Monthly Traffic Safety Analysis

831 CRASHES IN
MONTGOMERY, MD
JUNE 2025

All metrics benchmarked againstJune 2024

In June 2025, Montgomery County recorded 831 total crashes, a 7.1% decrease from the 894 crashes documented in June 2024. While the number of fatalities remained unchanged at four, there was a notable 42.9% reduction in crashes resulting in serious injuries, which fell from 21 to 12 year-over-year.

831

-7.0%was 894

Total Crash Events

4

Persons Killed

291

-7.9%was 316

Persons Injured

23

-23.3%was 30

Hit-and-Run Crashes

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 40 crashes with unreported severity are not shown in the severity breakdown.

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-06-01 to 2025-06-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash trends in Montgomery County show a year-over-year decline for the month of June. Total crashes fell by 7.1%, from 894 in June 2024 to 831 in June 2025. Similarly, the total number of injuries decreased by 7.9% from 316 to 291.

23

Hit-and-Run Crashes — June 2025

-23.3% vs prior (30)

Hit-and-run incidents showed a downward trend compared to the previous year. The total count of hit-and-run crashes decreased from 30 in June 2024 to 23 in June 2025. The hit-and-run rate also fell, declining from 3.4% to 2.8% of all crashes during the same period.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 10.0%

1

Cyclists Killed

Prior: 0%

2

Motorists Killed

Prior: 3-33.3%

0

Other Killed

Prior: 00.0%

26

Pedestrians Injured

Prior: 33-21.2%

10

Cyclists Injured

Prior: 16-37.5%

250

Motorists Injured

Prior: 258-3.1%

5

Other Injured

Prior: 9-44.4%

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-06-01 to 2025-06-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes showed a shift in the peak day of the week. In June 2025, Monday was the busiest day with 148 crashes, a change from June 2024 when Saturday saw the most incidents at 138. The peak hour for collisions remained consistent, occurring during the 4 p.m. hour in both periods, with 74 crashes this year compared to 71 last year.

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-06-01 to 2025-06-30 · Crash date field aggregated by weekday

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-06-01 to 2025-06-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The number of fatal crashes remained stable at four in both June 2025 and June 2024, though the fatal crash rate per 100 crashes increased slightly from 0.45 to 0.48 due to a lower overall crash total. Crashes resulting in serious injuries saw a significant decrease, falling from 21 (2.3% of total) to 12 (1.4% of total). The proportion of crashes with no injuries increased from 63.5% to 65.7% of all incidents.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.5%
0.0%prior 4
Serious Injury12serious injury crashes1.4%
-42.9%prior 21
Minor Injury129minor injury crashes15.5%
-17.8%prior 157
Possible Injury100possible injury crashes12%
7.5%prior 93
No Injury546no injury crashes65.7%
-3.9%prior 568

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-06-01 to 2025-06-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-06-01 to 2025-06-30 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factor in both periods was 'Failed to Yield Right-of-Way,' though its count decreased from 85 incidents in June 2024 to 70 in June 2025. The top three factors remained consistent, with 'Followed Too Closely' moving up to the second-ranked position with 36 crashes, an increase of one from the prior year. 'Other Improper Action' dropped to the third spot, with its count falling from 41 to 34.

Officer-Reported Primary Contributing Cause

Failed to Yield Right-of-Way70 (8.4%)-17.6%prior 85
Followed Too Closely36 (4.3%)2.9%prior 35
Other Improper Action34 (4.1%)-17.1%prior 41
Operated Motor Vehicle in Inattentive, Careless, Negligent, or Erratic Manner17 (2%)30.8%prior 13
Failed to Keep in Proper Lane15 (1.8%)0.0%prior 15
Improper Backing14 (1.7%)0.0%prior 14
Too Fast For Conditions11 (1.3%)-21.4%prior 14
Ran Red Light11 (1.3%)-26.7%prior 15
Ran Off Roadway9 (1.1%)-30.8%prior 13
Followed Too Closely, Too Fast For Conditions8 (1%)-20.0%prior 10

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-06-01 to 2025-06-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

A notable shift occurred in weather-related crashes, with incidents during rain increasing from 37 in June 2024 to 99 in June 2025. Correspondingly, crashes on wet road surfaces rose from 46 to 121 year-over-year. The proportion of crashes occurring in daylight increased slightly from 77.4% to 79.9%, while crashes in lighted, dark conditions decreased from 140 to 121.

Weather

Clear638 (77.8%)
-20.1%prior 798
Rain99 (12.1%)
167.6%prior 37
Cloudy77 (9.4%)
48.1%prior 52
Fog, Smog, Smoke6 (0.7%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-06-01 to 2025-06-30 · Weather condition at time of crash

Lighting

Daylight664 (80.4%)
-4.0%prior 692
Dark - Lighted121 (14.6%)
-13.6%prior 140
Dark - Not Lighted19 (2.3%)
-24.0%prior 25
Dawn8 (1.0%)
-20.0%prior 10
Dusk6 (0.7%)
-50.0%prior 12
Dark - Unknown Lighting5 (0.6%)
-16.7%prior 6
Other3 (0.4%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-06-01 to 2025-06-30 · Lighting condition field

Road Surface

Dry580 (82.5%)
-19.4%prior 720
Wet121 (17.2%)
163.0%prior 46
Mud, Dirt, Gravel1 (0.1%)
Other1 (0.1%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-06-01 to 2025-06-30 · Road surface condition field

Vehicles & Demographics

The ranking of the top three vehicle makes involved in crashes remained unchanged year-over-year, with Toyota, Honda, and Ford being the most common. However, the number of vehicles from each of these top makes involved in collisions decreased. Toyota-involved crashes fell from 295 to 269, Honda from 224 to 178, and Ford from 141 to 138. Data on driver age distribution was not available for comparison.

Top Vehicle Makes (1,446 vehicles)

1
TOYOTA269 (18.6%)
-8.8%prior 295
2
HONDA178 (12.3%)
-20.5%prior 224
3
FORD138 (9.5%)
-2.1%prior 141
4
CHEVROLET92 (6.4%)
-8.0%prior 100
5
NISSAN82 (5.7%)
-13.7%prior 95
6
HYUNDAI52 (3.6%)
-21.2%prior 66
7
LEXUS47 (3.3%)
11.9%prior 42
8
SUBARU41 (2.8%)
10.8%prior 37
9
JEEP38 (2.6%)
5.6%prior 36
10
KIA37 (2.6%)
0.0%prior 37

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-06-01 to 2025-06-30 · 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: 2025-06-01 through 2025-06-30
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2025-06-01 through 2025-06-30 (30 days)
  • Geographic scope: montgomery, MD
  • Total crash records analyzed: 831
  • Total persons involved: 1,495
  • Total vehicles involved: 1,446

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: June 2025." Published September 9, 2026. Reporting period: 2025-06-01 to 2025-06-30. 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/june-2025-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

ThatCarHitMe.com · An Injuria.ai Company