Edited by Susan Hanson, Clark University, Worcester, MA; received August 1, 2025; accepted November 4, 2025
December 8, 2025
122 (50) e2520328122
Edited by Susan Hanson, Clark University, Worcester, MA; received August 1, 2025; accepted November 4, 2025
December 8, 2025
122 (50) e2520328122
Significance
Many US cities are turning to automated speed cameras as a promising alternative to traditional traffic enforcement by police, aiming to reduce collisions without the risks of racial bias or harmful police encounters. But how effective are they? This study evaluates the largest automated speed enforcement program in the United States—2,000 cameras deployed across New York City from 2014 to 2023. Leveraging data on 700,000 collisions and 18 million speeding tickets, the analysis finds that cameras lead to persistent and sustained reductions in crashes. These results demonstrate that automated enforcement can meaningfully improve road safety while avoiding the harms associated with conventional traffic safety approaches.
Abstract
Each year, approximately 40,000 people die in vehicle collisions in the United States, generating $340 billion in economic costs. To make roads safer without expanding police contact, many cities have turned to automated traffic enforcement, cameras that detect and fine speeding motorists. Does automated enforcement reduce vehicle collisions and injuries? Many previous studies are limited to correlational evidence. By contrast, this study estimates the causal effect of automated enforcement on road safety in a difference-in-differences design. We exploit the staggered rollout of 2,000 speed cameras across New York City between 2014 and 2023, combining data on 700,000 collisions and 200,000 injuries with data on 18 million tickets issued. We find that cameras reduce collisions and injuries by 5 and 2.5% per month on average, respectively. Cumulatively, over the seven months following their introduction, collisions declined by 30% and injuries by 16%.
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Data, Materials, and Software Availability
Replication data and code for publication data have been deposited in OSF.io (56). All other data are included in the article and/or SI Appendix.
Acknowledgments
We thank Frank Edwards for his comments on a previous version of the manuscript. All underlying data used in this study are drawn from publicly available sources cited in the paper. The authors acknowledge that they received funding support for this research from the School of Criminal Justice, Rutgers University–Newark.
Author contributions
A.S.-B., J.B.-M., and B.B. designed research; performed research; analyzed data; and wrote the paper.
Competing interests
The authors declare no competing interest.
Supporting Information
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