Include Stakeholder Input and Public Acceptance Factors in Planning Automated Traffic Enforcement Systems.

Review of automated traffic enforcement studies and experience in the United States and internationally.

Date Posted
11/30/2020
TwitterLinkedInFacebook
Identifier
2020-L00995

Safety Impact of Speed and Red Light Cameras

Summary Information

Automated traffic enforcement systems utilize cameras in conjunction with sensors to detect and identify vehicles that potentially violate traffic regulations such as posted speed limits or red lights. Law enforcement and vendor personnel are provided with the image and data to determine whether a citation should be issued. Depending on state and local laws, citations may be issued to the registered vehicle owner linked to the license plate, or the driver identified based on photo images.

There are two major categories of automated traffic enforcement:

  • Automated Speed Enforcement – A speed detection system is combined with a camera or cameras to capture time-stamped images when a pre-defined threshold (i.e., detected vehicle speed exceeds the configured value) is triggered. Locations may be fixed with equipment installed on poles or cabinets on the roadside, or installed at movable locations with equipment attached to a parked vehicle or trailer, and configured based on the location and speed criteria. Average speed cameras utilize pairs of cameras mounted at fixed distances, and calculate the average speed using time stamps.
  • Red Light Cameras – A camera or cameras are connected to a system monitoring the traffic signal indications and detectors for vehicles on an approach to an intersection. Based on the vehicle detectors, camera images are captured with time stamps, including a view of the vehicle and traffic signal. Law enforcement or vendor personnel are provided with the images to determine whether a citation should be issued.

The authors reviewed studies and literature from speed and red-light camera programs in the United States and from countries around the world where they are widely used. Highlights of findings related to automated enforcement programs are presented along with a discussion of issues for policymakers to consider.

  • Based on a review of speed camera program studies, most concluded that speed cameras reduced speeding and/or crashes in the vicinity of the cameras, and in some cases the surrounding areas.
  • When speed enforcement cameras are moved from one location to another, drivers adhere more closely to the speed limit in other areas.
  • Based on a review of red-light camera program studies, cameras were mostly shown to decrease the number of red-light violations and crashes involving injuries and fatalities at signalized intersections. Some evidence suggests that the reduction in right-angle crashes can lead to an increase in rear-end collisions, although severity is generally lower than in right-angle crashes.
  • Not all studies were able to find statistically significant reductions in overall crash severity.

A survey of communities with speed camera programs found that 63 percent of administrators were not aware of USDOT-published operational guidelines for speed enforcement camera systems published in 2008. The guidelines can assist agencies with planning, site selection, system procurement, public awareness, processing notices of violations, and evaluating the programs. Most deployed implementations were consistent with the guidelines in many areas. The most frequent areas where implementations did not meet the guidelines were:

  • Using photos to identify the driver and issue a combination of fines and license sanctions
  • Establishing a stakeholder advisory panel to guide the program and provide input

Use of automated enforcement in the United States has observed a decline in public support. In 2008, 68 percent of respondents somewhat or strongly supported speed cameras on neighborhood streets, versus 42 percent in 2018. In 2008, 70 percent of respondents somewhat or strongly supported red light cameras, as compared to only 43 percent in 2019. Potential reasons include perceptions that some systems were ineffective or abusive, or a greater number of citations in the driver population.

Goal Areas

Keywords Taxonomy: