Background

2026 marks the sixth year the Virginia Department of Criminal Justice Services (DCJS) has published an annual report analyzing traffic stop data from the Virginia State Police’s Community Policing Database, as mandated by the Code of Virginia § 9.1-192. This report contains a review of how the data was collected and analyzed as well as preliminary findings from 1,048,265 traffic stops reported in Virginia during the 12-month period between January 1, 2025, and December 31, 2025. Also presented are findings from analyses of statewide data; aggregated data from the seven Virginia State Police (VSP) Divisions; and data from each individual law enforcement agency that reported sufficient data to the Community Policing Database.

The information presented in this report should be interpreted with caution. Although these analyses identified disparities in traffic stop rates related to race/ethnicity, it does NOT allow us to determine or measure specific reasons for these disparities. Most importantly for this study, this analysis does NOT allow us to determine the extent to which these disparities may or may not be due to bias-based profiling or to other factors that can vary depending on race or ethnicity. These other factors include:

• Differences in locations where police focus their patrol activities.

• Differences in underlying regional populations.

• Differences in driving patterns among individuals and across racial/ethnic groups.

• Lack of a scientifically established baseline for accurately determining the number of drivers in each racial/ethnic group who are on the road in any given area and subject to being stopped while driving.

The analysis of racial disparities in traffic enforcement is a complex field with many potential contributing factors. Many data elements play influential roles in racial/ethnic patterns of traffic enforcement but are unavailable to DCJS. Factors such as the race and/or gender of the officer performing the stop, agency policies and community priorities in driving enforcement policies, as well as police report narratives outlining legal justifications for stop, search, and arrest, can all inform stop patterns but are not included in the current data collected under the Community Policing Act (CPA).

Additionally, the data presented in this report cannot reflect any stop trends from agencies which did not provide data or records that were excluded for completeness issues. As such, while the report presents stop, search, and arrest disparities based on the available data, the dataset should not be considered as 100% complete. Any disparities should NOT be construed as evidence of bias due to the existence of contributing factors, the study of which falls outside the scope of these analyses.

This report does not tabulate the many positive actions that can occur pursuant to a traffic stop such as seizures of guns, confiscation of drugs, ensuring valid and current drivers’ licenses, arrests of individuals with outstanding warrants, and removal of impaired drivers from public roadways. The Community Policing Act imposes narrow requirements for data collection and analysis, and any benefits of traffic or pedestrian stops are not within the scope of the law.

Key Findings

Despite the limitations noted earlier, DCJS staff were able to identify differences in traffic stop rates for persons in different racial/ethnic groups for calendar year 2025. This was done by comparing the percentage of persons in each racial/ethnic group in Virginia’s population 15 years and older (generally the legal age to drive in Virginia) to the percentage of persons in each racial/ethnic group among drivers in traffic stops. The ratio between these two percentages was used to calculate a statewide Disparity Index (DI) for stops for each driver group. Traffic stop DIs were not calculated for agencies such as airport or campus PDs because population breakouts by age and race/ethnicity were not available for these specific areas.

The overall finding of this analysis is that, statewide, Black, Native American, and to a much lesser degree Hispanic drivers in Virginia were disproportionately stopped by law enforcement when compared to other drivers between January 1, 2025, and December 31, 2025, relative to their numbers in Virginia’s driving-age population. This disparity was noted for stops of Virginia resident drivers as well as out-of-state drivers and to a lesser extent for local resident drivers. Among the small percentage of traffic stops made for investigative reasons, Hispanic and Asian drivers were disproportionately stopped relative to their population percentage.

DCJS staff also examined differences in what happens to drivers in different racial/ethnic groups once a stop has occurred. This analysis was conducted only for those agencies reporting a sufficient number of searches and actions taken toward the driver. This was done by comparing the percentage of drivers stopped in each racial/ethnic group to the percentage in each group for which the stop resulted in a particular outcome such as a search or arrest. Differences between driver racial/ethnic groups were found regarding the reasons a stop was made, whether a search of individuals or the vehicle occurred, and what action was taken toward the driver (warning, citation, arrest, etc.).

Compared with stops of drivers from other racial groups, stops of Black and Hispanic drivers were generally more likely to result in a search or an arrest. This finding is consistent with traffic stop research conducted in other states, and with the general findings of the previous DCJS CPA reports.

