ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · MONTGOMERY, MD · APRIL 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/april-2025-report
Monthly Traffic Safety Analysis
853 CRASHES IN
MONTGOMERY, MD
APRIL 2025
In April 2025, Montgomery County recorded 853 total crashes, a 3.7% decrease from the 886 crashes reported in April 2024. Despite the overall decline in collisions, the number of fatalities increased from 4 to 5 year-over-year. The total number of injuries remained relatively stable, with 288 in the current period compared to 292 in the prior period.
853
▼ -3.7%was 886
Total Crash Events
5
▲ 25.0%was 4
Persons Killed
288
▼ -1.4%was 292
Persons Injured
29
▲ 26.1%was 23
Hit-and-Run Crashes
Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 25 crashes with unreported severity are not shown in the severity breakdown.
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-04-01 to 2025-04-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Year-over-year, total crashes in Montgomery County saw a modest decline, falling by 3.7% from 886 in April 2024 to 853 in April 2025. Total injuries also decreased slightly by 1.4%, from 292 to 288. However, the number of fatalities rose from 4 in the prior year to 5 in the current period.
29
Hit-and-Run Crashes — April 2025
▲ 26.1% vs prior (23)
Hit-and-run incidents increased in both count and as a proportion of total crashes compared to the previous year. In April 2025, there were 29 hit-and-run crashes, up from 23 in April 2024, representing a 26.1% increase in count. The hit-and-run rate also rose from 2.6% of all crashes in the prior period to 3.4% in the current period, indicating an upward trend.
Vulnerable Road User Casualties
3
Pedestrians Killed
0
Cyclists Killed
2
Motorists Killed
0
Other Killed
40
Pedestrians Injured
9
Cyclists Injured
236
Motorists Injured
3
Other Injured
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-04-01 to 2025-04-30 · 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 some shifts between April 2024 and April 2025. The peak day for crashes moved from Monday (160 crashes) in the prior year to Wednesday (161 crashes) in the current period. The peak hour for collisions remained consistent at 3 p.m., although the number of crashes during that hour decreased from 79 to 70.
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-04-01 to 2025-04-30 · Crash date field aggregated by weekday
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-04-01 to 2025-04-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes shifted slightly year-over-year, with an increase in the most severe outcomes. The number of fatal crashes increased from 4 to 5, and the corresponding fatal crash rate rose from 0.45% to 0.59%. Crashes resulting in minor injuries decreased from 143 to 134, while the proportion of non-injury crashes increased slightly from 67.2% to 67.8% of all incidents.
Outcome by Severity (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-04-01 to 2025-04-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-04-01 to 2025-04-30 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factors for crashes remained consistent year-over-year, though their counts varied. 'Failed to Yield Right-of-Way' was the top factor in both periods, but its count decreased from 82 crashes in April 2024 to 77 in April 2025. The second-ranked factor, 'Other Improper Action,' saw an increase in its count from 34 to 39 crashes. Similarly, 'Followed Too Closely' increased from 32 to 35 incidents, maintaining its position as the third most common factor.
Officer-Reported Primary Contributing Cause
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-04-01 to 2025-04-30 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crash conditions remained broadly similar year-over-year, with the majority of incidents in both periods occurring in clear weather and daylight. In April 2025, 75.0% of crashes happened in clear weather, down slightly from 77.0% in the prior year. There was a noticeable shift in road surface conditions, with the share of crashes on dry roads decreasing from 74.3% to 68.8%, while the share of crashes on wet roads increased from 15.2% to 16.3%.
Weather
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-04-01 to 2025-04-30 · Weather condition at time of crash
Lighting
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-04-01 to 2025-04-30 · Lighting condition field
Road Surface
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-04-01 to 2025-04-30 · Road surface condition field
Vehicles & Demographics
The most common vehicle makes involved in crashes showed some changes between the two periods. Toyota remained the top make in April 2025 with 284 vehicles, though this was a decrease from 304 in the prior year. Honda held the second position, with its involvement increasing from 222 to 228 vehicles. Ford, which was third, saw a notable decrease in crash involvement from 154 to 119 vehicles, while Chevrolet's count rose from 77 to 96.
Top Vehicle Makes (1,498 vehicles)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-04-01 to 2025-04-30 · 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: 2025-04-01 through 2025-04-30
- Report generated: September 9, 2026
Data Coverage
- Reporting period: 2025-04-01 through 2025-04-30 (30 days)
- Geographic scope: montgomery, MD
- Total crash records analyzed: 853
- Total persons involved: 1,559
- Total vehicles involved: 1,498
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: April 2025." Published September 9, 2026. Reporting period: 2025-04-01 to 2025-04-30. 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/april-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: 2025-04-01 – 2025-04-30
Generated: September 9, 2026 · All rights reserved