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Back to Archives | Back to July 2007 Contents 

Automated Speed Enforcement Study

Lieutenant Jack Hegarty, Arizona Department of Public Safety, Phoenix, Arizona


he Arizona Department of Public Safety (DPS) has responsibility for traffic enforcement and crash investigation on freeways in the Phoenix metropolitan area. Many of these freeways flow through incorporated cities. The department actively addresses the increasing crash problem on all Phoenix Valley freeways with aggressive traffic enforcement and innovative programs such as Safe Commute, Maximum Impact, and impaired-driver task forces.

The DPS initiated Operation Safe Commute on the premise that highway patrol officers could reduce the number of vehicle collisions on Phoenix Valley freeways by targeting commuter traffic during peak traffic periods. During Operation Safe Commute, the DPS stressed reducing patrol response time to collisions and to disabled motorists. This operation stressed immediately removing any hazard from the freeway. Officers practiced zero-tolerance enforcement of crash-causing violations observed on the freeway but conducted the actual traffic stop off the mainline freeway in order to minimize traffic flow disruption. During the first Safe Commute program period in 2002, the total number of collisions decreased by 22 percent on Interstate 17 and 42 percent on Interstate 10 as compared with the same freeway segments in the previous year.

Arizona Route 101, often referred to as Loop 101, was the primary highway targeted in Operation Maximum Impact. From 2002 to 2005, this program sought to reduce the frequency of motor vehicle collisions by targeting impaired drivers as well as those who drive at excessive speeds during off-peak traffic periods. Although the overall goal of this operation was similar to that of Operation Safe Commute, Maximum Impact targeted off-peak traffic periods. Like Safe Commute, Maximum Impact made removing hazards from the mainline a major operational goal. During the 2005 Maximum Impact details on Phoenix Valley freeways, law enforcement officials stopped 3,837 violators, issued 439 citations for crash-causing violations, issued 2,822 speed citations, assisted and removed from the mainline freeway 272 motorists with stranded vehicles, and made 67 arrests.

When the opportunity arose to assess the automated speed enforcement (ASE) systems on Arizona Route 101 through Scottsdale, the Arizona DPS was very interested. This article contains only preliminary data on the ASE systems, as traffic systems analyst Dr. Simon Washington of Arizona State University is currently evaluating the project’s results and will issue the final report in late 2007.1 Police executives may find the preliminary data interesting and even valuable as they consider the merits of ASE systems. It must also be remembered, though, that the DPS’s aggressive enforcement actions may have affected the results of the ASE study.

Intelligent Transportation Systems

Intelligent transportation systems now accurately enforce safe municipal speed limits using camera-based technology. Generically known as “speed cameras,” these systems have been effective in municipalities for years. By 2005, at least 75 countries relied on such cameras to enforce speed limits, especially on high-risk roads. But whereas ASE systems have proven their worth on municipal streets, it is technically challenging to deploy these devices on high-volume, high-speed, multilane freeways.2

The first automatic systems to be widely deployed in the United States were red-light cameras; their general success led to the use of speed cameras by some municipalities. Internationally, the success of such systems has prompted greater use of this technology than in the United States: for example, “by 2004 the United Kingdom had successfully deployed 6,000 photo speed cameras, and the number there continues to grow.”3

Automated or photographic speed enforcement systems use three subsystems: a vehicle speed subsystem, a vehicle/driver photo subsystem, and a speeding violation subsystem. The vehicle speed subsystem typically relies on a radar or LIDAR (light detection and ranging) sensor to determine the speed of the vehicle, or else it uses an in-pavement sensor. When a vehicle is speeding, this triggers the vehicle/driver photo subsystem, which takes two photos: one of the driver and one of the rear license plate. This requires two cameras, but only one camera is needed if the vehicle has a front license plate or the enabling legislation in a given jurisdiction does not require a driver photograph. A data record is formed by coupling the speed information with the photographs of the driver and license plate for each violation. The speeding violation subsystem uses the records created by the first two subsystems to identify the driver of the speeding vehicle, issue a speeding violation, and prosecute the person if guilt or responsibility is not admitted.4

Loop 101

When Scottsdale conducted an ASE operation on a segment of Loop 101, it provided an opportunity to assess the effectiveness of an ASE system on a major freeway. The route was completed as a northern loop around Phoenix in 2002, and the ASE test segment consists of three lanes of traffic and a continuous transition lane in both directions. It extends from Arizona Route 202 in the eastern part of the Phoenix Valley, north through Scottsdale to I-17, west to Glendale, and south to Avondale, where it connects with I-10, stretching a total of 51 miles. Since completion as a freeway, traffic volume has increased about 50 percent, and several locations averaged well over 120,000 vehicles per day by 2005. The speed limit on the loop is 65 miles per hour.