A statistical method known as a chi-square test of association showed that high to moderate levels of overrepresentation were not always statistically significant. Over one third of law enforcement agencies (LEAs) showed an overrepresentation of Black and Hispanic drivers for searches; however, chi-square testing suggests that no more than 16% of agencies had a statistically significant overrepresentation of either group with respect to searches. This pattern continued for arrest with nearly one third of agencies exhibiting overrepresentation for Black and Hispanic drivers but chi-square testing suggesting that overrepresentation was statistically significant at less than 14% of agencies.

Driver Racial/Ethnicity Analysis of Statewide Traffic Stops

In total, 1,144,265 traffic stops made in Virginia were analyzed, representing all stops with full data reported by VSP and 304 other PDs and SOs for the calendar year 2025. All references to “2024" refer to the previous analysis year.

• White and Asian/Pacific Islander drivers continue to be stopped at rates near or below their representation in the driving-age population statewide. This underrepresentation occurred not only for drivers stopped, but also for all related measures including reasons for stops, searches of drivers and vehicles, and stop outcomes such as arrests orcitations.

• During the current reporting period, Black drivers were stopped at a higher rate than White drivers. Although an estimated 19.4% of Virginia’s driving-age population in 2025 was Black, 29.6% of drivers stopped were Black, comparable to 29.6% stopped in 2023 and 29.1% stopped in 2024.

• Black drivers who were stopped were searched at higher rates than White drivers. 2.2% of stopped Black drivers had a search of their person or vehicle conducted (similar to 2.1% in 2024 and down from 2.5% in 2023), compared to 1.2% of White drivers (compared to 1.5% in 2024).

• Black drivers who were stopped were arrested at higher rates than White drivers. 1.1% of Black drivers stopped were arrested, compared to 0.5% of White drivers. These rates are both decreases from the 1.4% for Black drivers and 0.7% for Whited drivers stopped in 2024.

• Hispanic drivers (of any race) were stopped at marginally higher rates than White drivers. Hispanic drivers made up a slightly higher proportion (10.2%, up from 9.9% in 2024) of Virginia’s driving-age population in the 2025 dataset and constituted 11.3% of all drivers stopped compared to 11.2% in 2024.

• Hispanic drivers who were stopped were searched at higher rates (2.5%) than White drivers (1.5%) or Black drivers (2.2%), comparable to the previous two years.

• Hispanic drivers who were stopped were arrested at higher rates (1.3%) than either White drivers (0.5%) or Black drivers (1.1%). These rates are slightly decreased from 2024.

• Native American drivers were stopped at marginally higher rates than White drivers. While they made up 0.3% of Virginia’s driving-age population in the dataset, they made up 0.4% of drivers stopped. Due to the low frequency of Native American individuals in Virginia’s population, their disparity index rates in these analyses are especially prone to sensitivity. Stopped Native American Drivers were largely underrepresented in searches and arrests.

Driver Racial/Ethnicity Analysis of Traffic Stops: Agency-Level

Virginia State Police

Across the seven Divisions of the Virginia State Police, moderate overrepresentation of Black and Hispanic drivers regarding the number of drivers stopped relative to their proportion of the driving age population was noted for all Divisions.

Black drivers were moderately overrepresented for searches of the driver or vehicle in six divisions compared to the number of drivers stopped. Hispanic drivers were moderately overrepresented for searches in all seven VSP divisions; however, this is a decrease from the high overrepresentation in three Divisions seen in 2024. In addition, a slight decrease in overrepresentation of Asian drivers with regard to searches was noted.

White drivers experienced no overrepresentation in arrests in any VSP Division in 2025. This is a decrease from three Divisions showing moderate overrepresentation in 2024. For Black drivers, the number of divisions reporting high overrepresentation in arrests dropped from five to zero, with those divisions plus one additional division reporting moderate overrepresentation. Similarly, for Hispanic drivers, the number of divisions reporting high overrepresentation in arrests dropped from six to one, with the remaining six divisions reporting moderate overrepresentation.

Agencies Serving Counties and Independent Cities

Overrepresentation of Black drivers was found for the majority (78.9%) of City and County LEAs regarding the number of local resident drivers stopped compared to their proportion of the eligible driver population. Overrepresentation of Hispanic drivers decreased to 35.4% of agencies in 2025 from 57.6% in 2024. Overrepresentation in stops of Asian drivers also decreased to 2.8% from 19.2% of agencies in 2024. 52% of LEAs serving cities and counties reported high or moderate overrepresentation in searches of Black drivers and 41.2% reported overrepresentation in searches of Hispanic drivers compared to 10.1% reporting overrepresentation in searches of White drivers.

