In the United States more than two deaths are caused by drivers running red lights every day (Johnson, 2019). According to AAA, deaths caused by drivers running red lights have hit a ten year high. Even more pronounced are the 9,378 people who were killed in 2018 in the U.S as a result of speeding (NHTSA, 2019). Preventing the risky behaviors that contribute to these crashes remains a challenge, as driver culture is difficult to change and insufficient resources limit the scope and reach that enforcement may impose on deterring risk-prone drivers. A solution that has been deployed in other countries, and on a limited basis in the U.S., is the use of automated enforcement to expand the rate at which drivers who violate signals and speed limits may be detected and fined. As previous declines in fatal crashes have slowed or even reversed in the U.S., some cities and states are re-examining the role that automated enforcement may serve to reduce fatal crashes. This approach, however, is partially met with resistance by those unsure of the degree to which the claims of automated enforcement are accurate and whether the benefits outweigh the potential costs, including privacy concerns, fairness, and expense.
As noted, the use of automated camera enforcement systems to detect traffic violations and change driver behavior have become increasingly widespread. Automated enforcement systems have been gaining popularity by offloading the laborious process of traditional policing (Alkan, Balci, Elihos, & Artan, 2019). Several types of automated enforcement systems and techniques have been explored by researchers, including the use of infrared and surveillance camera systems to detect seat belt violations, the application of deep learning techniques (i.e., object detection and image classification) to detect damaged vehicles, and the use of live video to detect abnormal driving behavior (e.g., stalled vehicles) (Elihos, Alkan, Balci, & Artan, 2018; Balci, Artan, Alkan, & Elihos, 2019; Wang, Musaev, Sheinidashtegol, & Atkison, 2019).
RLCs are one type of enforcement countermeasure used at signalized intersections for detecting vehicles passing the approach during the red phase. This method of automatic enforcement addresses the issue of high cost manual enforcement and has the potential to change driver behavior (Shaaban & Pande, 2018). Research has found that the implementation or presence of RLCs can reduce red-light running violations, increase stopping behaviors, and reduce red-light running crashes (Shaaban & Pande, 2018; Hu, McCartt, & Teoh, 2011; Retting, Ferguson, & Farmer, 2008; Gates, Savolainen, & Maria, 2014; Baratian-Ghorghi, Zhou, & Franco-Watkins, 2017).
Although RLC deployment has significantly reduced angle crashes, left-turn crashes, and other injury crashes, many studies have noted an increase in rear-end crashes (Shin & Washington, 2007; Claros, Sun, & Edara, 2017; Polders et al., 2015). However, it appears that the benefits greatly outweigh the costs of using RLCs to improve driver safety. Researchers examined the impact of an expired law permitting automated enforcement for red-light running and found that there was a near-immediate increase in red-light running, and the behavior quadrupled more than one year later, i.e. post-expiration (Porter, Johnson, & Bland, 2013). Ultimately, research should examine the increase in rear-end crashes at locations where RLCs have been introduced to address the concerns surrounding this issue.
Pedestrian safety is another emerging area of concern that may have implications for automated enforcement. Controlling traffic speeds is integrally tied to pedestrian safety. Slower speeds not only provide more time for drivers to safely stop for pedestrians, they also increase the likelihood of survivability should a pedestrian be struck (Tefft, 2013). Several cities have implemented Automated Speed Enforcement (ASE) to address instances of drivers speeding where road redesign and additional police enforcement were not feasible; however, public pushback can stymie these approaches (Sanders, Judelman, & Schooley, 2019).
The crux of initial and sustained deployment of automated enforcement efforts is public acceptance, especially around schools, work zones, and high-risk roadways (Douma, Munnich, Loveland, & Garry, 2012). However, there is evidence that opposition of automated enforcement (e.g., legality, efficacy, fundraising, etc.) can be addressed, but opposition tends to be intractable among those who do not believe speeding and safety are linked (Peterson, Douma, & Morris, 2017). Further research is needed to determine the most effective ways to introduce automated enforcement to the public in order to receive continued support for its deployment.
Current research suggests that the use of automated enforcement may improve driver behavior and reduce crashes. Moreover, RLC installations have the potential to impact the prevalence of crashes in surrounding areas via the spillover effect (Martinez-Ruiz et al., 2019; Goldenbeld, Daniels, & Schermers, 2019). Several studies have noted that spillover effects of RLC implementation have occurred in adjacent non-treated RLC intersections, nationwide (Ko et al., 2017; Mahmassani et al., 2017; Cotnini & El-Basyoung, 2016).
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