The population of Phoenix, Scottsdale, and other valley cities has grown substantially in the last decade. This has led to double-digit percentage increases in traffic volumes and crashes on freeways throughout the Phoenix Valley in recent years.

The City of Scottsdale received a permit from the Arizona Department of Transportation to conduct an ASE test on Loop 101 in response to increasing crashes and citizen complaints about speeding. The test ran between January and October 2006. During the first month, before the enforcement period, 16,257 warnings were issued. During the eight-month period between February 22, 2006, and October 23, 2006, Scottsdale issued 90,344 ASE citations to vehicles traveling at or over the threshold speed of 76 miles per hour.

Analytical Methodology

Two analytical methods are discussed here for data generated from this enforcement effort: a simple pre- and posttest comparison of crash data on the test segment, and a comparison of crash data with a control freeway segment in the western valley. The segment of Loop 101 on which the ASE project was conducted is outlined with a small rectangular box at the right side of figure 1. The box at the left side of the figure is the comparison segment of freeway. Both segments run through similar suburban areas and employ modern freeway design methods. Although traffic volumes on both segments are similar, several factors affected traffic conditions on the western segment of Loop 101 that were not present or were present in varying degrees on the eastern segment, such as ongoing construction projects, rapid growth, and the completion of the new Arizona Cardinals stadium.

Fig. 2
The ASE segment of this highway has been characterized by drivers and in local editorials as a dangerous racetrack. Excessive speeds have been frequently observed. However, DPS crash data indicate that it is not as dangerous as some other local freeway segments. Figure 2 compares total crashes and crash rate, between February and October 2005, for segments of freeways the same length as the test segment but from different areas of the Phoenix Valley. Crash rate is defined as the occurrence of crashes per mile traveled, so it is affected by volume as well as by the total number of crashes. What became the ASE test segment on Loop 101 had the fewest total crashes.

Despite its safer crash profile, the selected segment of Loop 101 was still an excellent candidate for an ASE test because it experienced a variety of traffic volumes and speeds.

Crash Data

The Arizona Department of Transportation (ADOT) compiles and maintains crash data for the entire state, and the DPS compiles crash data for roadways in its jurisdiction. The DPS does not typically compile or maintain traffic speed information. The analysis in this article is based solely on DPS crash data.

Fig. 3
Pre- and Posttest Analysis

During the project period in 2006, 250 crashes occurred in the test segment. Of these, 185 involved only property damage, 64 involved personal injury, and 1 included a fatality. These numbers are lower than the totals for 2002–2005 (see figure 3). The average daily volume on the test segment increased about 50 percent from 2002 to 2005; 2006 daily volume figures are not yet available.

The pre- and posttest analysis indicates that 14 percent fewer total crashes (40) occurred in 2006 than in 2005, including a 16 percent decrease in property damage crashes (34) and a 7 percent decrease in injury crashes (5). Crashes in 2005 were down 13 percent over 2004, including an 11 percent decrease in property damage crashes and a 23 percent decrease in injury crashes. An ASE system was not in place in 2004 or 2005; however, the DPS did conduct several aggressive enforcement campaigns on Loop 101 in both years, which may account for this decrease. The effect of the significant reductions in 2005 make the 2006 analysis more complicated. If crashes had continued to increase in 2005, the reductions in 2006 could be attributed more easily to the ASE system. Since this is not the case, the reductions in 2006 cannot be clearly related to any single factor. Fluctuations in crash numbers occur, and these data samples are small enough that minor, unrelated changes in conditions could have caused these differences.

Fig. 4
Control Segment Comparison

The western segment of Loop 101, serving as a control, had more crashes of all types than the ASE test segment (figure 4). The test segment had 13 percent fewer total crashes (39), including 17 percent fewer property damage crashes (37) and 3 percent fewer injury crashes (2).

Additional Concerns Raised by the Study Data

This basic analysis raises some interesting questions. What caused the decreases in crash totals? Was the ASE system responsible? Did all types of crashes decrease? Were injuries resulting from crashes less severe? Did statistics depend on the day of the week or the time of day?

The 65-mph speed limit is not as important during peak traffic hours, as traffic slows to well below that limit. Crashes occur, however, when normal traffic flow meets congestion. Lower mean speeds due to use of an ASE system may have reduced the number of these crashes.

