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Evaluation of Automated Enforcement on Driver Behavior and Crash Risks

Description:

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).

Objective:

Evaluate how automated enforcement methods (e.g. red-light cameras (RLCs), automated speed measures, etc.) affect driver behavior (e.g. fewer red-light violations, slower vehicle speed, increase attention to on-road demand, etc.) and risk for drivers overall. The research will outline the many ways in which automated enforcement may be deployed and the pros and cons to each approach. Additionally, the observed risks for serious and fatal crashes in both time and distance halos in relation to automated enforced locations should be included in the outcome.

Benefits:

The research findings will provide sound guidance to cities, counties, and states regarding the efficacy of automated enforcement. These findings may also help reduce some of the behaviors that result in serious injury and fatal crash rates along with expected risk ratio changes that could be replicated upon deployment.

Related Research:

Alkan, B., Balci, B., Elihos, A., & Artan, Y. (2019). Driver cell phone usage violation detection using license plate recognition cameras. Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transportation Systems, 468-474.

Balci, B., Artan, Y., Alkan, B., & Eliohos, A. (2019). Front-view vehicle damage using roadway surveillance camera images. Proceedings in the 5th International Conference on Vehicle Technology and Intelligent Transport Systems, 193-198.

Baratain-Ghorghi-F., Zhou, H., & Franco-Watkins, A. (2017). Transportation Research Part F: Traffic Psychology & Behaviour, 46, 87-95.

Claros, B., Sun, C., & Edara, P. (2017). Safety effectiveness and crash cost benefit of red light cameras in Missouri. Traffic Injury Prevention, 18(1), 70-76.

Contini, L. and El-Basyouny, K. (2016). Lesson learned from application of intersection safety devices in Edmonton. Accident Analysis & Prevention, 94, 127-134.

Douma, F., L. Munnich, J.D. Loveland, and T. Garry. Identifying Issues Related to Deployment of Automated Speed Enforcement FY12 TechPlan. Center for Transportation Studies Research Report. CTS 12-23. 2012. http://www.cts.umn.edu/Publications/ResearchReports/report detail.html?id=2158. Accessed July 29, 2016

Elihos, A., Alkan, B., Balci, B., & Artan, Y. (2018). Comparison of image classification and object detection for passenger seat belt violation detection using NIR & RGB surveillance camera images. 15th IEEE Conference on Advanced Video and Signal Based Surveillance

Gates, T., Savolainen, P. T., Maria, H-U. (2014). Impacts of automated red light running enforcement cameras on driver behavior. Transportation Research Board 93rd Annual Meeting

Goldenbeld, C., Daniels, S., & Schermers, G. (2019). Red light cameras revisited. Recent evidence on red light camera safety effects. Accident Analysis & Prevention, 128, 139-147.

Hajbabaie, A., Benekohal, R. F., Chitturi, M., Wang, M-H., & Medina, J. C. (2009). Comparison of automated speed enforcement and police presence on speeding in work zones. 88th TRB Annual Meeting.

Hu, W., McCartt, A. T., & Teoh, E. R. (2011). Effects of red light camera enforcement on fatal crashes in large US cities. Journal of Safety Research, 42(4), 277-282.

Johnson, T. (2019). Red light running deaths hit 10 year high. AAA NewsRoom, Retrieved from: https://newsroom.aaa.com/2019/08/red-light-running-deaths-hit-10-year-high/

Ko. M., Greedipally, S.R., Walden, T.D., & Wunderlicht, R. C. (2017). Effects of red light running camera systems installation and then deactivation on intersection safety. Journal of Safety Research, 62, 117-126.

Martinez-Ruiz, D., M, Fandino-Losada, A., Ponce de Leon, A., Arango-Londono, D., Mateus, J. C., Jaramillio-Molina, C., Bonilla-Escobar, F. J., Vivas, H., Vanlaar, W., & Gutierrez-Martinez, M. I. (2019). Impact evaluation of camera enforcement for traffic violations in Cali, Colombia, 2008-2014. Accident Analysis & Prevention, 125, 267-274.

