greater success of cameras at the high ADT sites with a three-month improvement of 55.5% and a six-month improvement of 85.2% in violations. The lower ADT sites experienced less dramatic improvements, where threemonth improvements were shown to be 25.29% and a six-month improvement of 42.%.

RED LIGHT RUNNING CAMERAS Table : Comparative Results of RLR Camera Effects Jurisdiction Violation Reduction Accident Reduction Charlotte, NC Fairfax (City), VA Howard Co,, MD New York, NY Oxnard, CA San Francisco, CA United Kingdom Singapore Victoria, Australia -7% -44% -23% -34% -42% -42% -55% -4% -32% -29% -22% 3 mo. after 6 mo. after Fairfax County -36% -69% -4% Table 2: Effects of ADT on Violations Intersection Number of Violations Per Month ADT Vielations/, Vehicles** % Change Violations/, Vehicles Initial 3 mo. after 6 mo. after Initial 3 mo. after 6 mo. after 3 mo. after 6 mo. after High ADT 4 48 74 66, 2.43.87-64,4% 2 373 93 78 6. 2.7.7.43-48.4% -79.% 3 36 3 3 6, 2..7.7-9,7% -9,7% 6 552 382 58. 3.7 2.2-3.5% Total,766 78 9 244, 9.67 4,3.6-55.5% -85.2% Low ADT 393 372 227 53. 2.47 2.33.43-5.4% -42.% 7 53 56 5, 3.53 3.37 4,7% 5 795 54 24, n.o 7. -36,4% Total,79,382 227 27. 7. 2.7.43-25.29% -42.% "converted to monxny traffic volumes to determine the violations/i, vehicles 43

TRANSPORTATION QUARTERLY / SUMMER 23 Speed Limit and Tolerances The second variable that can be isolated to determine the effects of cameras is that of speed limit and, in turn, speed tolerances of the cameras. The county established the breakpoint in the speed limit for differentiation of speed tolerance to be 5 mph. Any roads with cameras that meet or exceed that speed have tolerances of 2 mph. In other words, a vehicle must be traveling at least 2 mph when entering the intersection to trigger the camera. Those intersections whose monitored direction having speed limits less than 5 mph have a speed tolerance of 8 mph. Therefore, examining the results by the speed tolerances may provide information about where the cameras are most effective and may aid in determining future camera locations. In this study there were two intersections with 2 mph speed tolerances, intersections and 7. There are also three intersections with 45 mph speed limits, and thus 8 mph tolerances, intersections 2, 3 and 6. The final two intersections that had at least three months of violation data have speed limits of 35 mph and 8 mph tolerances. As shown in Table 3, it would appear that cameras have a greater impact on intersections with lower speed limits and where there are lower speed thresholds. The highspeed intersections witnessed a 5.% decrease in violations after three months and a 42. % reduction after six months of camera operation. The medium-speed intersections experienced a three-month decrease of 52.48% and a six-month decrease of 85.2%. While the high-speed intersections were shown to be successful after the first six Table 3: Effects of Speed Limits/Speed Tolerances on Violations Intersection Number of Violations ADT Violations/, Vehicles** % Change Violations/, Vehicles Initial 3 mo. after 6 mo. after Initial 3 mo. after 6 mo. after 3 mo. after 6 mo. after 55 mph Speed Limit and 2 mph Camera Threshold 393 372 227 53, 2.47 2.33.433-5.4% -42.% 7 53 56 5, 3.53 3.37-4.7% Total 924 878 227 3, 6. 5.7.433-5.% -^2.% 45 mph Speed Limit and 8 mph Camera Threshold 2 373 93 78 6, 2.7.7.43-48.4% -79.% 3 36 3 3 6, 2..7.7-9.7% -9.7% 6 552 382 58, 3.7 2.2-3.5% Total,285 66 9 78, 7.24 3.44.6-52.48% -85.2% 35 mph Speed Limit and 8 mph Camera Threshold 4* 48 74 66, 2.43.87-64.4% 5 795 54 24,. 7- -36.4% Total,276 678-9, 2.43 7.87 - -36.68% - 44

