Fourteenth Report to the State of Maryland
Under Transportation Article, § 25-113
2017 Race Based Traffic Stop Data Analysis
Larry Hogan
Governor
Boyd K. Rutherford
Lt. Governor
V. Glenn Fueston, Jr.
Executive Director
Governor’s Office of Crime Control & Prevention
Submitted by:
Governor’s Office of Crime Control & Prevention
Contact: Jeffrey Zuback
410-697-9344 | [email protected]
MSAR #10561
September 1, 2018
Introduction
In 2001, the Maryland Transportation Article, § 25-113 (TR, § 25-113) required the collection of
data on every law eligible traffic stop in Maryland in an effort to provide information on the
pervasiveness of racial profiling. Pursuant to TR, § 25-113, the Maryland Police and
1
Correctional Training Commission, in consultation with the Maryland Statistical Analysis Center
(The Center), developed four guiding documents that consisted of (1) a model recording and
reporting format; (2) a model policy for law enforcement agencies to address
race/ethnicity-based traffic stops; (3) guidelines for law enforcement agencies to manage,
counsel, and train officers who collect traffic stop data; and (4) a model log for law enforcement
agencies to record traffic stop data.
2
TR, § 25-113 also mandates state funding for the collection and analysis of data; however, no
funds have been received by law enforcement agencies or the The Center for the reporting of
traffic stop records. Regardless, and since 2002, law enforcement agencies within the State of
Maryland have complied with TR, § 25-113. Prior reports are accessible to the public and may
be viewed at the website below:
http://goccp.maryland.gov/reports-publications/law-enforcement-reports/traffic-stop-data/.
In 2011, the Governor’s Office of Crime Control & Prevention awarded grant funds to the
Maryland State Police to create a modification to the E-TIX (Electronic Traffic Information
Exchange) interface to include a data entry system for law enforcement agencies to submit traffic
stop records electronically. Through the use of free DeltaPlus software, law enforcement
agencies submit data on individual traffic stops which are stored by the Maryland State Police,
which maintains a repository on all traffic stop data in the state. Law enforcement agencies have
been submitting data through Delta+ since 2013.
In 2015, Senate Bill 413, Vehicle Laws - Race-Based Traffic Stops, Policy and Reporting
Requirements
, required The Center to continue to collect and analyze traffic stop data to provide
information on the pervasiveness of racial profiling. Pursuant to this legislation, The Center is
required to submit a report to the Governor and General Assembly by September 1st each year.
1
Racial profiling refers to the practice of constructing a set of characteristics or behaviors based on race and using
that set of characteristics to decide whether an individual might be guilty of some crime.
2
Previously known as the Maryland Justice Analysis Center, the Maryland Statistical Analysis Center resided within
the University of Maryland until 2006. Pursuant to Executive Order 01.01.2007.05, the Maryland Statistical
Analysis Center transferred within the Governor’s Office of Crime Control & Prevention in 2007.
2
Methodology
The 2017 Race Based Traffic Stop Data Analysis report presents aggregate data on all law
eligible traffic stops, within the State of Maryland, that law enforcement agencies reported to
The Center for the 2017 calendar year (January 1, 2017 through December 31, 2017). The
original data was submitted in Microsoft Excel and subsequently merged, standardized, and
analyzed using a widely accepted software package and used by researchers and social scientists.
The analysis of this report includes traffic stop data from 126 law enforcement agencies (n =
852,799 traffic stops). A full list of the law enforcement agencies that submitted traffic stop data
can be found in the Appendix: Agency Breakdown of Traffic Stops
. The units of analysis for
this report consist of all law eligible traffic stops that occurred in a Maryland jurisdiction
between January 1, 2017 and December 31, 2017. To that end, law eligible traffic stops are
defined as all stops made by law enforcement agencies that have the authority to issue traffic
violations. TR, § 25-113 excludes traffic stops that result from checkpoints or roadblocks, stops
of multiple vehicles after an accident or emergency, the use of radar, laser, vascar technology,
and license plate readers. Such stops are excluded because officer discretion is unlikely to play a
role and therefore any differences observed between races and minority populations would not
be the result of systematic differences in treatment due to race/ethnicity.
