ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · MONTGOMERY, MD · FEBRUARY 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/february-2024-report
Monthly Traffic Safety Analysis
757 CRASHES IN
MONTGOMERY, MD
FEBRUARY 2024
In February 2024, Montgomery County recorded 757 total crashes, a 4.2% decrease from the 790 crashes documented in February 2023. While the overall number of crashes and resulting injuries remained relatively stable, the most significant year-over-year change was a dramatic reduction in reported hit-and-run incidents, which fell from 148 to 13.
757
▼ -4.2%was 790
Total Crash Events
2
▼ -33.3%was 3
Persons Killed
251
▲ 0.8%was 249
Persons Injured
13
▼ -91.2%was 148
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. 27 crashes with unreported severity are not shown in the severity breakdown.
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-02-01 to 2024-02-29 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, traffic crashes in Montgomery County saw a modest decline in February 2024 compared to the previous year. Total crashes decreased by 4.2%, from 790 to 757. Fatalities also decreased from 3 to 2, while the total number of injuries remained nearly unchanged, with 251 in the current period versus 249 in the prior period.
13
Hit-and-Run Crashes — February 2024
▼ -91.2% vs prior (148)
There was a substantial decrease in hit-and-run incidents in February 2024 compared to the same month in 2023. The number of hit-and-run crashes fell from 148 to 13. Consequently, the hit-and-run rate dropped from 18.7% of all crashes in February 2023 to just 1.7% in February 2024, marking a significant downward trend.
Vulnerable Road User Casualties
1
Pedestrians Killed
0
Cyclists Killed
1
Motorists Killed
0
Other Killed
34
Pedestrians Injured
4
Cyclists Injured
210
Motorists Injured
3
Other Injured
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-02-01 to 2024-02-29 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The timing of crashes shifted between the two periods. In February 2024, the peak day for crashes was Thursday with 143 incidents, whereas in February 2023, Monday was the peak day with 127 crashes. The peak hour for collisions also moved slightly earlier, shifting from the 4 PM hour in the prior year (61 crashes) to the 3 PM hour in the current year (67 crashes).
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-02-01 to 2024-02-29 · Crash date field aggregated by weekday
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-02-01 to 2024-02-29 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While the number of fatal crashes decreased from 3 to 2 year-over-year, the distribution of non-fatal injury crashes shifted towards higher severity. The share of crashes involving serious injuries increased from 1.3% to 2.0% of all incidents, and minor injury crashes rose from a 10.9% share to 15.9%. Conversely, crashes resulting in possible injuries decreased from 14.6% to 10.2% of the total.
Outcome by Severity (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-02-01 to 2024-02-29 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-02-01 to 2024-02-29 · Most severe injury per crash record
Top Contributing Factors
A direct comparison of contributing factors is not possible due to a significant change in how this data was categorized between February 2023 and February 2024. In the current period, the top contributing factor was 'Failed to Yield Right-of-Way,' cited in 90 crashes (11.9% share of total). In the prior period, the leading factor was related to environmental conditions, 'RAIN, SNOW, WET,' which was associated with 41 crashes (5.2% share of total). The fundamental difference in the factor lists prevents a year-over-year analysis of specific driver actions.
Officer-Reported Primary Contributing Cause
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-02-01 to 2024-02-29 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes in February 2024 occurred more frequently in clear and dry conditions compared to the previous year. Crashes on dry roads accounted for 75.8% of the total, up from a 69.6% share in February 2023. Correspondingly, the proportion of crashes on wet roads decreased from 15.3% to 10.8% of all incidents. Lighting conditions remained proportionally similar across both periods, with approximately 59% of crashes in the current period and 57% in the prior period occurring in daylight.
Weather
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-02-01 to 2024-02-29 · Weather condition at time of crash
Lighting
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-02-01 to 2024-02-29 · Lighting condition field
Road Surface
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-02-01 to 2024-02-29 · Road surface condition field
Vehicles & Demographics
The most common vehicle makes involved in crashes remained consistent, with Toyota, Honda, and Ford being the top three in both February 2024 and February 2023. The number of both Toyotas (231 vs. 266) and Hondas (197 vs. 222) involved in crashes decreased. A notable shift occurred in vehicle types, with Sport Utility Vehicles involved in crashes increasing from 147 to 196, while Passenger Cars decreased from 973 to 906.
Top Vehicle Makes (1,370 vehicles)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-02-01 to 2024-02-29 · 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: 2024-02-01 through 2024-02-29
- Report generated: September 9, 2026
Data Coverage
- Reporting period: 2024-02-01 through 2024-02-29 (29 days)
- Geographic scope: montgomery, MD
- Total crash records analyzed: 757
- Total persons involved: 1,416
- Total vehicles involved: 1,370
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: February 2024." Published September 9, 2026. Reporting period: 2024-02-01 to 2024-02-29. 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/february-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: 2024-02-01 – 2024-02-29
Generated: September 9, 2026 · All rights reserved