Monthly Traffic Safety Analysis

790 CRASHES IN
MONTGOMERY, MD
FEBRUARY 2023

All metrics benchmarked againstFebruary 2022

In February 2023, Montgomery County recorded 790 total crashes, a 10.6% increase from the 714 crashes reported in February 2022. While total crashes and injuries saw an increase, the most significant year-over-year shift was in crash severity, with the number of fatalities rising from one to three.

790

10.6%was 714

Total Crash Events

3

200.0%was 1

Persons Killed

249

11.2%was 224

Persons Injured

148

-11.4%was 167

Hit-and-Run Crashes

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 1 crash with unreported severity is not shown in the severity breakdown.

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-02-01 to 2023-02-28 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash data for February 2023 indicates an upward trend in collisions compared to the same month in the prior year. Total crashes increased by 10.6% from 714 to 790. Similarly, total injuries rose by 11.2% from 224 to 249, and fatalities increased from one to three.

148

Hit-and-Run Crashes — February 2023

-11.4% vs prior (167)

The number of hit-and-run incidents decreased in February 2023 compared to the same month in the previous year. There were 148 hit-and-run crashes, down from 167 in February 2022. This represents a downward trend in the hit-and-run rate, which fell from 23.4% of all crashes in the prior period to 18.7% in the current period.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 0%

0

Cyclists Killed

Prior: 00.0%

1

Motorists Killed

Prior: 10.0%

0

Other Killed

Prior: 00.0%

35

Pedestrians Injured

Prior: 342.9%

4

Cyclists Injured

Prior: 6-33.3%

208

Motorists Injured

Prior: 18214.3%

2

Other Injured

Prior: 20.0%

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-02-01 to 2023-02-28 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

Temporal crash patterns remained broadly consistent year-over-year, with collisions concentrated during weekday commute hours. The peak day for crashes shifted from Tuesday (120 crashes) in February 2022 to Monday (127 crashes) in February 2023. The single busiest hour also shifted slightly, moving from the 3 p.m. hour (64 crashes) in the prior period to the 4 p.m. hour (61 crashes) in the current period.

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-02-01 to 2023-02-28 · Crash date field aggregated by weekday

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-02-01 to 2023-02-28 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity increased in February 2023 compared to the prior year, with the number of fatal crashes rising from one to three. This caused the fatal crash rate to more than double, from 0.14% to 0.38%. While the count of serious injury crashes decreased from 15 to 10, crashes involving minor injuries increased from 64 to 86.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.4%
200.0%prior 1
Serious Injury10serious injury crashes1.3%
-33.3%prior 15
Minor Injury86minor injury crashes10.9%
34.4%prior 64
Possible Injury115possible injury crashes14.6%
15.0%prior 100
No Injury575no injury crashes72.8%
8.3%prior 531

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-02-01 to 2023-02-28 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-02-01 to 2023-02-28 · Most severe injury per crash record

Top Contributing Factors

Adverse weather and wet conditions were the leading contributing factors in both periods. The top factor, "RAIN, SNOW, WET," saw its crash count decrease from 45 to 41. In contrast, crashes attributed to "ICY OR SNOW-COVERED, N/A" increased significantly in count from 1 to 8, and those involving "SLEET, HAIL, FREEZ. RAIN, WET" doubled in count from 4 to 8.

Officer-Reported Primary Contributing Cause

RAIN, SNOW, WET41 (5.2%)-8.9%prior 45
N/A, WET27 (3.4%)-12.9%prior 31
ICY OR SNOW-COVERED, N/A8 (1%)
SLEET, HAIL, FREEZ. RAIN, WET8 (1%)
N/A, VISION OBSTRUCTION (INCL. BLINDED BY SUN)7 (0.9%)16.7%prior 6
ANIMAL, N/A6 (0.8%)-14.3%prior 7
ICY OR SNOW-COVERED, SLEET, HAIL, FREEZ. RAIN5 (0.6%)
ICY OR SNOW-COVERED, V WIPERS|W OTHER ENVIRONMENTAL3 (0.4%)
N/A, RAIN, SNOW3 (0.4%)-50.0%prior 6
BACKUP DUE TO REGULAR CONGESTION, N/A2 (0.3%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-02-01 to 2023-02-28 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

While most crashes in both periods occurred in clear weather and on dry roads, February 2023 saw a notable increase in incidents under adverse conditions. The number of crashes on roads with ice or frost increased from 4 to 17 year-over-year. The proportion of crashes occurring in daylight remained stable at approximately 57% for both periods.

Weather

Clear538 (75.1%)
7.0%prior 503
Rain88 (12.3%)
8.6%prior 81
Cloudy73 (10.2%)
69.8%prior 43
Other6 (0.8%)
Snow4 (0.6%)
-20.0%prior 5
Sleet Or Hail2 (0.3%)
Fog, Smog, Smoke2 (0.3%)
-66.7%prior 6
Severe Crosswinds2 (0.3%)
-60.0%prior 5
Wintry Mix1 (0.1%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-02-01 to 2023-02-28 · Weather condition at time of crash

Lighting

Daylight452 (57.9%)
11.9%prior 404
Dark - Lighted244 (31.3%)
1.7%prior 240
Dark - Not Lighted33 (4.2%)
32.0%prior 25
Dusk24 (3.1%)
84.6%prior 13
Dawn18 (2.3%)
38.5%prior 13
Dark - Unknown Lighting7 (0.9%)
Other2 (0.3%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-02-01 to 2023-02-28 · Lighting condition field

Road Surface

Dry550 (79.7%)
9.1%prior 504
Wet121 (17.5%)
14.2%prior 106
Ice/Frost17 (2.5%)
Mud, Dirt, Gravel1 (0.1%)
Snow1 (0.1%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-02-01 to 2023-02-28 · Road surface condition field

Vehicles & Demographics

Passenger cars were the most frequently involved vehicle type in both periods, with their count increasing from 859 in February 2022 to 973 in February 2023. The top three vehicle makes involved in crashes remained consistent: Toyota, Honda, and Ford. The number of vehicles from all three top makes involved in collisions increased, in line with the overall rise in total crashes.

Top Vehicle Makes (1,383 vehicles)

1
TOYOTA180 (13%)
13.2%prior 159
2
HONDA169 (12.2%)
9.7%prior 154
3
FORD125 (9%)
5.9%prior 118
4
TOYT86 (6.2%)
56.4%prior 55
5
NISSAN62 (4.5%)
5.1%prior 59
6
HOND53 (3.8%)
82.8%prior 29
7
HYUNDAI42 (3%)
50.0%prior 28
8
DODGE38 (2.7%)
46.2%prior 26
9
KIA32 (2.3%)
77.8%prior 18
10
JEEP31 (2.2%)
55.0%prior 20

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-02-01 to 2023-02-28 · 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: 2023-02-01 through 2023-02-28
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2023-02-01 through 2023-02-28 (28 days)
  • Geographic scope: montgomery, MD
  • Total crash records analyzed: 790
  • Total persons involved: 1,428
  • Total vehicles involved: 1,383

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: February 2023." Published September 9, 2026. Reporting period: 2023-02-01 to 2023-02-28. 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/february-2023-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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