3
Required Data Elements
Pursuant to Senate Bill 413 (2015), law enforcement agencies are required to report the
following data elements:
Data Information
Units of Measure
Gender of driver
Male, Female
Race of driver
Asian, Black, Hispanic, Other, White
3
Driver date of birth
Date of birth
Agency that made the stop
Agency name
Time of day the stop occurred
0000 - 0800, 0800 - 1600, 1600 - 2400
Length of stop (in minutes)
0 - 5, 5 - 15, 15 - 30, 30 minutes or longer
Vehicle registration
In state, out of state
Driver county of residence
County
Reason for the stop
Traffic article
Type of search (if one was
conducted)
Person, property, both person and property
Reason for the search (if one was
conducted)
Consensual, incident to arrest, exigent circumstances,
probable cause, K-9, other
Outcome of search (if one was
conducted) (what was seized?)
Contraband, property, both contraband and property,
nothing seized
Outcome of the traffic stop
Warning, Safety Equipment Repair Order (SERO),
citation, arrest
4
Arrest reason (if an arrest occurred)
Based on the search, based on the stop, other
3
The demographic information of the driver in the traffic stop was determined using the officer’s observations, and
in some cases, supplemented with information from Maryland’s Motor Vehicle Administration (MVA). The statute
requires the use of the following: Asian, Black, White, Hispanic and Other; whereas, the MVA uses the following:
Black or African American, White, Asian, Native Hawaiian or Other Pacific Islander, American Indian, and Other.
4
The categories of this variable are mutually exclusive and were coded to reflect the most severe outcome of the
traffic stop. Therefore, if the traffic stop resulted in both a citation and an arrest, only arrest was coded.
4
Results
Between January 1, 2017 and December 31, 2017, law enforcement agencies reported 852,799
law eligible traffic stops. Table 1. Race/Ethnicity of Driver in Traffic Stops displays the
overall breakdown of the race/ethnicity of drivers involved in traffic stops. Information on
race/ethnicity was missing or could not be correctly classified in 7,528 traffic stops. As
illustrated below, the majority of drivers who were stopped during a traffic stop were White
(44.2%) or Black (40.1%). Also, 8.3% of all drivers stopped were Hispanic, and 2.7% were
Asian. In comparison, the U.S. Census Bureau 2017 data estimates that Maryland’s population
consisted of 50.9% non-Hispanic Whites, 30.8% Black or African Americans (alone), 6.7%
Asians (alone), and 10.1% Hispanics. An additional 4.0% of the drivers stopped in Maryland
were classified as “other” which consisted of American Indian and Alaskan Native, Native
Hawaiian and Other Pacific Islander, or two or more races, which would be reported in the
Motor Vehicle Administration (MVA) data as Other.
5
Table 1. Race/Ethnicity of Driver in Traffic Stops
Frequency
Percent
Asian
22,654
2.7%
Black
341,737
40.1%
Hispanic
70,391
8.3%
Other
33,886
4.0%
White
376,603
44.2%
Missing/Unknown
7,528
0.9%
Total
852,799
100.0%
Table 2. Gender of Driver in Traffic Stops displays the breakdown of the gender for all drivers
involved in traffic stops. Male drivers (62.9%) were stopped more frequently than female drivers
(35.7%). Unknown/missing gender data was found in 12,092 traffic stops (1.4%).
Table 2. Gender of Driver in Traffic Stops
Frequency
Percent
Female
304,461
35.7%
Male
536,246
62.9%
Unknown/Missing
12,092
1.4%
Total
852,799
100.0%
5
Population data is taken from U.S. Census Bureau, Population Estimates Program (PEP). The numbers are
predicted from the 2010 Census of Population, and updated as of July 1, 2018. They can be accessed at
https://www.census.gov/quickfacts/fact/table/md/PST045217.
5
Chart 1. Number of Traffic Stops by Month illustrates that the highest number of stops
occurred in March (79,296, 9.2%), whereas, the fewest number of stops occurred in December
(63,061, 7.3%).
