Tag: Vehicle and Traffic Enforcement

NYPD Vehicle Stops Data – NYCLU

NYPD officers stop over one million New Yorkers in vehicles every year and these stops are likely the largest category of police-civilian interaction. One reason for the enormous number of vehicle stops is that courts have ruled police officers generally have the authority to stop any vehicle as long as they can claim a traffic or vehicle infraction. This standard is so low – especially since it is difficult to drive without violating one of the numerous traffic laws – that it makes it difficult to challenge stops that are made for impermissible reasons, including racial profiling.
— Read on www.nyclu.org/data/nypd-vehicle-stops-data

Mapping the Progress of Policies to Limit Non-Safety Related Traffic Stops | Vera Institute

Over the past decade, efforts to limit non-safety-related traffic stops have swept across the United States. These stops for low-level infractions—like a dangling air freshener, single burnt-out taillight, or expired registration—do not improve traffic safety, and police officers have used them in ways that disproportionately subject Black drivers to physical, psychological, and economic harm. Oftentimes, police have used these stops as a pretext to search for guns and drugs—with little success. Police departments across the country are proving that change is possible. The first known policy to eliminate non-safety-related traffic stops was implemented in 2013 in Fayetteville, North Carolina, under the direction of then-Police Chief Harold Medlock. Fayetteville’s experiment led to decreased racial disparities in traffic enforcement and fewer car crashes and traffic injuries/fatalities, with no impact on non-traffic crime, showing that this type of policy can work. Although the Fayetteville policy ended in 2017, it set the stage for state and local governments, police departments, and district attorneys across the country to take action for safer, fairer traffic enforcement.
— Read on www.vera.org/ending-mass-incarceration/criminalization-racial-disparities/public-safety/redefining-public-safety-initiative/sensible-traffic-ordinances-for-public-safety/stops-map

New data fill long-standing gaps in the study of policing | Science

Data limitations have long stymied research on racial bias in policing. To persuasively demonstrate bias, scholars have sought to compare officer behavior toward minority versus white civilians while holding constant all other factors in the police-civilian encounter that might provide alternative explanations for enforcement disparities. These comparisons in “similar circumstances” are also critical in litigation concerning discriminatory policing, which can often lead to court-ordered remedies (1). Such “all-else-equal” scenarios are elusive in many realms of social science, but two challenges have made them particularly difficult to find in the study of policing. On page 1397 of this issue, Aggarwal et al. (2) report using data from the ridesharing service Lyft—having obtained vehicle location on more than 200,000 drivers using highfrequency GPS pings from their smartphones—to analyze speeding enforcement by the Florida Highway Patrol (FHP) and to show how such data offer a path forward for addressing both challenges.
— Read on www.science.org/doi/10.1126/science.adw3618

2023 TRAFFIC STOP DATA ANALYSIS – Suffolk County PD

Traffic Stop Data Analysis
Stonewall Analytics, LLC 2
Executive Summary
This report analyzes nearly 160,000 traffic stops conducted in Suffolk County, New York, in
2023, with the goal of assessing whether racial or ethnic disparities exist in stop and search
practices. The study applies two key statistical tests: the Veil-of-Darkness test to evaluate racial bias in stop decisions and the Hit Rate test to examine search outcomes across different racial and ethnic groups. The data were obtained through the Suffolk County Police Department. The data were cleaned and standardized to ensure consistency across the reporting period of calendar year 2023.
The Veil-of-Darkness test compares traffic stops made during daylight and nighttime hours,
under the assumption that officers are less able to discern a driver’s race at night. Logistic
regression models were used to assess whether minority drivers were stopped more frequently
in daylight than at night, controlling for various factors such as officer command type. The
results showed no statistically significant relationship between daylight stops and the likelihood
of stopping minority drivers, as compared to White drivers. The odds ratios for minority drivers
remained close to 1.0, indicating that racial bias did not appear to be a significant factor in
initial stop decisions.
The second component of the analysis focused on traffic stops with searches using the Hit Rate test, which assesses whether searches yielded a positive result, which is defined as a search yielding illegal drugs, illegal weapons, or other contraband or evidence. Although the data revealed variation in hit rates across racial and ethnic groups, with White drivers having the highest positive result rate, statistical tests found no significant difference in hit rates between White drivers and Black or Hispanic drivers across geographic areas. While White drivers were more likely to have positive result searches, the difference was not statistically significant, suggesting that variations in search outcomes may be influenced by other factors rather than bias in policing practices.
Overall, the study found no evidence of racial bias in traffic stop decisions based on the Veil-of-Darkness test and no statistically significant differences in search hit rates between minority
and White drivers. However, the findings highlight the importance of ongoing monitoring and
refinement of data collection practices to ensure transparency and fairness in law enforcement
activities. Future research should explore additional variables that may contribute to disparities
in post-stop outcomes and continue evaluating traffic stop data over time to detect any
emerging patterns or policy impacts.

Get the report HERE

Despite fewer people experiencing police contact, racial disparities in arrests, police misconduct, and police use of force continue | Prison Policy Initiative

New Bureau of Justice Statistics data reveal that concerning trends in policing persisted in 2022, even while fewer people interacted with police than in prior …
— Read on www.prisonpolicy.org/blog/2024/12/19/policing_survey_2022/

New Jersey State Police Traffic Stop Analysis 2018-21

Introduction

In November 2021, the New Jersey Attorney General’s Office of Public Integrity and Accountability (NJ-OPIA) engaged the author of this study for the purpose of conducting an independent analysis of traffic stops made by the New Jersey State Police (NJ-SP). Based on the author’s extensive experience working

with state and local policymakers to develop early warning systems for identifying police disparities, the NJ OPIA requested that the analysis focus on the central question of whether there was disparate treatment on the part of NJ-SP towards racial and ethnic minorities.2 After cleaning and linking all of the raw data provided by the New Jersey Office of Law Enforcement Professional Standards (NJ-OLEPS), the analytical sample used in this analysis consisted of 6,177,109 traffic stops made by NJ-SP from 2009 to 2021. In the full analytical sample, 60.52 percent of traffic stops were made of White non-Hispanic motorists while 18.8 percent were Black/African-American and 13.44 percent were Hispanic/Latinx. The overall volume of minority motorists stopped by NJ-SP increased from 35.34 percent in 2009 to 46.28 percent in 2021

www.nj.gov/oag/newsreleases23/2023-0711_NJSP_Traffic_Stop_Analysis.pdf