ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · MONTGOMERY, MD · APRIL 2024
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-2024-report
Monthly Traffic Safety Analysis
886 CRASHES IN
MONTGOMERY, MD
APRIL 2024
In April 2024, Montgomery County recorded 886 traffic crashes, a 2.6% increase from the 864 crashes documented in April 2023. While the total number of crashes remained relatively stable, the number of fatalities doubled from 2 to 4. The most significant year-over-year change was a substantial decrease in reported hit-and-run incidents, which fell from 204 in the prior period to 23 in the current period.
886
▲ 2.5%was 864
Total Crash Events
4
▲ 100.0%was 2
Persons Killed
292
▼ -0.3%was 293
Persons Injured
23
▼ -88.7%was 204
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. 34 crashes with unreported severity are not shown in the severity breakdown.
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-04-01 to 2024-04-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall crash totals in Montgomery County saw a slight increase of 2.6% in April 2024 compared to the same month last year, rising from 864 to 886. Despite this increase in collisions, the number of people injured remained nearly identical, decreasing by one person from 293 to 292. However, the number of fatalities doubled, increasing from 2 in April 2023 to 4 in April 2024.
23
Hit-and-Run Crashes — April 2024
▼ -88.7% vs prior (204)
There was a dramatic year-over-year decrease in hit-and-run incidents. In April 2024, there were 23 hit-and-run crashes, a sharp decline from the 204 incidents recorded in April 2023. This represents an 88.7% reduction in the count of hit-and-run crashes. Consequently, the hit-and-run rate plummeted from 23.6% of all crashes in the prior year to just 2.6% in the current period.
Vulnerable Road User Casualties
3
Pedestrians Killed
0
Cyclists Killed
1
Motorists Killed
0
Other Killed
28
Pedestrians Injured
9
Cyclists Injured
253
Motorists Injured
2
Other Injured
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-04-01 to 2024-04-30 · 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 2024, the highest number of crashes occurred on Monday (160), whereas in April 2023, Saturday was the peak day (161). The peak hour for collisions also shifted slightly, moving from the 4 p.m. hour in the prior year (70 crashes) to the 3 p.m. hour in the current year (79 crashes).
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-04-01 to 2024-04-30 · Crash date field aggregated by weekday
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-04-01 to 2024-04-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Crash severity worsened in April 2024 compared to the previous year. The number of fatal crashes doubled from 2 to 4, increasing their share of all crashes from 0.2% to 0.5%. While the count of serious injury crashes remained stable at 20, there was a notable shift in other injury categories. Crashes resulting in minor injuries rose from 96 to 143, while those with possible injuries fell from 131 to 90.
Outcome by Severity (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-04-01 to 2024-04-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-04-01 to 2024-04-30 · Most severe injury per crash record
Top Contributing Factors
A direct year-over-year comparison of contributing factors is not possible due to significant changes in how this data was categorized between April 2023 and April 2024. In the current period, the leading factor was 'Failed to Yield Right-of-Way', cited in 82 crashes. In the prior period, the data focused on environmental conditions, with 'RAIN, SNOW, WET' being the most cited factor in 43 crashes, making a trend analysis of specific driver behaviors unfeasible.
Officer-Reported Primary Contributing Cause
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-04-01 to 2024-04-30 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The distribution of crashes across different environmental conditions remained broadly similar year-over-year, with most incidents in both periods occurring in clear weather on dry roads. In April 2024, 73.6% of crashes happened during daylight, compared to 71.0% in April 2023. The proportion of crashes on wet road surfaces saw a slight increase, accounting for 15.2% of crashes in the current period versus 13.0% in the prior year.
Weather
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-04-01 to 2024-04-30 · Weather condition at time of crash
Lighting
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-04-01 to 2024-04-30 · Lighting condition field
Road Surface
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-04-01 to 2024-04-30 · Road surface condition field
Vehicles & Demographics
The top three vehicle makes involved in crashes remained consistent, with Toyota, Honda, and Ford being the most common in both periods. After normalizing for data entry variations in the prior year's records, Toyota-made vehicles were involved in 304 crashes in April 2024, nearly identical to the estimated 302 in April 2023. The involvement of Ford vehicles increased from 130 to 154, while Honda vehicles saw a slight decrease from approximately 237 to 222.
Top Vehicle Makes (1,551 vehicles)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-04-01 to 2024-04-30 · 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: 2024-04-01 through 2024-04-30
- Report generated: September 9, 2026
Data Coverage
- Reporting period: 2024-04-01 through 2024-04-30 (30 days)
- Geographic scope: montgomery, MD
- Total crash records analyzed: 886
- Total persons involved: 1,603
- Total vehicles involved: 1,551
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 2024." Published September 9, 2026. Reporting period: 2024-04-01 to 2024-04-30. 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-2024-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: 2024-04-01 – 2024-04-30
Generated: September 9, 2026 · All rights reserved