The data in Chart 2. Time of Day of Traffic Stops (24hrs) displays statistics on the time that
each traffic stop occurred. The most common time interval of traffic stops occurred between the
hours of 4:00 p.m. and 12:00 a.m. (42.5%), followed by 8:00 a.m. and 4:00 p.m. (35.8%), and
12:00 a.m. and 8:00 a.m. (21.7%).
6
As illustrated in Chart 3. Length of Traffic Stops, the majority of traffic stops (78.6%) lasted
between zero and five minutes, and over 96% of stops lasted 15 minutes or less. A total of 1% of
stops lasted more than 30 minutes.
The registration of the vehicles stopped (i.e., in-state or out-of-state), stratified by the
race/ethnicity of the driver is displayed in Chart 4. Vehicle Registration by Driver’s
Race/Ethnicity. There was little variation by race/ethnicity as roughly 83% of all drivers had
in-state registration at the time of the stop. There was also little variance by gender for the
percent of drivers registered in-state at the time of the stop (82.0% for males, 85.5% for females).
7
As illustrated on the following pages, Table 3. Primary Initial Reason for Stop by Driver’s
Race/Ethnicity and Gender (Males) and Table 4. Primary Initial Reason for Stop by
Driver’s Race/Ethnicity and Gender (Females) display the initial reason for the traffic stop
provided by the officer and stratified by the driver’s race/ethnicity, for males and females
respectively. The data is broken by the total number of stops and the percent of all stops. The
totals do not equal all traffic stops due to missing gender data in 12,092 cases. Overall, patterns
6
were similar across race/ethnicity and gender with comparable frequencies for the primary initial
stop reason with a few exceptions. Males of all races/ethnicities were stopped most frequently
for traffic stop violations characterized as “other” ranging from 17.0% for Asian males to 23.4%
for Black males. Black males were stopped more frequently for registration violations (15.0%)
7
than the other races/ethnicities. Asian males were stopped more frequently for signs, signals, and
marking offenses (14.7%). Black males and White males were equally the most likely to be
stopped for an equipment violation (21.9% of all stops respectively).
Similar trends were also found with female drivers who were stopped most frequently for ”other”
traffic violations which ranged from 15.4% for Asian females to 24.3% for Black females. Black
females were the most likely to be stopped for registration violations (15.3%). Asian females
were stopped more frequently for signs, signals, and marking offenses (15.9%), and along with
all other races, were stopped the most frequently for moving violations (11.3% and 12.3%).
Black females and White females were the most likely to be stopped for an equipment violation
(21.1% and 20.1% of all stops respectively).
6
Traffic stop titles in this analysis include the following:
Title 13: Registration
Title 16: Drivers License
Title 21.11: Miscellaneous rules
Title 21.13: Operation of motorcycles
Title 21.14: Operation of vehicles on certain toll facilities
Title 21.2: Traffic signs, signals, and markings
Title 21.3: Driving on right side of roadway, overtaking and passing
Title 21.4: Right of way
Title 21.5: Pedestrians rights and rules
Title 21.6: Turning and starting, signals and stopping
Title 21.7: Special stops required
Title 21.8: Speed restrictions
Title 21.9: Reckless, negligent or impaired driving; fleeing or eluding police
Title 22: Equipment of vehicles
Title 24: Size, weight, and load
Code 99: All other stops
7
“All other stops” consists of various violations including, but not limited to: anti-theft laws, security, vehicle
rentals, accident reports and vehicle inspections.
