ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · MONTGOMERY, MD · FEBRUARY 2026
Purpose: Machine-readable JSON endpoint for AI agents, LLMs, researchers, and programmatic consumers. Returns all underlying crash data and AI-generated commentary without HTML.
Authentication: None required. Public endpoint.
GET: https://thatcarhitme.com/api/crash-data/reports/data/maryland/statewide/february-2026-report
Monthly Traffic Safety Analysis
812 CRASHES IN
MONTGOMERY, MD
FEBRUARY 2026
In February 2026, Montgomery County recorded 812 total traffic crashes, a 16.8% increase from the 695 crashes reported in February 2025. Despite this overall rise in collisions, the number of fatalities decreased from three to one. The most significant year-over-year shift was the overall increase in crash volume, which also saw a corresponding rise in total injuries from 212 to 243.
812
▲ 16.8%was 695
Total Crash Events
1
▼ -66.7%was 3
Persons Killed
243
▲ 14.6%was 212
Persons Injured
11
▼ -45.0%was 20
Hit-and-Run Crashes
Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 30 crashes with unreported severity are not shown in the severity breakdown.
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2026-02-01 to 2026-02-28 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Crash data for February indicates a rising trend in traffic incidents compared to the previous year. Total crashes increased by 16.8%, from 695 to 812. Similarly, the number of people injured in these incidents grew by 14.6%, from 212 to 243. However, the number of fatalities reported saw a decrease, falling from 3 in the prior period to 1 in the current period.
11
Hit-and-Run Crashes — February 2026
▼ -45.0% vs prior (20)
Hit-and-run incidents showed a significant decrease compared to the previous year. The total number of hit-and-run crashes fell by 45%, from 20 in February 2025 to 11 in February 2026. Consequently, the hit-and-run rate as a percentage of all crashes was also cut in half, dropping from 2.9% to 1.4%.
Vulnerable Road User Casualties
1
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
0
Other Killed
29
Pedestrians Injured
2
Cyclists Injured
210
Motorists Injured
2
Other Injured
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2026-02-01 to 2026-02-28 · 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 broadly similar year-over-year. Friday was the peak day for crashes in both February 2025 (129 crashes) and February 2026, though in 2026 it tied with Wednesday for the highest volume at 135 crashes. The afternoon rush hour remained the most hazardous time, with the peak hour shifting slightly earlier from 4 PM (65 crashes) in the prior year to 3 PM (66 crashes) in the current year.
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2026-02-01 to 2026-02-28 · Crash date field aggregated by weekday
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2026-02-01 to 2026-02-28 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While total crashes increased, the severity distribution showed a mixed pattern. The number of fatal crashes decreased from 3 in the prior period to 1 in the current period. Conversely, crashes resulting in serious injuries increased from 7 to 11. The proportion of crashes involving any injury (fatal, serious, minor, or possible) slightly decreased from 27.2% to 24.8% of all crashes, as the number of no-injury incidents grew from 461 to 581.
Outcome by Severity (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2026-02-01 to 2026-02-28 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2026-02-01 to 2026-02-28 · Most severe injury per crash record
Top Contributing Factors
The top three contributing factors to crashes remained consistent in ranking year-over-year: 'Failed to Yield Right-of-Way', 'Other Improper Action', and 'Followed Too Closely'. The count for 'Failed to Yield Right-of-Way' crashes increased by 28.2%, from 39 to 50. Other factors saw more dramatic growth; crashes attributed to 'Failed to Keep in Proper Lane' increased by 189% (from 9 to 26), and those involving 'Ran Red Light' grew by 125% (from 8 to 18).
Officer-Reported Primary Contributing Cause
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2026-02-01 to 2026-02-28 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crash conditions were largely consistent for weather and lighting, with clear weather and daylight hours accounting for the majority of incidents in both periods. Approximately 78% of crashes in both years occurred in clear weather. A notable shift occurred in road surface conditions; while crashes on dry roads remained stable (493 prior vs. 488 current), crashes on wet roads increased by 93%, from 71 to 137 incidents.
Weather
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2026-02-01 to 2026-02-28 · Weather condition at time of crash
Lighting
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2026-02-01 to 2026-02-28 · Lighting condition field
Road Surface
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2026-02-01 to 2026-02-28 · Road surface condition field
Vehicles & Demographics
The makes of vehicles involved in crashes showed a stable pattern, with Toyota, Honda, and Ford being the top three in both February 2025 and February 2026. The number of crashes involving Toyotas and Hondas increased, in line with the overall trend, rising from 232 to 280 and 164 to 202, respectively. Passenger cars and SUVs continued to be the most frequently involved vehicle types in both periods.
Top Vehicle Makes (1,413 vehicles)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2026-02-01 to 2026-02-28 · 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: 2026-02-01 through 2026-02-28
- Report generated: September 9, 2026
Data Coverage
- Reporting period: 2026-02-01 through 2026-02-28 (28 days)
- Geographic scope: montgomery, MD
- Total crash records analyzed: 812
- Total persons involved: 1,450
- Total vehicles involved: 1,413
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 2026." Published September 9, 2026. Reporting period: 2026-02-01 to 2026-02-28. 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-2026-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
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Montgomery County Crash Reporting (ACRS) · Socrata
Period: 2026-02-01 – 2026-02-28
Generated: September 9, 2026 · All rights reserved