Yearly Traffic Safety Analysis

10,308 CRASHES IN
MONTGOMERY, MD
2025

All metrics benchmarked against2024

In 2025, Montgomery County recorded 10,308 total crashes, a 3.0% decrease from the 10,629 crashes reported in 2024. Despite the overall decline in collisions, the number of fatalities increased by 8.8% year-over-year, rising from 34 to 37. This divergence, with fewer total crashes but more deaths, represents the most significant shift in the data.

10,308

▼ -3.0%was 10,629

Total Crash Events

▲ 8.8%was 34

Persons Killed

3,431

▼ -5.4%was 3,628

Persons Injured

267

▲ 0.8%was 265

Hit-and-Run Crashes

Note: "Persons Killed" (37) counts individual fatalities across all crash events. "Fatal" in the severity table below (39) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 456 crashes with unreported severity are not shown in the severity breakdown.

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crash trends in Montgomery County showed a modest improvement in volume but a worsening in severity. The total number of crashes decreased by 3.0% from 2024 to 2025, and total injuries fell by 5.4%. However, this was offset by an 8.8% increase in fatalities over the same period.

267

Hit-and-Run Crashes — 2025

▲ 0.8% vs prior (265)

The number of hit-and-run crashes remained nearly unchanged, increasing from 265 incidents in 2024 to 267 in 2025. As a percentage of all crashes, the hit-and-run rate saw a marginal increase from 2.5% to 2.6%. This indicates a stable trend in the frequency of hit-and-run events relative to the total number of collisions.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 16↓-12.5%

Cyclists Killed

Prior: 0→%

Motorists Killed

Prior: 18↑5.6%

Other Killed

Prior: 0→0.0%

423

Pedestrians Injured

Prior: 451↓-6.2%

120

Cyclists Injured

Prior: 109↑10.1%

2,836

Motorists Injured

Prior: 3,009↓-5.7%

Other Injured

Prior: 59↓-11.9%

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-01-01 to 2025-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes remained largely consistent year-over-year. The peak day for crashes in both 2025 and 2024 was Friday, with 1,673 and 1,688 incidents respectively. Similarly, the afternoon hour of 3 PM was the peak time for collisions in both periods, accounting for 814 crashes in 2025 and 840 in the prior year, indicating stable daily and weekly crash rhythms.

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes declined, the severity of outcomes worsened in 2025. The fatal crash rate increased from 0.34 to 0.38 per 100 crashes. The proportion of crashes resulting in serious injury decreased slightly from 2.0% to 1.9% of all crashes, and minor injury crashes also saw a proportional decline from 16.4% to 15.4%. Conversely, crashes resulting in no injuries increased as a share of the total, from 66.3% to 66.7%.

Severity is per crash event (most severe injury). 39 fatal crash events resulted in 37 persons killed.

Outcome by Severity (Crash Events)

Fatal37fatal crashes0.4%

↑8.8%prior 34

Serious Injury200serious injury crashes1.9%

↓-7.4%prior 216

Minor Injury1,590minor injury crashes15.4%

↓-8.6%prior 1,739

Possible Injury1,149possible injury crashes11.1%

↑1.7%prior 1,130

No Injury6,876no injury crashes66.7%

↓-2.5%prior 7,052

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors to crashes remained consistent between the two periods, though their counts shifted. 'Failed to Yield Right-of-Way' was the top factor in both years, but its incident count decreased by 12.6% from 953 in 2024 to 833 in 2025. The most significant change in count among the top five factors was a 23.8% reduction in crashes attributed to 'Too Fast For Conditions,' which fell from 240 to 183 incidents. Despite these decreases, the top three factors—'Failed to Yield,' 'Other Improper Action,' and 'Followed Too Closely'—retained their top rankings.

