ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · MONTGOMERY, MD · OCTOBER 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/october-2025-report
Monthly Traffic Safety Analysis
992 CRASHES IN
MONTGOMERY, MD
OCTOBER 2025
In October 2025, Montgomery County recorded 992 total traffic crashes, a 2.0% increase from the 973 crashes reported in October 2024. Despite the rise in total incidents, the number of fatalities decreased by 50%, falling from 4 in the prior year to 2 in the current period. Crashes involving pedestrians also saw a notable decline, dropping by 31.3% from 67 to 46 year-over-year.
992
▲ 2.0%was 973
Total Crash Events
2
▼ -50.0%was 4
Persons Killed
353
▲ 3.2%was 342
Persons Injured
25
▲ 8.7%was 23
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. 32 crashes with unreported severity are not shown in the severity breakdown.
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-10-01 to 2025-10-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall traffic collisions in Montgomery County showed a slight upward trend in October 2025 compared to the same month in the previous year. Total crashes increased by 2.0%, from 973 to 992. While the number of injuries also rose by 3.2% from 342 to 353, fatalities were halved, decreasing from 4 to 2.
25
Hit-and-Run Crashes — October 2025
▲ 8.7% vs prior (23)
The incidence of hit-and-run crashes saw a slight increase in both absolute numbers and as a percentage of total crashes. In October 2025, there were 25 hit-and-run incidents, up from 23 in October 2024. This represents a hit-and-run rate of 2.5% of all crashes, a marginal increase from the 2.4% rate recorded in the prior year.
Vulnerable Road User Casualties
0
Pedestrians Killed
1
Cyclists Killed
1
Motorists Killed
0
Other Killed
43
Pedestrians Injured
11
Cyclists Injured
293
Motorists Injured
6
Other Injured
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-10-01 to 2025-10-31 · 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 October 2025, the highest number of crashes occurred on Fridays with 182 incidents, and the peak hour for collisions was 3 p.m. with 94 incidents. This contrasts with October 2024, when Wednesday was the most frequent day for crashes (190 incidents) and the peak hour was 4 p.m. (75 incidents).
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-10-01 to 2025-10-31 · Crash date field aggregated by weekday
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-10-01 to 2025-10-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes saw a notable shift toward less severe outcomes year-over-year. The number of fatal crashes was halved from 4 to 2, and serious injury crashes declined from 31 to 25. Consequently, the share of fatal crashes dropped from 0.4% to 0.2% of all incidents. Crashes resulting in possible injuries increased from 112 to 127, representing 12.8% of all crashes in October 2025 compared to 11.5% in the prior year.
Outcome by Severity (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-10-01 to 2025-10-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-10-01 to 2025-10-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factors cited in crashes remained consistent, with 'Failed to Yield Right-of-Way' being the most common factor in both periods, increasing slightly from 75 to 77 incidents. 'Other Improper Action' (44 to 49 incidents) and 'Followed Too Closely' (37 to 38 incidents) also held their rankings as the second and third most frequent factors. A notable increase was observed in crashes attributed to 'Failed to Keep in Proper Lane,' which rose by 66.7% from 15 incidents in October 2024 to 25 in October 2025.
Officer-Reported Primary Contributing Cause
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-10-01 to 2025-10-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
While most crashes in both periods occurred in clear weather, there was a significant increase in crashes under adverse conditions. The number of collisions happening in the rain rose from 37 in October 2024 to 69 in October 2025. Correspondingly, crashes on wet road surfaces increased by 87.8%, from 49 to 92 incidents. The distribution of crashes by lighting conditions remained largely stable, with about two-thirds of incidents in both years occurring during daylight hours.
Weather
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-10-01 to 2025-10-31 · Weather condition at time of crash
Lighting
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-10-01 to 2025-10-31 · Lighting condition field
Road Surface
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-10-01 to 2025-10-31 · Road surface condition field
Vehicles & Demographics
The types of vehicles involved in crashes were consistent year-over-year, with Passenger Cars (1,134 in 2025 vs. 1,106 in 2024) and Sport Utility Vehicles (256 vs. 277) being the most common. The top three vehicle makes involved in collisions also remained the same: Toyota, Honda, and Ford. While Toyota and Honda held their positions with similar counts, Ford's involvement decreased from 172 vehicles in October 2024 to 132 in October 2025.
Top Vehicle Makes (1,740 vehicles)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2025-10-01 to 2025-10-31 · 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: 2025-10-01 through 2025-10-31
- Report generated: September 9, 2026
Data Coverage
- Reporting period: 2025-10-01 through 2025-10-31 (31 days)
- Geographic scope: montgomery, MD
- Total crash records analyzed: 992
- Total persons involved: 1,807
- Total vehicles involved: 1,740
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: October 2025." Published September 9, 2026. Reporting period: 2025-10-01 to 2025-10-31. 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/october-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: 2025-10-01 – 2025-10-31
Generated: September 9, 2026 · All rights reserved