NYC speed cameras take six months to change driver behavior, effects vary by neighborhood

New York City’s automated speed cameras reduced traffic crashes by 14% and decreased speeding violations by 75% over time, according to research from NYU Tandon’s C2SMARTER published in Transportation Research Interdisciplinary Perspectives that tracked more than 1,800 cameras across school zones from 2019 to 2021.

With speeding contributing to approximately one-third of all motor vehicle fatalities nationwide, these findings translate to potentially hundreds of lives saved in America’s most densely populated city.

The study from C2SMARTER—a US Department of Transportation Tier 1 University Transportation Center—complements the NYC Department of Transportation’s (NYC DOT) own 2024 report, which similarly found a 14% reduction in injuries and fatalities at camera locations compared to control sites without cameras.

While the NYC DOT report provides valuable citywide statistics, the C2SMARTER study reveals several critical insights: cameras typically reach a strong level of effectiveness within six months, effectiveness patterns vary geographically across the city, and changes in driving behavior may exhibit a ‘time-lag’ effect.

“Our research methodology provided an in-depth short-term and long-term analysis of these cameras, taking into consideration the continuous installation of new cameras,” explained Jingqin Gao, Assistant Director of Research at C2SMARTER and the paper’s lead author. “By tracking each camera’s performance over time, we uncovered spatial and temporal patterns that may be less visible in citywide data, providing officials additional insights on the longitudinal effects and more strategic positioning of future cameras to maximize the program’s effectiveness.”

NYC’s speed camera program has evolved from a 20-camera pilot in 2013 to a network of 2,200 cameras across all 750 school zones by 2023—expanding from limited hours (6 a.m.–10 p.m. weekdays) to 24/7 operation in 2022. C2SMARTER’s research examines the critical 2019–2021 timeframe when the program first achieved citywide scale.

What sets this study apart is its longitudinal approach—tracking fixed camera sites over extended periods. The research revealed most cameras achieve their safety purpose within six months, with violations dropping and staying low—showing drivers have changed behavior to drive more slowly and the cameras are working as intended, to deter speeding.

“Our long-term analysis identified four distinct patterns in how specific camera installations performed,” said Gao. “Cameras at some locations showed consistent reductions at varying magnitudes in two groups, with a surge in speeding tickets during COVID.

“A third group exhibited a relatively modest effect but nearly curbed speeding behaviors within 1.5 years, despite COVID-19 impacts, and a small set of camera sites saw marginal impact in the first few months but experienced dramatic COVID-era speeding increases,” Gao added.

“Our short-term analysis also provided evidence of a ‘time-lag effect,’ where driver compliance improved gradually rather than immediately after installation.”

The C2SMARTER team, led by its Director Kaan Ozbay, professor in the NYU Tandon Civil and Urban Engineering Department (CUE), pioneered the application of Survival Analysis with Random Effect (SARE) for before-and-after evaluation of traffic safety treatments. This statistical method models the time intervals between crashes rather than simply counting them. Their findings were published in a series of papers in top traffic safety journals, including Risk Analysis and Safety Science.

This approach alleviates the challenge posed by the need to wait years to collect data needed to conduct before and after analyses using traditional statistical approaches. The significantly shorter time periods of data collection potentially saves lives by allowing traffic engineers to re-evaluate their deployment approaches of safety treatments.

“The SARE method can accommodate the different implementation dates of speed cameras,” said Di Yang, a paper co-author who is currently an assistant professor at Morgan State University. Yang received his Ph.D. from CUE in 2022 under Ozbay’s advisement. “This approach allows us to better leverage the time intervals between crashes to estimate the change in crash rates before and after implementing speed cameras.”

These nuanced findings provide critical guidance for policymakers and urban planners across the country. Rather than a one-size-fits-all approach, the research points to the need for targeted, data-driven strategies that combine enforcement with engineering solutions tailored to specific locations.

“This isn’t just about issuing tickets,” concluded Ozbay. “It’s about using data analytics and advanced statistical methods to save lives on our streets, especially in dense urban areas where a single speeding vehicle can have devastating consequences.”