Monthly Traffic Safety Analysis

641 CRASHES IN
MONTGOMERY, MD
MARCH 2021

All metrics benchmarked againstMarch 2020

In March 2021, Montgomery County recorded 641 total traffic crashes, a slight decrease from the 654 crashes reported in March 2020. Despite this 2.0% drop in overall incidents, the number of people injured increased by 16.1%, rising from 205 to 238. Fatalities also rose, from 3 in the prior period to 4 in the current period.

641

-2.0%was 654

Total Crash Events

4

33.3%was 3

Persons Killed

238

16.1%was 205

Persons Injured

137

12.3%was 122

Hit-and-Run Crashes

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 5 crashes with unreported severity are not shown in the severity breakdown.

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2021-03-01 to 2021-03-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Year-over-year, total crashes in Montgomery County saw a minor decline of 2.0%, from 654 in March 2020 to 641 in March 2021. However, this decrease in volume was accompanied by an increase in severity, as total injuries rose 16.1% from 205 to 238 and total fatalities increased from 3 to 4.

137

Hit-and-Run Crashes — March 2021

12.3% vs prior (122)

The number and rate of hit-and-run incidents trended upward. In March 2021, there were 137 hit-and-run crashes, an increase from 122 in March 2020. This represents a rise in the hit-and-run rate from 18.7% to 21.4% of all crashes.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 10.0%

0

Cyclists Killed

Prior: 00.0%

3

Motorists Killed

Prior: 250.0%

0

Other Killed

Prior: 00.0%

13

Pedestrians Injured

Prior: 21-38.1%

4

Cyclists Injured

Prior: 8-50.0%

220

Motorists Injured

Prior: 17525.7%

1

Other Injured

Prior: 10.0%

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2021-03-01 to 2021-03-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes remained consistent between the two periods. Friday was the day with the most crashes in both March 2021 (123 crashes) and March 2020 (110 crashes). Similarly, the 3 p.m. hour was the peak time for collisions in both years, accounting for 52 crashes in the current period and 60 in the prior.

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2021-03-01 to 2021-03-31 · Crash date field aggregated by weekday

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2021-03-01 to 2021-03-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity increased from March 2020 to March 2021. The fatal crash rate rose from 0.46% to 0.78%, with fatalities increasing from 3 to 4. The proportion of crashes involving serious injuries also grew from 1.5% (10 incidents) to 2.5% (16 incidents). Consequently, the share of crashes with no injuries decreased from 72.2% to 68.3%.

Severity is per crash event (most severe injury). 5 fatal crash events resulted in 4 persons killed.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.6%
33.3%prior 3
Serious Injury16serious injury crashes2.5%
60.0%prior 10
Minor Injury74minor injury crashes11.5%
8.8%prior 68
Possible Injury104possible injury crashes16.2%
8.3%prior 96
No Injury438no injury crashes68.3%
-7.2%prior 472

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2021-03-01 to 2021-03-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2021-03-01 to 2021-03-31 · Most severe injury per crash record

Top Contributing Factors

Environmental conditions were the top contributing factors in both periods, though their reported frequency decreased. The count of crashes attributed to 'N/A, WET' conditions fell from 43 in March 2020 to 15 in March 2021. Similarly, crashes with 'RAIN, SNOW, WET' as a factor decreased from 38 to 29. These two factors remained the top two contributors but swapped ranks year-over-year.

Officer-Reported Primary Contributing Cause

RAIN, SNOW, WET29 (4.5%)-23.7%prior 38
N/A, WET15 (2.3%)-65.1%prior 43
N/A, VISION OBSTRUCTION (INCL. BLINDED BY SUN)3 (0.5%)
V WIPERS|W OTHER ENVIRONMENTAL, WET3 (0.5%)
SLEET, HAIL, FREEZ. RAIN, WET3 (0.5%)
ICY OR SNOW-COVERED, N/A2 (0.3%)
DEBRIS OR OBSTRUCTION, N/A2 (0.3%)
N/A, V EXHAUST SYSTEM|R OTHER ROAD2 (0.3%)
ANIMAL, N/A1 (0.2%)
BACKUP DUE TO PRIOR CRASH, N/A1 (0.2%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2021-03-01 to 2021-03-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

A notable shift occurred in road surface conditions associated with crashes. The proportion of collisions on wet roads decreased significantly, from 18.5% (121 crashes) in March 2020 to 9.5% (61 crashes) in March 2021. Correspondingly, crashes on dry roads made up a larger share of the total, increasing from 65.6% to 77.2%. Crashes in clear weather also increased as a proportion of the total, from 66.7% to 76.1%.

Weather

Clear488 (84.4%)
11.9%prior 436
Rain52 (9.0%)
-37.3%prior 83
Cloudy31 (5.4%)
-61.7%prior 81
Fog, Smog, Smoke3 (0.5%)
Other2 (0.3%)
Severe Crosswinds1 (0.2%)
Wintry Mix1 (0.2%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2021-03-01 to 2021-03-31 · Weather condition at time of crash

Lighting

Daylight394 (63.1%)
-8.8%prior 432
Dark - Lighted169 (27.1%)
11.2%prior 152
Dark - Not Lighted22 (3.5%)
-12.0%prior 25
Dusk20 (3.2%)
42.9%prior 14
Dawn15 (2.4%)
15.4%prior 13
Dark - Unknown Lighting3 (0.5%)
-66.7%prior 9
Other1 (0.2%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2021-03-01 to 2021-03-31 · Lighting condition field

Road Surface

Dry495 (88.7%)
15.4%prior 429
Wet61 (10.9%)
-49.6%prior 121
Ice/Frost2 (0.4%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2021-03-01 to 2021-03-31 · Road surface condition field

Vehicles & Demographics

Passenger cars were the most common vehicle type in crashes for both periods, with counts remaining relatively stable at 799 in March 2021 versus 780 in the prior year. The involvement of (Sport) Utility Vehicles in crashes increased from 84 to 103. Regarding vehicle makes, Toyota and Honda were the top two in both years, swapping positions; Toyota led with 175 vehicles in 2021, whereas Honda led with 136 in 2020.

