ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · MONTGOMERY, MD · SEPTEMBER 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/september-2024-report
Monthly Traffic Safety Analysis
968 CRASHES IN
MONTGOMERY, MD
SEPTEMBER 2024
In September 2024, Montgomery County recorded 968 total traffic crashes, a slight decrease from the 973 crashes reported in September 2023, representing a 0.5% year-over-year reduction. While overall crash volume remained stable, the most significant change was a dramatic decrease in reported hit-and-run incidents, which fell from 193 to 26.
968
▼ -0.5%was 973
Total Crash Events
4
▲ 33.3%was 3
Persons Killed
329
▼ -8.4%was 359
Persons Injured
26
▼ -86.5%was 193
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. 46 crashes with unreported severity are not shown in the severity breakdown.
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-09-01 to 2024-09-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall crash volume in Montgomery County was nearly stable in September 2024 compared to the same month in the prior year, with a minor decrease of 5 crashes from 973 to 968. The number of persons injured in these crashes decreased by 8.4% from 359 to 329. Conversely, the number of fatalities increased from 3 in the prior period to 4 in the current period.
26
Hit-and-Run Crashes — September 2024
▼ -86.5% vs prior (193)
There was a substantial year-over-year decrease in hit-and-run incidents. The number of hit-and-run crashes fell by 86.5%, from 193 in September 2023 to 26 in September 2024. Consequently, the hit-and-run rate, representing the percentage of total crashes that were hit-and-runs, dropped significantly from 19.8% to 2.7%.
Vulnerable Road User Casualties
4
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
0
Other Killed
34
Pedestrians Injured
10
Cyclists Injured
275
Motorists Injured
10
Other Injured
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-09-01 to 2024-09-30 · 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 September 2024, the peak day for crashes was Wednesday with 152 incidents, a change from September 2023 when Friday was the peak with 186 crashes. The peak hour for collisions remained the 3 p.m. hour in both periods, though the number of crashes during this hour increased from 74 to 88 year-over-year.
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-09-01 to 2024-09-30 · Crash date field aggregated by weekday
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-09-01 to 2024-09-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes showed a mixed trend year-over-year. The number of fatal crashes increased from 3 to 4, with the fatal crash share rising from 0.3% to 0.4% of all incidents. While the proportion of crashes involving serious injuries decreased from 2.4% to 1.4%, crashes with minor injuries saw an increase in their share, rising from 13.3% to 17.6% of the total.
Outcome by Severity (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-09-01 to 2024-09-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-09-01 to 2024-09-30 · Most severe injury per crash record
Top Contributing Factors
A direct year-over-year comparison of contributing factors is not possible due to changes in how this data was categorized between the two periods. In September 2024, the leading factor was 'Failed to Yield Right-of-Way,' cited in 65 crashes (6.7% of total). In contrast, for September 2023, the data combined environmental and road conditions, with 'RAIN, SNOW, WET' being the top listed factor in 96 crashes (9.9% of total).
Officer-Reported Primary Contributing Cause
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-09-01 to 2024-09-30 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in adverse weather conditions increased in September 2024 compared to the previous year. The number of crashes during rain rose from 162 to 203, and those on wet road surfaces increased from 193 to 236. Despite this, the majority of crashes in both periods occurred in clear weather (641 in 2024 vs. 661 in 2023). The proportion of crashes in daylight was slightly higher in the current period, accounting for 69.1% of incidents versus 65.4% in the prior year.
Weather
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-09-01 to 2024-09-30 · Weather condition at time of crash
Lighting
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-09-01 to 2024-09-30 · Lighting condition field
Road Surface
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-09-01 to 2024-09-30 · Road surface condition field
Vehicles & Demographics
The top three vehicle makes involved in crashes remained consistent, with Toyota, Honda, and Ford leading in both September 2024 and 2023. A notable shift occurred in vehicle types, with Sport Utility Vehicles involved in 281 crashes this year compared to 147 in the prior year, an increase of 91%. Conversely, passenger car involvement decreased from 1,193 to 1,088 vehicles.
Top Vehicle Makes (1,675 vehicles)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-09-01 to 2024-09-30 · 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: 2024-09-01 through 2024-09-30
- Report generated: September 9, 2026
Data Coverage
- Reporting period: 2024-09-01 through 2024-09-30 (30 days)
- Geographic scope: montgomery, MD
- Total crash records analyzed: 968
- Total persons involved: 1,737
- Total vehicles involved: 1,675
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: September 2024." Published September 9, 2026. Reporting period: 2024-09-01 to 2024-09-30. 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/september-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: 2024-09-01 – 2024-09-30
Generated: September 9, 2026 · All rights reserved