RED LIGHT RUNNING CAMERAS months of camera use, the medium to lowspeed intersections appear to have even more successful. As seen in the previous tables, even though there appeared to be some measure of success at all intersections, variables can have impacts on these results. Roadways with higher ADT appear to exhibit greater effectiveness of RLR cameras. Also, roadways with lower speed limits and the accompanying lower speed tolerances appear to have a greater reduction in violations. Public Opinion The importance of community support for programs such as RLR cameras cannot be underestimated. That is why Fairfax County undertook a survey of public opinion of its residents. The survey was done prior to camera installation and polled 4 residents of at least 8 years of age. The questionnaire was comprised of 4 questions used to gauge the public's knowledge of RLR cameras, favorable versus unfavorable opinion of the cameras, and demographics of the responders. Selected questions and responses are shown in the following tables: Question "A" Fairfax County will be using a new technology called photo traffic management. It works by a computer detecting if a motorist enters the intersection after the light turns red. The computer triggers a camera mounted on a pole at the intersection, which then photographs the rear of the vehicle, to include the license plate. The registered owner of the vehicle is then sent a ticket by mail. Do you strongly approve, somewhat approve, somewhat disapprove, or strongly disapprove of this red light photo traffic management system in Fairfax County? Question "B" Do you strongly approve, somewhat approve, somewhat disapprove, or strongly disapprove of a red light photo traffic management being used in high commuter traffic areas? Question "C" 'What about your neighborhood? Do you strongly approve, somewhat approve, somewhat disapprove, or strongly disapprove of a red light photo traffic management being used in your community to prevent red light running? Table 4: Camera Approval or Disapproval in Fairfax County Response to Question "A" Frequency Cumulative Frequency Percentage Cumulative Percentage Strongly Approve 26 26 5.5 5.5 Somewhat Approve 8 324 29.5 8. Somewhat Disapprove 3 355 7.8 88.8 Strongly Disapprove 37 392 9.3 98, Don't Know 8 4 2., No Response.. Table 5: Camera Approval or Disapproval in High Commuter Traffic Areas Response to Question "B" Frequency Frequency Cumulative Percentage Percentage Cumulative Strongly Approve 242 242 6.5 65 Somewhat Approve 94 336 23.5 84. Somewhat Disapprove 29 365 7.3 9.3 Strongly Disapprove 27 392 6.8 98, Don't Know 7 399.8 99,8 IMo Response 4.3, 45

TRANSPORTATION QUARTERLY/SUMMER 23 Table 6: Camera Approval or Disapproval in your Community Response to Question "C" Strongly Approve Somewhat Approve Somewhat Disapprove Strongly Disapprove Don't Know No Response Frequency 22 86 29 4 9 5 Frequency Cumulative 22 36 335 376 395 4 Pefcentage 55. 2.5 7.3.3 4.8.3 Percentage Cumulative 55. 76.5 83.8 94. 98.8. As seen in the responses to the survey, there is strong support for RLR cameras in Fairfax County. The positive responses to the three approval or disapproval questions averaged 8.5% approval while disapproval averaged 6.2%. With the apparent success of the cameras in reducing violation and accident rates, the support would likely be bolstered, although those drivers ticketed may have changed their opinion. The prior is the more likely scenario, as has been found in other research performed in other jurisdictions. Conclusions The results of this study have identified improvements in violation rates on the order of 36% over the first three months of automated enforcement and 69% reductions after six months of camera operation. Intersections with higher average daily traffic counts were found to have a greater reduction in violations than intersections that had lower speed limits and speed tolerances. Accident rates were found to be reduced by 4% by an initial investigation in accident rates with limited data. Based on the reduction in accidents, there will be a projected 34 property damage only accidents, a projected 28 injury accidents, and a projected 4 fatal accidents avoided due to the use of the red light running camera system in Fairfax County over the first eight years of use. This reduction in accidents, accounting for expenses of implementation and operation of the system, could lead to a benefit of approximately $8.75 million over those same eight years. Public opinion in the Fairfax County jurisdiction was found to be strongly supportive of the automated enforcement. The results found in this study show that the implementation of red light running cameras in Fairfax County, Virginia, have been successful in providing benefits with respect to traffic, economy, and safety. 46