Mahmassani, H. S., Schoefer, J. L, Johgnson, B. L., Verbas, O., Elfar, A., Mital, A., & Ostojic, M. (2017). Chicago red light enforcement. Best Practices and Program Road Map, Northwestern University. The Transportation Center, Evanston.

National Highway Traffic Safety Administration (2019). 2018 fatal motor vehicle crashes: Overview. Traffic Safety Facts Research Note, DOT HS 812 826. Retrieved from: https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812826

Peterson, C., Douma, F., & Morris, N. (2017). Addressing key concerns regarding automated speed enforcement via interactive survey. Transportation research record, 2660(1), 66-73.

Polders, E., Cornu, J., Ceunynch, T. D., Daniels, S., Brijs, K., Brijs, T., Hermans, E., & Wets, G. (2015). Drivers’ behavioral responses to combined speed and red light cameras. Accident Analysis & Prevention, 81, 153-166.

Porter, B. E., Johnson, K. L., & Bland, J. F. (2013). Turning off the cameras: Red light running characteristics and rates after photo enforcement legislation expired. Accident Analysis & Prevention, 40, 1104-1111.

Retting, R. A., Ferguson, S. A. & Farmer, C. M. (2008). Reducing red light running through longer yellow signal timing and red light camera enforcement: Results of a field investigation. Accident Analysis & Prevention, 40(1), 327-333.

Retting, R. A. & Kyrychenko, S. Y. (2002). Reductions in injury crashes with red light camera enforcement in Oxnard, California, American Journal of Public Health, 92(11), 1822-1825.

Sanders, R. L., Judelman, B., & Schooley, S. (2019). Pedestrian Safety Relative to Traffic-Speed Management. NCHRP Synthesis 535, No. Project 20-05, Topic 49-08. Retrieved from: http://www.trb.org/main/blurbs/179827.aspx

Shaaban, K. & Pande, A. (2018). Evaluation of red-light camera enforcement using traffic violations. Journal of Traffic and Transportation Engineering, 5(1), 66-72.

Shin, K. & Washington, S. (2007). The impact of red light cameras on safety in Arizona. Accident Analysis & Prevention, 39(6), 1212-1221.

Tefft, B.C. (2013). Impact speed and a pedestrian’s risk of severe injury or death. Accident Analysis & Prevention, 50, 871–878.

Wang, C., Musaev, A., Sheinidashtegol, P., & Atkison, T. (2019). Towards a detection of abnormal vehicle behavior using traffic cameras. International Conference on Big Data, 125-136.

Tasks:
  1. Review and synthesize the relevant literature including the sources listed above and any other ongoing or completed work in the area.

  2. Identify and recruit research partners, transportation agencies that are willing to contribute recent and relevant data pertaining to automated enforcement practices and results.

  3. Synthesize existing publications and data into overall findings for short and long-term efficacy of driver behavior change resulting from automated enforcement. May include a meta-analysis, cost-benefit analysis, etc.

  4. Integrate and summarize barriers and best practices for community engagement and deployment of automated enforcement to result in public acceptance of automated speed enforcement.

  5. Prepare a draft Final Report describing the outcomes, best practices, case studies, etc. to enhance adoption and implementation by local and state agencies of automated enforcement based on clear expectations for efficacy related to the application domain.

  6. Finalize and submit the Final Report.

Implementation:

The project will involve the documentation of lessons learned, best practices, and use cases, as noted in task 5-6 above. Implementation support could include peer exchanges, workshops, and presentations to support state Department of Transportation (DOT) implementation.

Sponsoring Committee:ACH30, Human Factors of Vehicles
Research Period:24 - 36 months
RNS Developer:Nichole Morris
Date Posted:01/04/2021
Date Modified:01/15/2021
Index Terms:Automated enforcement, Crash rates, Behavior, Drivers, Crash risk forecasting, Signalized intersections, Red light running, Pedestrian safety,
Cosponsoring Committees: 
Subjects    
Highways
Safety and Human Factors
Law

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