8
Table 3. Primary Initial Reason for Stop by Driver’s Race/Ethnicity and Gender (Males)
Stop Reason
Asian
Black
Hispanic
Other
White
Unknown
/Missing
Total
Registration
1,592
11.1%
31,696
15.0%
5,763
10.8%
2,755
11.5%
28,009
12.2%
7
0.5%
69,822
13.06%
Driver’s License
135
0.9%
5,295
2.5%
1,488
2.8%
268
1.1%
3,053
1.3%
7
0.5%
10,246
1.92%
Miscellaneous Rules
1,104
7.7%
12,254
5.8%
4,412
8.3%
2,154
9.0%
21,889
9.5%
5
0.3%
41,818
7.8%
Operation of Motorcycle
6
0.0%
161
0.1%
23
0.0%
13
0.0%
281
0.1%
0
0.0%
484
0.1%
Tolls
17
0.1%
386
0.2%
109
0.2%
70
0.3%
467
0.2%
1
0.1%
1,050
0.2%
Signs, Signals, and
Markings
2,109
14.7%
19,896
9.4%
6,969
13.1%
3,163
13.2%
23,095
10.0%
19
1.3%
55,251
10.3%
Right side of roadway
813
5.7%
10,094
4.8%
2,950
5.6%
1,438
6.0%
12,260
5.3%
9
0.6%
27,564
5.2%
Right of way
576
4.0%
4,177
2.0%
1,497
2.8%
842
3.5%
5,833
2.5%
4
0.3%
12,929
2.4%
Pedestrians rights and
rules
91
0.6%
567
0.3%
239
0.5%
97
0.4%
1,025
0.5%
0
0.0%
2,019
0.4%
Turning, Signals, and
Stopping
126
0.9%
1,795
0.9%
587
1.1%
159
0.7%
1,863
0.8%
1
0.1%
4,531
0.9%
Special Stops
1,041
7.3%
7,320
3.5%
2,455
4.6%
1,038
4.3%
12,049
5.2%
4
0.3%
23,907
4.5%
Speed Restrictions
1,886
13.2%
19,825
9.4%
5,078
9.6%
3,138
13.1%
25,498
11.1%
19
1.3%
55,444
10.4%
Reckless or Impaired
Driving; Fleeing or
Eluding Police
118
0.8%
1,891
0.9%
775
1.5%
197
0.8%
3,076
1.3%
1
0.1%
6,058
1.1%
Equipment
2,270
15.8%
46,354
21.9%
9,695
18.2%
4,287
17.9%
50,297
21.2%
402
27.2%
11,305
21.2%
Size, Weight, and Load
13
0.1%
308
0.2%
165
0.3%
50
0.2%
410
0.2%
0
0.0%
946
0.2%
All Other Violations
2,432
17.0%
49,563
23.4%
10,945
20.6%
4,272
17.8%
41,012
17.8%
998
67.6%
109,222
20.4%
Total
Percent
14,329
100.0%
211,582
100.0%
53,150
100.0%
23,941
100.0%
230,117
100.0%
1,477
100.0%
534,596
100.0%
9
Table 4. Primary Initial Reason for Stop by Driver’s Race/Ethnicity and Gender (Females)
Stop Reason
Asian
Black
Hispanic
Other
White
Unknown
/Missing
Total
Registration
853
10.4%
18,979
15.3%
1,691
10.0%
1,112
11.8%
17,004
11.8%
4
0.6%
39,643
13.1%
Driver’s License
56
0.7%
2,215
1.8%
365
2.2%
106
1.1%
1,747
1.2%
3
0.5%
4,492
1.5%
Miscellaneous Rules
756
9.2%
9,064
7.3%
1,679
10.0%
1,100
11.7%
17,672
12.3%
1
0.2%
30,272
10.0%
Operation of Motorcycle
0
0.0%
13
0.0%
3
0.0%
0
0.0%
30
0.0%
0
0.0%
46
0.0%
Tolls
14
0.2%
168
0.1%
14
0.1%
12
0.1%
149
0.1%
0
0.0%
357
0.1%
Signs, Signals, and
Markings
1,308
15.9%
11,160
9.0%
2,440
14.5%
1,146
12.2%
13,952
9.7%
4
0.6%
30,010
10.0%
Right side of roadway
503
6.1%
5,478
4.4%
870
5.2%
515
5.5%
6,752
4.7%
2
0.3%
14,120
4.7%
Right of way
382
4.7%
2,897
2.3%
626
3.7%
396
4.2%
4,205
2.9%
0
0.0%
8,506
2.8%
Pedestrians rights and
rules
57
0.7%
322
0.3%
111
0.7%
62
0.7%
556
0.4%
0
0.0%
1,108
0.4%
Turning, Signals, and
Stopping
68
0.8%
869
0.7%
151
0.9%
63
0.7%
1,044
0.7%
0
0.0%
2,195
0.7%
Special Stops
700
8.5%
4,975
4.0%
1,137
6.7%
534
5.7%
9,060
6.3%
0
0.0%
16,406
5.4%
Speed Restrictions
926
11.3%
11,141
9.0%
1,512
9.0%%
1,161
12.3%
15,190
10.5%
7
1.1%
29,937
10.0%
Reckless or Impaired
Driving; Fleeing or
Eluding Police
57
0.7%
634
0.5%
120
0.7%
55
0.6%
1,250
0.9%
1