Officer-Reported Primary Contributing Cause

Failed to Yield Right-of-Way833 (8.1%)↓-12.6%prior 953

Other Improper Action440 (4.3%)↓-6.2%prior 469

Followed Too Closely410 (4%)↑0.2%prior 409

Failed to Keep in Proper Lane208 (2%)↓-1.4%prior 211

Too Fast For Conditions183 (1.8%)↓-23.8%prior 240

Operated Motor Vehicle in Inattentive, Careless, Negligent, or Erratic Manner164 (1.6%)↓-15.0%prior 193

Improper Backing158 (1.5%)↓-13.2%prior 182

Ran Red Light128 (1.2%)↓-9.9%prior 142

Ran Off Roadway120 (1.2%)↓-6.3%prior 128

Improper Turn112 (1.1%)↓-15.8%prior 133

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-01-01 to 2025-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crash conditions were broadly similar year-over-year, with most incidents occurring in clear weather on dry roads during daylight hours. In 2025, 78.9% of crashes happened in clear weather, a slight increase from 77.4% in 2024. Correspondingly, crashes occurring during rain decreased as a share of the total from 11.5% in 2024 to 9.1% in 2025. This shift is also reflected in road surface conditions, where the share of crashes on wet roads fell from 14.8% to 12.4%.

Weather

Clear8,131 (79.7%)

↓-1.2%prior 8,231

Cloudy935 (9.2%)

↑3.1%prior 907

Rain934 (9.2%)

↓-23.6%prior 1,222

Snow124 (1.2%)

↑37.8%prior 90

Blowing Snow30 (0.3%)

↑66.7%prior 18

Severe Crosswinds17 (0.2%)

↑240.0%prior 5

Freezing Rain Or Freezing Drizzle17 (0.2%)

↑6.3%prior 16

Fog, Smog, Smoke12 (0.1%)

↓-53.8%prior 26

Sleet Or Hail4 (0.0%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash

Lighting

Daylight7,006 (68.5%)

↓-1.2%prior 7,094

Dark - Lighted2,463 (24.1%)

↓-4.8%prior 2,587

Dark - Not Lighted430 (4.2%)

↓-8.7%prior 471

Dusk138 (1.3%)

↓-7.4%prior 149

Dawn91 (0.9%)

↓-30.5%prior 131

Dark - Unknown Lighting69 (0.7%)

↓-23.3%prior 90

Other30 (0.3%)

↓-30.2%prior 43

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field

Road Surface

Dry7,377 (83.1%)

↓-2.4%prior 7,561

Wet1,283 (14.5%)

↓-18.5%prior 1,574

Snow100 (1.1%)

↑85.2%prior 54

Ice/Frost66 (0.7%)

↑24.5%prior 53

Slush36 (0.4%)

↑111.8%prior 17

Other10 (0.1%)

↓-44.4%prior 18

Mud, Dirt, Gravel3 (0.0%)

Water (standing, moving)2 (0.0%)

↓-60.0%prior 5

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field

Vehicles & Demographics

The demographics of vehicles involved in crashes saw little change between periods. Passenger Cars and Sport Utility Vehicles remained the top two vehicle types involved in collisions in both years. The ranking of the most common vehicle makes was also stable, with Toyota (3,482 vehicles in 2025 vs. 3,517 in 2024) and Honda (2,569 vs. 2,707) holding the top two spots. The only shift in the top five was Chevrolet and Nissan swapping the 4th and 5th positions.

Top Vehicle Makes (17,961 vehicles)

TOYOTA3,482 (19.4%)

↓-1.0%prior 3,517

HONDA2,569 (14.3%)

↓-5.1%prior 2,707

FORD1,584 (8.8%)

↓-8.1%prior 1,723

CHEVROLET1,011 (5.6%)

↑0.7%prior 1,004

NISSAN996 (5.5%)

↓-8.7%prior 1,091

HYUNDAI718 (4%)

↓-5.5%prior 760

LEXUS496 (2.8%)

↑5.1%prior 472

SUBARU436 (2.4%)

↓-4.0%prior 454

JEEP412 (2.3%)

↓-5.7%prior 437

KIA411 (2.3%)

↑9.3%prior 376

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-01-01 to 2025-12-31 · 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: 2025-01-01 through 2025-12-31
Report generated: July 21, 2026

Data Coverage

Reporting period: 2025-01-01 through 2025-12-31 (365 days)
Geographic scope: montgomery, MD
Total crash records analyzed: 10,308
Total persons involved: 18,640
Total vehicles involved: 17,961

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: 2025." Published July 21, 2026. Reporting period: 2025-01-01 to 2025-12-31. 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/2025-annual-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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10,308 CRASHES INMONTGOMERY, MD2025