Assessing the impact of fixed speed cameras on speeding behavior and crashes: A longitudinal study in New York City

Abstract

Speeding is a leading contributor to fatal crashes. This longitudinal study examines the short- and long-term changes associated with an automated speed enforcement program’s expansion from 2019 to 2021 in New York City, including the COVID-19-induced surge on speeding behaviors and the complex nature of high volumes of pedestrians and non-motorized vehicles. Leveraging speeding tickets from 1,821 fixed speed cameras in school zones and crash data, this study employs interrupted time-series, spatial distribution, clustering analysis, and Survival Analysis with a random effect (SARE) to investigate if such a program brings about immediate and/or long-term change in speeding behaviors and crash reduction. The findings suggest a decrease in speeding tickets by an average of 18.4 %, 13.3 %, and 0.6 % in the second-, third- and fourth-month post-installation, demonstrating the program’s short-term efficacy in reducing speeding behavior. However, diminishing and time-lag effects were observed at some camera locations, indicating the need for further investigation and potential alternative safety interventions at these sites. Long-term analysis revealed a substantial 75 % reduction in speeding tickets by the end of 2021, despite a temporary surge during the pandemic. Four different long-term patterns were identified. Furthermore, crash analysis showed a statistically significant 14 % decrease in traffic crashes (pre-COVID) following speed camera implementation. Overall, the program has been largely successful in reducing speeding violations and traffic crashes, but its temporal effect varies across sites. Continuous monitoring, data-led adaptive strategies, and additional safety countermeasures are needed to optimize the program’s impact.