0.2%
2,117
0.7%
Equipment
1,258
15.3%
26,199
21.1%
2,932
17.4%
1,536
16.3%
29,077
20.2%
211
34.0%
61,213
20.2%
Size, Weight, and Load
5
0.1%
12
0.0%
7
0.0%
0
0.0%
27
0.0%
0
0.0%
51
0.0%
All Other Violations
1,261
15.4%
30,161
24.3%
3,207
19.0%
1,634
17.3%
26,503
18.4%
388
62.5%
63,154
20.8%
Total
Percent
8,204
100.0%
124,287
100.0%
16,865
100.0%
9,432
100.0%
144,218
100.0%
621
100.0%
303,627
100.0%
10
Chart 5. Search Conducted by Driver’s Race/Ethnicity displays the breakdown of the total
number of searches conducted which was stratified by the race/ethnicity of the driver, and
stratified by gender. There were 24,928 conducted searches with a valid search type, representing
2.9% of all traffic stops. Males were twice as likely to be searched as females (3.6% compared to
1.8%). Black males (5.5%) were significantly more likely to be searched than any other race.
Females were searched at relatively similar rates across race/ethnicity (0.5% - 2.1%).
11
Chart 6. Type of Search Conducted by Driver’s Race/Ethnicity (Males) and Chart 7. Type
of Search Conducted by Driver’s Race/Ethnicity (Females) display the types of searches
conducted (i.e., person or property) with regards to the race/ethnicity of the driver and
disaggregated by gender. Of the 24,928 searches conducted in 2017, the majority for males and
females of all races/ethnicities, consisted of both person and property (69.5% - 77.5% for males
and 67.2% - 75.0% for females). Asian males and Hispanic males were the most likely to have
only their person searched (15.2% and 15.5% respectively). Asian males were less likely than
other races/ethnicities to have just their property searched (9.2%). Black females were the least
likely to have only their person searched (5.5%) and were the most likely to have only their
property searched (27.2%).
12
Chart 8. Type of Search Conducted by Driver’s Race/Ethnicity (Males) and Chart 9. Type
of Search Conducted by Driver’s Race/Ethnicity (Females) display the reason for the search
of the driver’s person or property, provided by the officer. Search incident to arrest (SIR),
probable cause, driver’s consent, and K-9 searches were the four most prevalent search reasons
for males and females across all race/ethnicities. Black males and Other males were more likely
to be searched due to probable cause (61.8% and 51.8%). Black males were the least likely to
have a consensual search conducted (7.6%). Hispanic males and Asian males were the most
likely to be searched incident to arrest (39.2% and 33.6%). White males were more likely to have
a K-9 search conducted than all other races/ethnicities (16.0%).
The search reason trends varied slightly for females. Black females were the most likely to be
searched due to probable cause (65.0%), and along with other non-white females, were the least
likely to have a consensual search (4.8% and 5.6%, respectively). Hispanic females were the
most likely to be searched incident to arrest (35.6%), followed by Asian females (35.0%). White
females were the most likely to have a consensual search (12.7%) and a K-9 search (20.4%).
