ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · MONTGOMERY, MD · MARCH 2025
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/march-2025-report
Monthly Traffic Safety Analysis
795 CRASHES IN
MONTGOMERY, MD
MARCH 2025
In March 2025, Montgomery County recorded 795 total vehicle crashes, a 10.0% decrease from the 883 crashes reported in March 2024. While total incidents declined, the most notable temporal shift was the peak crash day moving from Friday in the prior year to Monday in the current period. Fatalities remained unchanged year-over-year, with two deaths reported in both March 2024 and March 2025.
795
▼ -10.0%was 883
Total Crash Events
2
Persons Killed
273
▼ -6.2%was 291
Persons Injured
21
▲ 16.7%was 18
Hit-and-Run Crashes
Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 39 crashes with unreported severity are not shown in the severity breakdown.
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-03-01 to 2025-03-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend in traffic incidents shows a year-over-year improvement, with total crashes falling by 10.0% from 883 to 795. This downward trend extended to injuries, which decreased by 6.2% from 291 to 273. However, the number of fatalities held steady at two for both periods.
21
Hit-and-Run Crashes — March 2025
▲ 16.7% vs prior (18)
Despite a decrease in total crashes, hit-and-run incidents trended upward. The number of hit-and-run crashes increased from 18 in March 2024 to 21 in March 2025. This pushed the hit-and-run rate, or the proportion of all crashes that were hit-and-runs, from 2.0% in the prior year to 2.6% in the current period.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
2
Motorists Killed
0
Other Killed
34
Pedestrians Injured
7
Cyclists Injured
221
Motorists Injured
11
Other Injured
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-03-01 to 2025-03-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
A notable shift occurred in the daily and hourly patterns of crashes compared to the previous year. The peak day for crashes moved from Friday (148 crashes) in March 2024 to Monday (146 crashes) in March 2025. Similarly, the peak hour for incidents shifted an hour earlier, from the 4 p.m. hour in the prior year (80 crashes) to the 3 p.m. hour in the current period (68 crashes).
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-03-01 to 2025-03-31 · Crash date field aggregated by weekday
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-03-01 to 2025-03-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While the number of fatal crashes remained constant at two in both periods, the severity distribution of non-fatal crashes shifted. The count of serious injury crashes increased from 16 to 20, raising their share of total crashes from 1.8% to 2.5%. Conversely, crashes resulting in no injury decreased from 593 to 526, and their proportion of all crashes fell slightly from 67.2% to 66.2%.
Outcome by Severity (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-03-01 to 2025-03-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-03-01 to 2025-03-31 · Most severe injury per crash record
Top Contributing Factors
The top three contributing factors remained consistent in ranking between both periods: 'Failed to Yield Right-of-Way,' 'Other Improper Action,' and 'Followed Too Closely.' However, the incident count for 'Failed to Yield Right-of-Way' dropped by 29% from 93 to 66, and 'Other Improper Action' saw a 31% decrease in count from 48 to 33. In contrast, crashes attributed to 'Failed to Keep in Proper Lane' increased in count from 13 to 21.
Officer-Reported Primary Contributing Cause
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-03-01 to 2025-03-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes in March 2025 occurred under more favorable conditions compared to March 2024. The number of crashes happening in rain dropped from 121 to 40, and incidents on wet road surfaces fell from 136 to 50. Consequently, the proportion of crashes on dry roads increased from 72.4% in the prior year to 80.0% in the current year, while crashes in clear weather rose from 76.0% to 83.9% of the total.
Weather
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-03-01 to 2025-03-31 · Weather condition at time of crash
Lighting
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-03-01 to 2025-03-31 · Lighting condition field
Road Surface
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-03-01 to 2025-03-31 · Road surface condition field
Vehicles & Demographics
The composition of vehicles involved in crashes remained largely stable year-over-year. Toyota, Honda, and Ford were the top three most common vehicle makes in both March 2024 and March 2025, with their rankings unchanged. Passenger cars and Sport Utility Vehicles continued to be the most frequent vehicle types involved, with their counts of 919 and 217 respectively in the current period reflecting a decrease from 1,013 and 223 in the prior year.
Top Vehicle Makes (1,404 vehicles)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-03-01 to 2025-03-31 · 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: 2025-03-01 through 2025-03-31
- Report generated: September 9, 2026
Data Coverage
- Reporting period: 2025-03-01 through 2025-03-31 (31 days)
- Geographic scope: montgomery, MD
- Total crash records analyzed: 795
- Total persons involved: 1,462
- Total vehicles involved: 1,404
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 2025." Published September 9, 2026. Reporting period: 2025-03-01 to 2025-03-31. 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-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
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Montgomery County Crash Reporting (ACRS) · Socrata
Period: 2025-03-01 – 2025-03-31
Generated: September 9, 2026 · All rights reserved