Research has verified that people of color are more often stopped than whites. Researchers have been working to figure out how much of this disparity is because of discrimination and how much is due to other factors, but untangling these other factors is challenging:
- Differences in driving patterns. The representation of minority drivers among those stopped could differ greatly from their representation in the residential census. Naturally those driving on the road, particularly major thoroughfares, could differ from those who live in the neighborhood. As a result, social scientists now disregard comparisons to the census for assessing racial bias. 
- Differences in exposure to the police. If minority drivers tend to drive in communities where there are more police patrols then the police will be more likely to notice any infractions the black drivers commit. Having more intense police patrols in these areas could be a source of bias or it could simply be the police department's response to crime in the neighborhood.
- Differences in offending. Seatbelt usage is chronically lower among black drivers.  If a law enforcement agency aggressively enforces seatbelt violations, police will stop more black drivers.
What is clear from the research is that race is a consistent predictor of attitudes toward the police. Hence, some researchers argue that what happens during the stop is as important as the reason for it. So, in addition to questions about bias in the decision to initiate a stop, questions have been asked about bias in other aspects of the traffic stop: the length of the stop and the decision to cite, search or use force. Furthermore, researchers are exploring whether bias, if it exists, is a department-wide culture or isolated in certain units or a select few problem officers. Resolving each of these questions requires different data sources and different methodological approaches.
Below is a sample of research about traffic stops. The studies highlight various approaches researchers have taken to assess racial profiling in traffic stops:
- Several studies have searched for replacements for the residential census as a benchmark. One study used data on the location of traffic accidents and the race of the not-at-fault driver to get a better handle on the racial makeup of drivers in each community. Other researchers have found “race-blind” sources for learning the racial makeup of drivers, such as speed-triggered cameras,  drivers cited by photographic stoplight enforcement cameras,  and aerial patrols . Comparing changes in the racial makeup of drivers stopped before and after Daylight Saving Time has also been used. This method takes advantage of the abrupt change in the officers' ability to see the race of the drivers in advance of the stop. 
- In Savannah, Ga., trained observers accompanied police officers on 132 tours and focused on officers' decision-making and discretion prior to a traffic stop. Officers were questioned every time a person aroused their suspicions. Of those who evoked suspicion, 74 percent were male and 71 percent were minorities. Suspicious behavior, a traffic offense, “looking nervous” or similar behavior accounted for 66 percent of the officers' reactions; 18 percent were the result of information they had received to be on the lookout for a suspect; 10 percent because someone was where he or she would not be expected to be; and 6 percent because of the person's appearance. Officers stopped individuals under suspicion 59 percent of the time, but the suspect's race did not affect the outcome of the stop. The authors concluded that the results did not support the perception that a high level of discrimination occurs prior to a traffic stop. 
- Racial bias, if present, could be the result of a few problem officers in a department of otherwise race-neutral officers. Researchers have suggested creating benchmarks for individual officers to identify those detaining disproportionately more minority drivers than their peers. 
- A study in Cincinnati found that black drivers had longer stops and higher search rates than white drivers. However, when the researchers matched stops involving black drivers with similarly situated white drivers, those stopped at the same time, place, and context (reason for the stop, validity of the driver's license, etc.), they found no differences. Their conclusion was that differences in the time, place, and context of the stops were the cause of the longer stops and higher search rates. 
[note 1] See, for example, Smith, William R., Donald Tomaskovic-Devey, Matthew T. Zingraff, H. Marcinda Mason, Patricia Y. Warren, and Cynthia Pfaff Write, The North Carolina Highway Traffic Study (pdf, 407 pages), Final report to the National Institute of Justice, grant number 1999-MU-CX-0022, Washington, D.C.: U.S. Department of Justice, Office of Justice Programs, National Criminal Justice Reference Service, January 2004, NCJ 204021.
[note 2] See, for example, the discussion of benchmarks in Ridgeway, Greg, and John MacDonald, “Methods for Assessing Racially Biased Policing,” in Race, Ethnicity, and Policing: New and Essential Readings, ed. Stephen K. Rice and Michael D. White, New York: NYU Press, 2010: 180–204.
[note 3] Pickreall, Timothy M. and Tony Jianqiang Ye, Seat Belt Use in 2008 — Race and Ethnicity Results Among Occupants Traveling With Children, Traffic Safety Facts: Research Note, Washington, D.C.: U.S. Department of Transportation, National Highway Traffic and Safety Administration, National Center for Statistics and Analysis, April 2009.
[note 4] Alpert, Geoffrey P., Michael R. Smith, and Roger G. Dunham, “Toward a Better Benchmark: Assessing the Utility of Not-at-Fault Traffic Crash Data in Racial Profiling Research,” paper presented at Confronting Racial Profiling in the 21st Century: Implications for Racial Justice, Boston, 2003.
[note 5] Lange, James E., Kenneth O. Blackman, and Mark B. Johnson, “Speed Violation Survey of the New Jersey Turnpike: Final Report,” submitted to the Office of the Attorney General of New Jersey, 2001.
[note 6] Montgomery County, Maryland, Department of Police, “Traffic Stop Data Collection Analysis: Third Report, 2002.
[note 7] McConnell, E. H., and A. R. Scheidegger, “Race and Speeding Citations: Comparing Speeding Citations Issued by Air Traffic Officers With Those Issued by Ground Traffic Officers,” paper presented at the annual meeting of the Academy of Criminal Justice Sciences, Washington, D.C., April 2001, 4–8.
[note 8] Grogger, Jeffrey, and Greg Ridgeway, “Testing for Racial Profiling in Traffic Stops from Behind a Veil of Darkness,” Journal of the American Statistical Association 101 (2006): 878–887; Worden, Robert E., Sarah J. McLean, and Andrew P. Wheeler, “Testing for Racial Profiling With the Veil-of-Darkness Method,” Police Quarterly 15 (March 2012): 92–111.
[note 9] Alpert, Geoffrey P., Roger G. Dunham, Meghan Stroshine, Katherine Bennett, and John MacDonald, Police Officers' Decision Making and Discretion: Forming Suspicion and Making a Stop (pdf, 151 pages), Final report to the National Institute of Justice, grant number 2001-IJ-CX-0035, February 2006, NCJ 213004.
[note 10] Walker, Samuel, “Searching for the Denominator: Problems with Police Traffic Stop Data and an Early Warning System Solution,” Justice Research and Policy 3 (2001): 63–95.
[note 11] Schell, Terry L., Greg Ridgeway, Travis L. Dixon, Susan Turner, and K. Jack Riley, Police-Community Relations in Cincinnati: Year Three Evaluation Report, Santa Monica, Calif.: RAND, 2007.