13
Table 5. Search Disposition by Driver’s Race/Ethnicity (Males) and Table 6. Search
Disposition by Driver’s Race/Ethnicity (Females) display the search disposition stratified by
race/ethnicity and collapsed across gender. The data is broken by the total number of searches
and the percent of all searches. Of those searches where a search disposition was included, the
majority of males and females had nothing confiscated (58.3% and 56.2%, respectively). Of
those searches where a disposition was reported, Hispanic males (25.2%) were the least likely to
have contraband seized, but the most likely to have property seized (9.6%). White males (37.8%)
were the most likely to have contraband only seized.
There was a somewhat similar trend experienced for females. In fact, Asian females were the
least likely to have contraband seized (25.0%). White females and Other females were the most
likely to have just contraband seized (39.0% and 40.5%). Asian females were the least likely to
have contraband and property seized (4.3%).
Table 5. Search Disposition by Driver’s Race/Ethnicity (Males)
Search Disposition
Asian
Black
Hispanic
Other
White
Unknown
/Missing
Total
Contraband
78
35.9%
3,277
32.1%
452
25.2%
141
34.0%
2,551
37.8%
0
0.0%
6,499
33.5%
Property
11
5.1%
670
6.6%
172
9.6%
20
4.8%
362
5.4%
0
0.0%
1,235
6.4%
Contraband &
Property
7
3.2%
756
7.4%
125
7.0%
27
6.5%
468
6.9%
0
0.0%
1,383
7.2%
Nothing
121
55.8%
5,504
53.9%
1,045
58.3%
227
54.7%
3,373
49.9%
7
100.0%
10,277
53.0%
Total
Percent
217
100.0%
10,207
100.0%
1,794
100.0%
415
100.0%
6,754
100.0%
7
100.0%
19,394
100.0%
Table 6. Search Disposition by Driver’s Race/Ethnicity (Females)
Search Disposition
Asian
Black
Hispanic
Other
White
Unknown
/Missing
Total
Contraband
10
25.0%
771
32.3%
64
25.6%
36
40.5%
1,081
39.0%
1
50.0%
1,963
35.47%
Property
5
12.5%
174
7.3%
30
12.0%
1
1.1%
137
5.0%
0
0.0%
347
6.27%
Contraband &
Property
4
10.0%
130
5.5%
21
8.4%
2
2.3%
187
6.8%
0
0.0%
344
6.2%
Nothing
21
52.5%
1,309
54.9%
135
54.0%
50
56.2%
1,365
49.3%
1
50.0%
2,881
52.1%
Total
Percent
40
100.0%
2,384
100.0%
250
100.0%
89
100.0%
2,770
100.0%
2
100.0%
5,535
100.0%
14
Chart 10. Traffic Stop Outcome by Driver’s Race/Ethnicity (Males) and Chart 11. Traffic
Stop Outcome by Driver’s Race/Ethnicity (Females) specify the outcome of each traffic stop
by race/ethnicity disaggregated by gender. Missing data was apparent in the outcome of 26,469
traffic stops. Statistics indicate that males were slightly more likely to receive a citation than
females (28.4% compared to 23.4%). Conversely, males were less likely to receive a warning
than females (59.8% compared to 64.6%). Receiving a warning, written or verbal, was the most
common outcome for males ranging from 52.3% for Hispanics to 64.4% for Whites. Conversely,
Hispanic males were more likely to receive a citation (35.8%) than all other race/ethnicities.
Also, the probability of an arrest ranged from 0.6% for Other males to 1.8% for Hispanic males.
Similarly, the most common outcome for females was a warning which ranged from 59.8% for
Hispanic females to 68.0% for White females. Hispanic females were the most likely to receive a
citation at 29.1%. The probability of an arrest was under 1% across all races/ethnicities.
15
Restricting the analysis to only those cases in which the traffic stop resulted in an arrest, Table 7.
Reason for Arrest by Driver’s Race/Ethnicity and Gender (Males) and Table 8. Reason for
Arrest by Driver’s Race/Ethnicity and Gender (Females) present the reason of the officer for
the arrest delineated by the driver’s race/ethnicity and gender. The data is broken by the total
number of arrests and the percent of all arrests with a stop reason. An arrest reason was missing
in 38 cases.
