A before-after full bayes multivariate intervention model to estimate the safety effectiveness of red light cameras

Abstract

Objective

Intersection-related crashes account for approximately 40% of all crashes and tend to be more severe. Red-light running (RLR) crashes are most severe as almost half of these crashes result in injuries and fatalities. To reduce RLR crashes, agencies have been deploying red light cameras (RLCs). The main objective of this study was to evaluate the safety effectiveness of RLCs in the City of Miami Beach, Florida.

Method

The full Bayes (FB) approach was conducted based on five treatment intersections with six RLCs and 14 comparison intersections without RLCs. The analysis focused on target crash types, including rear-end, sideswipe, and angle/left-turn/right-turn crashes, and crash severity.

Results

The FB analysis indicated a significant sudden drop in all types of target crashes immediately after the installation of RLCs. Compared to the before-period, the after-period experienced: fewer angle/left-turn/right-turn crashes, fewer sideswipe crashes, and more rear-end crashes. The sideswipe and angle/left-turn/right-turn crashes dropped immediately after the installation of RLCs and then continued to increase, but they were still lower than the before- period. The rear-end crashes dropped immediately after the installation of RLCs and then continued to increase, but they increased at a steeper rate. Major and minor approaches AADT, higher speed limit, longer amber time, length of pedestrian crosswalk, and number of driveways within the intersection influence area increased the frequency of total target, PDO, and FI crashes. Intersections with all-red interval more than two seconds, major approach with more than two through lanes, and minor approach with more than one through lane, on the contrary, resulted in a fewer number of the total target, PDO, and FI crashes. The treatment indicator showed that treatment intersections experienced fewer FI, angle/left-turn/right-turn, and sideswipe crashes and more total, PDO, and rear-end crashes compared to the non-treatment intersections.

Conclusion

This study provides reliable estimates of the safety effectiveness of RLCs since it accounts for uncertainties in the data, regression-to-the-mean, and spillover effects.

Keywords:

• Red-light running crashes
• red light camera
• full bayes
• before-and-after
• regression-to-the-mean

Acknowledgments

The authors thank the City of Miami Beach Officials for their guidance and support throughout the course of this project. A special thanks is due to the City of Miami Beach Police Department and the City of Miami Beach Transportation Department for providing the required data. The authors are also thankful to Mr. Hector Vargas and Ms. Liana Roque, undergraduate research assistants at Florida International University, for assisting with the data collection. The opinions, findings, and conclusions expressed in this paper are those of the authors and not necessarily those of the City of Miami Beach.

Data availability

All data used during the study were provided by the City of Miami Beach. Direct requests for these materials may be made to the provider, as indicated in the Acknowledgements.

Additional information

Funding

This research was funded by the City of Miami Beach.