Similarly, 46.6% of agencies reported high or moderate overrepresentation in arrests of Black drivers and 32.5% of agencies reported overrepresentation in arrests of Hispanic drivers, compared to 10.8% reporting overrepresentation in arrests of White drivers.

Agencies Serving Towns

For stops of local residents, 75.9% of LEAs serving towns reported overrepresentation of Black drivers stopped compared to their proportion of the local driving population. Overrepresentation of Hispanic drivers was observed for 44.6% of town LEAs, compared to 8.9% for White drivers.

For searches of drivers, 24.4% of town agencies reported overrepresentation in searches of Black drivers and 20% reported overrepresentation in searches of Hispanic drivers compared to 17.4% for White drivers. The rates for Black and Hispanic driver continue a decreasing trend (roughly 10% and 5% per year, respectively), beginning in 2023.

For arrests of drivers following traffics stops conducted by LEAs serving towns, overrepresentation was comparable across Black drivers (21.7% of LEAs), Hispanic drivers (20% of LEAs), and White drivers (22.6% of LEAs).

Data on Complaints Alleging Excessive Use of Force

The Community Policing Act also directs DCJS to obtain data from VSP on “the prevalence of complaints alleging the use of excessive force." Use-of-force data is reported to VSP by local LEAs on the VSP SP-335 form. Use-of-force data reporting under HB 1250 began on July 1, 2020. DCJS examined the data that agencies reported to VSP for the period from January 1, 2025–December 31, 2025 (see Appendix M). Due to the limited amount of data reported, no analysis of the data is presented in this report. VSP and DCJS continue to examine future options for reporting use-of-force data to include an online data portal and repository. Therefore, the focus of the current report is on the analysis of traffic stop data.

Conclusions and Recommendation

As with the previous five reports, the overall finding of this analysis is that, statewide, Black and Hispanic drivers in Virginia were disproportionately stopped by law enforcement when compared to White drivers based on the number of drivers stopped relative to their numbers in Virginia’s driving-age population. This disparity was also observed for searches and arrests occurring after a stop, although to a much lesser extent.

Although this analysis identified disparities in traffic stop rates related to race/ethnicity, it does not allow us to determine or measure specific reasons for these disparities. Most importantly for this study, this analysis does NOT allow us to determine the extent to which these disparities may be due to bias-based profiling or other factors that can vary depending on race or ethnicity.

STANDING RECOMMENDATION: The percentages and Disparity Indexes (DIs) presented in this report should not be interpreted to indicate that any individual law enforcement agency is practicing bias-based profiling. Given the limitations noted above, these figures should only be used to identify where the numbers indicate that certain ethnic/racial groups are being disproportionately stopped, which may bear further review to identify why this is occurring and whether any action should be considered to reduce or eliminate it.

This is a standing recommendation given the limitations of the CPA’s current data fields. In addition, any year-to-year comparison of CPA findings should take into consideration both methodological differences and external factors involved in each year’s report.

RECOMMENDATION #16: DCJS reiterates a recommendation from the first Traffic Stop report submitted in 2022.

Collect data on the race/ethnicity, age, and gender of drivers involved in traffic accidents in each Virginia locality. (It would not be necessary to collect personally identifiable information on the driver, only the demographic data.) How and where this data would be collected and stored would need to be determined, but the data would need to be maintained in a way that would allow DCJS to compare it with traffic stop data for each locality.

During the 2023 data collection window, DCJS and VSP explored the possibility of this recommendation and determined that race/ethnicity data was currently unavailable from either the Virginia Department of Transportation, the Department of Motor Vehicles, or the State Police. Verifiable driver demographic information in combination with vehicle crash data is critical to establishing a driver population estimate for benchmarking traffic stop totals against. Population estimates are a crude measure by which to estimate who is actively using roadways. One option would be for DMV to collect race/ethnicity data for drivers and allow DCJS to access the Traffic Records Electronic Data System (TREDS) database and crossmatch driver data with vehicle accidents to create a driver population estimate.

RECOMMENDATION #17: DCJS could provide more context for the traffic stop analysis by publishing empirical papers addressing the following:

• Benchmark issues and comparison of data to multiple benchmarks

• Further analysis into searches and arrests of Hispanic pedestrian populations in Northern Virginia

• LEA staffing shortages and its effect on data reporting mandated by the CPA

In order to keep the traffic stop report and the pedestrian supplement at reasonable lengths, in-depth contextual information and comparable research will be the subject of a series of smaller papers which will allow for study of the bias issue while preserving the main report for data reporting purposes only.