Top Vehicle Makes (1,133 vehicles)

1
TOYOTA175 (15.4%)
29.6%prior 135
2
HONDA134 (11.8%)
-1.5%prior 136
3
FORD98 (8.6%)
-15.5%prior 116
4
NISSAN65 (5.7%)
27.5%prior 51
5
TOYT54 (4.8%)
3.8%prior 52
6
HOND36 (3.2%)
-14.3%prior 42
7
CHEV34 (3%)
25.9%prior 27
8
HYUNDAI30 (2.6%)
66.7%prior 18
9
DODGE29 (2.6%)
-21.6%prior 37
10
JEEP27 (2.4%)
35.0%prior 20

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2021-03-01 to 2021-03-31 · Vehicle unit records

Data Sources & Methodology

Primary Data Source

All crash data in this report is sourced from Montgomery County Crash Reporting (ACRS) (https://data.montgomerycountymd.gov/d/bhju-22kf), accessed programmatically via the Socrata Open Data API (SODA). This dataset contains official police-reported motor vehicle traffic crash records maintained by the reporting jurisdiction's law enforcement agency. Records are published to the open data portal by the municipality and are subject to the portal's terms of use.

Data Retrieval

  • Access method: Socrata Open Data API (SoQL queries)
  • Dataset URL: https://data.montgomerycountymd.gov/d/bhju-22kf
  • Data format: Structured JSON via REST API
  • Record types queried: Crash events, person records, and vehicle unit records
  • Date filter applied: 2021-03-01 through 2021-03-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2021-03-01 through 2021-03-31 (31 days)
  • Geographic scope: montgomery, MD
  • Total crash records analyzed: 641
  • Total persons involved: 1,153
  • Total vehicles involved: 1,133

Analytical Methodology

  • Severity classification: Uses the KABCO injury scale (K=Fatal, A=Incapacitating injury, B=Non-incapacitating injury, C=Possible injury, O=No injury/property damage only), the standard classification in U.S. Model Minimum Uniform Crash Criteria (MMUCC). Severity is assigned per crash event based on the most severe injury in that crash. A single fatal crash (K) may involve multiple fatalities; therefore the "Persons Killed" count in the headline KPIs may differ from the "Fatal" crash count in the severity breakdown.
  • Contributing factors: Reflect the officer-determined primary contributory cause recorded at the time of the crash report. These are preliminary determinations and may not reflect final investigation findings.
  • Hit-and-run classification: Based on the hit-and-run indicator field in the official crash report, as determined by the responding officer at the scene.
  • Temporal analysis: Day-of-week and hour-of-day distributions are computed from the crash date/time timestamp in each record.
  • Demographics: Age and sex distributions are drawn from person-level records linked to each crash event. A single crash may involve multiple persons.
  • Vehicle data: Make information is drawn from vehicle unit records linked to each crash event.
  • AI commentary: Narrative sections are generated by Google Gemini (large language model) based on the structured data. Commentary is descriptive, not predictive, and should not be interpreted as expert opinion.

Limitations & Disclaimers

  • Only crashes reported to and documented by law enforcement are included. Minor incidents, unreported crashes, and near-misses are not captured in this dataset.
  • Data reflects conditions at the time of the initial police report and may be subject to subsequent corrections, reclassifications, or supplements by the reporting agency.
  • Open data portal records may experience a publication lag - recently occurring crashes may not yet appear in the dataset at the time of report generation.
  • AI-generated commentary is produced by a large language model and is intended to highlight patterns in the data. It does not constitute legal, medical, or professional analysis.
  • Percentages are calculated from reported data and are subject to rounding.

Non-Affiliation Disclosure

This report is produced independently by ThatCarHitMe.com (Injuria.ai). It is not affiliated with, endorsed by, or produced in partnership with any law enforcement agency, municipal government, state department of transportation, or the National Highway Traffic Safety Administration (NHTSA). Data is sourced from publicly available government open data portals.

Data License

The underlying crash data is provided under the municipality's Open Data Terms of Use and is made available to the public for unrestricted use. This analysis and report is © 2026 Injuria.ai and may be cited with attribution using the suggested citation below.

Corrections & Feedback

If you believe any data in this report is inaccurate or have questions about our methodology, please contact: data@injuria.ai. We are committed to accuracy and will issue corrections promptly.

Suggested Citation

ThatCarHitMe.com (Injuria.ai). "montgomery, MD Crash Intelligence Report: March 2021." Published September 9, 2026. Reporting period: 2021-03-01 to 2021-03-31. Data source: Montgomery County Crash Reporting (ACRS), Socrata Open Data. Dataset: https://data.montgomerycountymd.gov/d/bhju-22kf. Available at: https://thatcarhitme.com/crash-data/maryland/statewide/march-2021-report

About the Publisher

ThatCarHitMe.com is a crash data intelligence platform developed by Injuria.ai, a legal technology company specializing in traffic safety analytics. We aggregate and analyze publicly available government crash data to produce structured intelligence reports for communities, researchers, journalists, and legal professionals. Our reports combine programmatic data retrieval from official open data portals with AI-assisted narrative analysis.

Questions about this report's data or methodology: data@injuria.ai

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