The most common arrest reason for all ethnic groups for both males and females were based on
the stop, ranging from 41.4% for Black males to 57.9% for Asian males, and 41.6% for Black
females to 53.3% for Hispanic females. Black males and Black females were the least likely to
be arrested based on the stop (41.4% and 41.6%). Hispanic females and Asian females were
equally the least likely to be arrested based on the search (26.3%). Hispanic males were more
likely to be arrested for an “other” reason (23.2%) and Other non-white females were more likely
to be arrested for an “other” reason (29.7%).
Table 7. Reason for Arrest by Driver’s Race/Ethnicity and Gender (Males)
Arrest
Reason
Asian
Black
Hispanic
Other
White
Missing/
Unknown
Total
Based on
Search
23
22.6%
1,265
36.2%
185
19.4%
48
34.3%
975
34.8%
1
33.3%
2,497
33.3%
Based on
Stop
59
57.9%
1,447
41.4%
546
57.3%
74
52.9%
1,374
49.0%
0
0.0%
3,500
46.7%
Other
20
19.6%
780
22.3%
221
23.2%
18
12.9%
454
16.2%
2
66.7%
1,495
20.0%
Total
Percent
102
100.0%
3,492
100.0%
952
100.0%
140
100.0%
2,803
100.0%
3
100.0%
7,492
100.0%
Table 8. Reason for Arrest by Driver’s Race/Ethnicity and Gender (Females)
Arrest
Reason
Asian
Black
Hispanic
Other
White
Missing/
Unknown
Total
Based on
Search
5
26.3%
239
33.6%
36
26.3%
14
37.8%
427
36.8%
1
100.0%
722
35.0%
Based on
Stop
10
52.6%
296
41.6%
73
53.3%
12
32.4%
533
46.0%
0
0.0%
924
44.8%
Other
4
21.1%
176
24.8%
28
20.4%
11
29.7%
200
17.2%
0
0.0%
419
20.3%
Total
19
100.0%
711
100.0%
137
100.0%
37
100.0%
1,160
100.0%
1
100.0%
2,065
100.0%
Percent
16
Discussion
Conclusions regarding the relationships between race/ethnicity and traffic stops should be
cautiously interpreted and carefully utilized. In fact, the race and ethnic categories required under
TR, § 25-113 differ from the race and ethnic categories used by the MVA. These differences can
create inconsistencies in the data. To overcome this limitation, the TR, § 25-113 and MVA
definitions should be consistent.
The major limitation of the current study pertains to the possibility of omitted variables that may
account for any differences observed between race/ethnicities. The purpose of this report is to
discover whether drivers who exhibit similar behaviors, but are of different race/ethnicities, are
stopped at different rates and whether the traffic stops result in different treatment and outcomes.
However, the current method allows the possibility of error by neglecting confounding variables,
such as driving behavior, the driver’s violation history, and law enforcement deployment. If
temporal and spatial traveling patterns differ by race/ethnicity, any differences observed may be
the result of these driving patterns and not systematic differences between race/ethnicities.
Considering that it is unknown whether traveling behavior and patterns differ by race/ethnicity,
no statistical conclusions can be drawn regarding whether there is differential treatment.
No definitive conclusions can be drawn from this report regarding the effect of race/ethnicity on
the frequency or characteristics associated with traffic stops due to data limitations beyond the
scope of what reporting agencies could provide.
