ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · MONTGOMERY, MD · MAY 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/may-2025-report
Monthly Traffic Safety Analysis
897 CRASHES IN
MONTGOMERY, MD
MAY 2025
In May 2025, Montgomery County recorded 897 total vehicle crashes, a 6.3% decrease from the 957 crashes reported in May 2024. Total injuries also declined by 6.0% from 332 to 312, and fatalities dropped from two to one. The most significant year-over-year shift was a 35% reduction in crashes attributed to the leading factor, 'Failed to Yield Right-of-Way,' which fell from 97 incidents to 63.
897
▼ -6.3%was 957
Total Crash Events
1
▼ -50.0%was 2
Persons Killed
312
▼ -6.0%was 332
Persons Injured
18
▼ -18.2%was 22
Hit-and-Run Crashes
Note: "Persons Killed" (1) 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. 40 crashes with unreported severity are not shown in the severity breakdown.
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-05-01 to 2025-05-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Traffic safety metrics in Montgomery County showed a general improvement in May 2025 compared to the same month in the prior year. Total crashes fell by 6.3%, from 957 to 897. This downward trend was also observed in crash outcomes, with total injuries decreasing by 6.0% and total fatalities falling from two to one.
18
Hit-and-Run Crashes — May 2025
▼ -18.2% vs prior (22)
Hit-and-run incidents showed a downward trend in May 2025 compared to the previous year. The absolute number of hit-and-run crashes decreased from 22 to 18. This reduction is also reflected in the hit-and-run rate, which fell from 2.3% of all crashes in May 2024 to 2.0% in May 2025.
Vulnerable Road User Casualties
1
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
0
Other Killed
33
Pedestrians Injured
9
Cyclists Injured
266
Motorists Injured
4
Other Injured
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-05-01 to 2025-05-31 · 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 broadly similar between the two periods. Friday was the peak day for crashes in both May 2024 (176 crashes) and May 2025, though in the current period Thursday saw an equal number of crashes (155). The afternoon commute remains the highest-risk time, with the peak hour shifting slightly from a tie between 3 PM and 4 PM in 2024 to a distinct peak at 3 PM in 2025 (90 crashes).
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-05-01 to 2025-05-31 · Crash date field aggregated by weekday
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-05-01 to 2025-05-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes decreased in May 2025 compared to the prior year. The fatal crash rate fell from 0.31% to 0.22%, with fatal crashes dropping from 3 to 2. Crashes involving serious injuries also saw a notable decline, decreasing from 20 to 13, and their share of all crashes fell from 2.1% to 1.4%. The proportions of minor and possible injury crashes remained stable year-over-year.
Severity is per crash event (most severe injury). 2 fatal crash events resulted in 1 persons killed.
Outcome by Severity (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-05-01 to 2025-05-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-05-01 to 2025-05-31 · Most severe injury per crash record
Top Contributing Factors
While the top contributing factors were consistent across both periods, their counts decreased. 'Failed to Yield Right-of-Way' remained the number one cause but its count dropped by 35% from 97 crashes in May 2024 to 63 in May 2025. 'Other Improper Action' saw a 41% decrease in count, falling from 51 to 30 incidents. 'Followed Too Closely' became the second-leading factor in 2025, with its count decreasing from 44 to 37.
Officer-Reported Primary Contributing Cause
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-05-01 to 2025-05-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The distribution of crashes across weather and road surface conditions was largely unchanged year-over-year, with clear weather and dry roads being the predominant conditions in both periods. Crashes in the rain accounted for 18.5% of the total in May 2025, compared to 19.7% in May 2024. A slight shift was observed in lighting, with the proportion of crashes in daylight increasing from 74.6% to 79.8% and crashes in dark, lighted conditions decreasing from 18.4% to 15.2%.
Weather
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-05-01 to 2025-05-31 · Weather condition at time of crash
Lighting
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-05-01 to 2025-05-31 · Lighting condition field
Road Surface
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-05-01 to 2025-05-31 · Road surface condition field
Vehicles & Demographics
The top vehicle makes involved in crashes remained consistent, with Toyota, Honda, and Ford being the three most frequent in both May 2024 and May 2025. The number of Toyotas and Fords in crashes decreased from 330 to 307 and 152 to 133, respectively. Chevrolet and Nissan swapped the fourth and fifth ranks, as Chevrolet involvement rose from 90 to 102 vehicles while Nissan involvement fell from 96 to 85.
Top Vehicle Makes (1,581 vehicles)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-05-01 to 2025-05-31 · 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: 2025-05-01 through 2025-05-31
- Report generated: August 3, 2026
Data Coverage
- Reporting period: 2025-05-01 through 2025-05-31 (31 days)
- Geographic scope: montgomery, MD
- Total crash records analyzed: 897
- Total persons involved: 1,633
- Total vehicles involved: 1,581
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: May 2025." Published August 3, 2026. Reporting period: 2025-05-01 to 2025-05-31. 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/may-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: 2025-05-01 – 2025-05-31
Generated: August 3, 2026 · All rights reserved