A drawback to the analysis is that neither the DPS nor ADOT collects sufficiently detailed data to support analysis of change, injury severity, or damage severity. Injury severity is recorded on the basic ADOT accident report form, but injuries are classified on the form by general category. Detailed information regarding severity of injury and extent of property damage are contained only in the narrative portion. Therefore, jurisdictions and communities seeking ASE programs should consider how and what data are collected and should make appropriate improvements if required. Since community and civic support of these programs is crucial, a detailed analysis of the impact on injury and damage severity would be very useful. Such an analysis would provide a more accurate picture of the impact of an ASE program on the economy and quality of life.

Fig. 5
Analysis by Hour of Day and Day of the Week: Pre- and posttest crash totals counted by hour of day during the workweek show mixed results (figure 5). Some hours show increases; others, decreases. Viewing the data on the hour of day as a whole, crash frequency tended to reflect traffic volume throughout the day. One might expect the ASE system to affect the number of crashes outside the 7:00–9:00 a.m. and 4:00–7:00 p.m. peak traffic periods, but instead, crashes decreased significantly in the 7:00–9:00 a.m. period. The afternoon peak period delivered mixed results. Crashes increased in the 3:00–4:00 p.m. period and decreased in the 5:00–8:00 p.m. time frame. Outside the peak traffic hours, mixed results are again apparent. The reader is warned that the data sample in this study is small and warrants caution in drawing conclusions.

Since crash totals decreased during the morning peak traffic period, when the ASE system was not expected to be a factor, other conditions may have been responsible for these reductions. The other possibility is that the ASE system did affect the frequency of crashes during the morning commute and that lower mean speeds were a factor. Congestion and peak traffic times do not lead to constant traffic flow. Occasionally, congestion eases and traffic flow accelerates. If mean speeds at these times were lower than in the past, crashes could have been reduced.

Fig. 5
Pre- and posttest crash totals broken down by day of the week (figure 6) show significant reductions in number of crashes on Fridays, Saturdays, and Sundays. Wednesdays registered a reduction, Tuesdays and Thursdays experienced increases in overall crash totals, and Mondays were unchanged.

The ASE system was expected to decrease crash totals on weekends since traffic volumes are lower than during the workweek. Fridays are typically the busiest day of the week in terms of traffic volume and number of crashes. The significant crash reduction on Fridays occurred throughout the day, not just during nonpeak traffic hours.

Fig. 6
Causes of Crashes: Crashes arising from several causes tracked by the DPS decreased during the ASE test compared with the previous year (figure 7). Pre- and posttest analysis indicates that crashes due to driving at a speed not reasonable and prudent decreased 6 percent (9), lane usage crashes decreased 26 percent (10), and evasive-action crashes decreased 44 percent (12). This last statistic could explain some of the decreases in crashes during peak traffic hours when normal traffic flow meets congestion and is consistent with the expected effects of the ASE system.

When compared with the control segment, the test segment had nearly the same number of crashes due to driving at a speed not reasonable and prudent (154 versus 155) and lane usage (28 versus 29) as the control segment (figure 8). Evasive-action crashes on the test segment were 35 percent lower than the control segment (15 versus 23).

Fig. 7
Again, sample size is small, and further study is necessary before drawing definitive conclusions.

Public Information and Media Coverage

The information campaign before and during the ASE period was substantial. The details of the project location and dates were widely publicized. Morning radio talk shows, local television news, and the Internet all covered the story.

Local newspapers published dozens of related articles during 2006. Several potential legislative bills regarding ASE systems received publicity during the project. This traffic enforcement project was one of the most publicized in Arizona history; the attention alone may have had a significant effect on its results.


Was the ASE system responsible for the decreases in number of crashes? It seems very possible, but the reductions cannot be explained simply by this limited study.

Before the ASE test, there were 290 crashes. During the test there were 250 crashes, a reduction of 14 percent (a total of 40 fewer crashes). Assuming that the numbers would have stayed the same or even increased without the ASE system, then at least 40 motorists and their passengers benefited from the test.

If the reduction in crash numbers resulted from deployment of the ASE system, then future ASE projects should produce similar results. ASE systems on major freeways likely affect traffic flow in complex ways that require further study to understand. Thus, the DPS looks forward to the final report from Dr. Washington later this year.■

1Dr. Washington’s preliminary report is available as Simon Washington, Kangwon Shin, and Ida Van Shalkwyk, Evaluation of the City of Scottsdale Loop 101 Photo Enforcement Demonstration Program: Draft Summary Report, January 11, 2007,, May 24, 2007.
2See Craig A. Roberts and Jamie Brown-Esplain, Technical Evaluation of Photo Speed Enforcement for Freeways, Final Report 596, October 2005,, May 16, 2007, 1.
3Ibid., 2.


From The Police Chief, vol. 74, no. 7, July 2007. Copyright held by the International Association of Chiefs of Police, 515 North Washington Street, Alexandria, VA 22314 USA.

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