17
Appendix: Agency Breakdown of Traffic Stops
Agency
Number
of Stops
Agency
Number
of Stops
Aberdeen Police Department
1,584
Coppin State University Police
60
Allegany County Sheriff's Office
793
Cottage City Police Department
217
Annapolis Police Department
3,030
Crisfield Police Department
29
Anne Arundel Community College Public Safety
& Police
444
Crofton Police Department
104
Anne Arundel County Police Department
49,123
Cumberland City Police Department
1,668
Anne Arundel County Sheriff's Office
12
Delmar Police Department
466
Baltimore City Sheriff's Office
183
Denton Police Department
309
Baltimore County Police Department
69,235
Department of General Services
608
Baltimore Environmental Police
276
District Heights Police Department
320
Baltimore Police Department
62,689
Dorchester County Sheriff's Office
780
Bel Air Police Department
1,919
Easton Police Department
3,697
Berlin Police Department
457
Edmonston Police Department
580
Berwyn Heights Police Department
2
Elkton Police Department
1,736
Bladensburg Police Department
1,048
Federalsburg Police Department
488
Boonsboro Police Department
900
Forest Heights Police Department
741
Bowie Police Department
3,335
Fort Detrick Police
437
Bowie State University Police
18
Fort Meade Police
342
Brentwood Police Department
292
Frederick County Sheriff's Office
25,019
Brunswick Police Department
918
Frederick Police Department
10,313
Calvert County Sheriff's Office
7,450
Frostburg City Police Department
548
Cambridge Police Department
3,707
Frostburg State University Police
289
Capitol Heights Police Department
215
Fruitland Police Department
4,525
Caroline County Sheriff's Office
1,916
Gaithersburg Police Department
5,465
Carroll County Sheriff's Office
4,719
Garrett County Sheriff's Office
477
Cecil County Sheriff's Office
5,783
Glenarden Police Department
312
Centreville Police Department
466
Greenbelt Police Department
2,731
Charles County Sheriff's Office
12,705
Greensboro Police Department
145
Chestertown Police Department
1,631
Hagerstown Police Department
2,376
Cheverly Police Department
2,072
Hampstead Police Department
573
Chevy Chase Village Police Department
537
Hancock Police Department
371
Colmar Manor Police Department
4
Harford County Sheriff's Office
10,933
Comptroller of Maryland
125
Havre de Grace Police Department
689
18
Agency
Number
of Stops
Agency
Number
of Stops
Howard County Police Department
22,341
Ridgely Police Department
204
Hurlock Police Department
534
Rising Sun Police Department
526
Hyattsville Police Department
2,693
Riverdale Park Police Department
1,596
Kent County Sheriff's Office
3,963
Rock Hall Police Department
104
La Plata Police Department
1,941
Rockville Police Department
5,276
Landover Police Department
384
Salisbury Police Department
3,504
Laurel Police Department
5,549
Salisbury University Police Department
100
Manchester Police Department
16
Seat Pleasant Police Department
1,671
Maryland Motor Vehicle Administration
25
Smithsburg Police Department
61
Maryland State Police
233,482
Snow Hill Police Department
165
Maryland Transit Administration
453
Somerset County Sheriff's Office
472
Maryland Transportation Authority Police
53,446
St. Mary’s County Sheriff’s Office
3,863
Maryland-National Capital Park Police
Montgomery County
4,554
St. Michaels Police Department
509
Maryland-National Capital Park Police Prince
George's County
2,547
Sykesville Police Department
1,271
Montgomery County Police Department
78,400
Takoma Park Police Department
2,531
Montgomery County Sheriff's Office
724
Talbot County Sheriff's Office
2,190
Morningside Police Department
725
Taneytown Police Department
767
Mount Rainier Police Department
462
Thurmont Police Department
1,008
Mt. Airy Police Department
149
Towson University Police
583
Natural Resources Police
4,757
Trappe Police Department
3
New Carrollton Police Department
763
University of Baltimore Police
181
Oakland Police Department
57
University of Maryland Baltimore County Police
464
Ocean City Police Department
9,112
University of Maryland Baltimore Police
742
Ocean Pines Police Department
1,173
University of Maryland Eastern Shore
77
Oxford Police Department
17
University of Maryland Police College Park
7,293
Perryville Police Department
606
University Park Police
301
Pocomoke City Police Department
1,001
Upper Marlboro Police Department
126
Prince George's County Police Department
48,402
Washington County Sheriff's Office
6,409
Prince George's County Sheriff's Office
123
Westminster Police Department
3,196
Princess Anne Police Department
1,026
Wicomico County Sheriff's Office
4,990
Queen Anne's County Sheriff's Office
3,338
Worcester County Sheriff's Office
4,917
19