RED UGH-"RUNNING CAMERAS Endnotes. Jack Fleck, and Bridget Smith. "Can We Make Red-Ligbt Runners Stop? Red Light Photo Enforcement in San Francisco, California." Transportation Research Record 693 (March 999): 2. 2. Karl A. Passetti. Use of Automated Enforcement for Red Light Violations. Department of Civil Engineering, Texas A&M University, College Station, Texas, August 997, J-39. 3. Karl A. Passetti. Use of Automated Enforcement for Red Light Violations. Department of Civil Engineering, Texas A&M University, College Station, Texas, August 997, J-25. 4. Insurance Institute for Highv^'ay Safety. "st Time in United States; Study Finds Red Light Cameras Yield Reductions in Crashes, Especially Injury Crashes." http://www.highwaysafety.org/news_releases/2/pr426.htm, April 26, 2. 5. Federal Highway Administration (website), http://safety.fhwa.dot.gov/programs/srlr.htm, August 2, 2. 6. Richard A. Retting, Allan F. Williams, and Michael A. Greene. "Red-Eight Runnmg and Sensible Countermeasures." Transportation Research Record 64 (998): 23. 7. Howard County, Maryland, Department of Traffic (website), http://www.co.ho.md.us/redltech.htmtt2, August 2, 2. 8. Karl A. Passetti. Use of Automated Enforcement for Red Light Violations. Department of Civil Engineering, Texas A&M University, College Station, Texas, August, 997. j-29. 9. Howard County, Maryland, Department of Traffic (website). bttp://vi^ww.co.ho.md.us/redltech.htm#2, August 2, 2.. Howard County, Maryland, Department of Traffic (website), http://www.co.ho.md.us/redltech. htm#2, August 2, 2.. H. C. Chin. "Effect of Automated Red-Eight Cameras on Red-Running." Traffic Engineering & CoHfro/(April 4, 989): 75. 2. Jack Eleck, and Bridget Smith. "Can We Make Red-Light Runners Stop? Red Eight Photo Enforcement in San Erancisco, California." Transportation Research Record 693 (March 999): 3. 3. See note 2 above. 4. Richard A. Retting, Allan F. Williams, and Michael A. Greene. "Red-Light Running and Sensible Countermeasures." Transportation Research Record 64 (998): 25. 5. See note above. f6. Karl A. Passetti. Use of Automated Enforcement for Red Light Violations. Department of Civil Engineering, Texas A&M University, College Station, Texas, August, 997. 7. See note 2 above. 8. See note 4 above. 9. See note 4 above. Acknowledgments We would like to acknowledge the following people for their input, cooperation, and support without which this study would not have been possible. Bruce Tayior of the Fairfax County DOT, First Lt. David S. Vice and Michael A. Uram of the Fairfax County Police Department, and Mena Lockwood, Ling Li, and Jobn L. Jenkins III of the Virginia DOT. 47

TRANSPORTATION QUARTERLY/SUMMER 23 Daniel E. Ruby earned his Master of Science Degree in Civil and Environmental Engineering from Virginia Tech in December 2. He is currently working in the transportation consulting business. Antoine G. Hobeika is a professor of Civil Engineering at Virginia Polytechnic Institute and State University (Virginia Tech). He obtained his Ph.D. in Civil Engineering (Transportation) from Purdue University. In 988, he founded and directed the University Center for Transportation Research, which became the largest interdisciplinary research program at Virginia Tech. In 993, He was the principal investigator and the person responsible for obtaining the "ITS research Center of Excellence" at Virginia Tech, one of the three Research Centers of Excellence funded by Federal Highway Administration (FHWA). He was also responsible for creating the "Smart Road" test bed at Virginia Tech, a two-mile road established at a cost of $37 million for testing and evaluating new transportation technologies. Hoheika was appointed in 994 to a three-year term by FHWA as a member of the Technical Review Team of the National ITS System Architectural development program. Hobeika's interest in transportation encompasses Intelligent Transportation Systems (ITS), transportation planning and economics, transportation emergency management, and airport planning and design. He has successfully conducted research in all these areas and has served as consultant to many public agencies and private firms. 48