ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · MONTGOMERY, MD · APRIL 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/april-2026-report
Monthly Traffic Safety Analysis
784 CRASHES IN
MONTGOMERY, MD
APRIL 2026
In April 2026, Montgomery County recorded 784 total traffic crashes, an 8.1% decrease from the 853 crashes reported in April 2025. Alongside this overall decline, the number of fatalities also decreased from 5 to 4. A notable change was observed in the leading contributing factor, 'Failed to Yield Right-of-Way,' which saw its count decrease by nearly 30% from the previous year.
784
▼ -8.1%was 853
Total Crash Events
4
▼ -20.0%was 5
Persons Killed
268
▼ -6.9%was 288
Persons Injured
22
▼ -24.1%was 29
Hit-and-Run Crashes
Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 42 crashes with unreported severity are not shown in the severity breakdown.
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2026-04-01 to 2026-04-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall traffic safety trends in Montgomery County showed improvement in April 2026 compared to the same month in the prior year. Total crashes decreased by 8.1%, from 853 to 784. This downward trend was also reflected in crash outcomes, with total injuries falling by 6.9% and fatalities dropping from 5 to 4.
22
Hit-and-Run Crashes — April 2026
▼ -24.1% vs prior (29)
Hit-and-run incidents decreased in April 2026 compared to the same month in 2025. The total number of hit-and-run crashes fell from 29 to 22. Consequently, the hit-and-run rate, measured as the number of such incidents per 100 crashes, also declined from 3.4 to 2.8.
Vulnerable Road User Casualties
1
Pedestrians Killed
0
Cyclists Killed
3
Motorists Killed
0
Other Killed
30
Pedestrians Injured
9
Cyclists Injured
224
Motorists Injured
5
Other Injured
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2026-04-01 to 2026-04-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes shifted between the two periods. In April 2026, the peak day for crashes was Thursday with 146 incidents, a change from April 2025 when Wednesday was the peak day with 161 crashes. Similarly, the peak hour moved from the 3 p.m. hour in the prior year (70 crashes) to the 4 p.m. hour in the current year (67 crashes).
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2026-04-01 to 2026-04-30 · Crash date field aggregated by weekday
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2026-04-01 to 2026-04-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes saw a slight improvement year-over-year. The number of fatal crashes decreased from 5 in April 2025 to 4 in April 2026, with the fatal crash rate per 100 crashes dropping from 0.59 to 0.51. While the overall proportion of crashes involving any injury remained stable at approximately 29% for both periods, the share of serious injury crashes decreased from 2.5% to 1.9% of the total.
Outcome by Severity (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2026-04-01 to 2026-04-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2026-04-01 to 2026-04-30 · Most severe injury per crash record
Top Contributing Factors
The top three contributing factors cited in crashes remained consistent, though their frequencies changed. 'Failed to Yield Right-of-Way' was the leading factor in both April 2025 (77 crashes) and April 2026 (54 crashes), but its count decreased by 30%. 'Followed Too Closely' also saw a decrease in count from 35 to 30 incidents. Conversely, crashes attributed to 'Failed to Keep in Proper Lane' increased significantly, rising from 9 in the prior year to 23 in the current period.
Officer-Reported Primary Contributing Cause
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2026-04-01 to 2026-04-30 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes in April 2026 occurred under more favorable conditions compared to the previous year. The proportion of crashes happening in clear weather increased from 75.0% to 85.3% of all incidents. Correspondingly, crashes on wet roads decreased significantly, falling from 139 incidents in April 2025 to 68 incidents in April 2026. The distribution of crashes by lighting conditions remained largely unchanged between the two periods.
Weather
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2026-04-01 to 2026-04-30 · Weather condition at time of crash
Lighting
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2026-04-01 to 2026-04-30 · Lighting condition field
Road Surface
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2026-04-01 to 2026-04-30 · Road surface condition field
Vehicles & Demographics
The ranking of the top three vehicle makes involved in crashes—Toyota, Honda, and Ford—remained unchanged year-over-year. The number of vehicles from these makes involved in crashes decreased, reflecting the overall reduction in total incidents. In terms of vehicle types, passenger cars and SUVs continued to be the most common vehicles involved, with their counts decreasing from 1015 to 898 and 227 to 194, respectively.
Top Vehicle Makes (1,374 vehicles)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2026-04-01 to 2026-04-30 · 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: 2026-04-01 through 2026-04-30
- Report generated: July 21, 2026
Data Coverage
- Reporting period: 2026-04-01 through 2026-04-30 (30 days)
- Geographic scope: montgomery, MD
- Total crash records analyzed: 784
- Total persons involved: 1,429
- Total vehicles involved: 1,374
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 2026." Published July 21, 2026. Reporting period: 2026-04-01 to 2026-04-30. 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-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: 2026-04-01 – 2026-04-30
Generated: July 21, 2026 · All rights reserved