ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · MONTGOMERY, MD · 2023
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/2023-annual-report
Yearly Traffic Safety Analysis
10,776 CRASHES IN
MONTGOMERY, MD
2023
In 2023, Montgomery County recorded 10,776 total vehicle crashes, a 7.2% increase from the 10,056 crashes reported in 2022. Despite the rise in overall collisions, the number of fatalities decreased from 39 in 2022 to 31 in 2023, a 20.5% reduction. Total injuries saw a 5.2% increase from 3,464 to 3,644 over the same period.
10,776
▲ 7.2%was 10,056
Total Crash Events
31
▼ -20.5%was 39
Persons Killed
3,644
▲ 5.2%was 3,464
Persons Injured
2,261
▲ 8.2%was 2,089
Hit-and-Run Crashes
Note: "Persons Killed" (31) counts individual fatalities across all crash events. "Fatal" in the severity table below (34) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 48 crashes with unreported severity are not shown in the severity breakdown.
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall crash trends in Montgomery County show an increase year-over-year. Total crashes rose by 7.2%, from 10,056 in 2022 to 10,776 in 2023, and total injuries increased by 5.2% from 3,464 to 3,644. However, fatalities saw a notable decrease, falling 20.5% from 39 in the prior year to 31 in the current year.
2,261
Hit-and-Run Crashes — 2023
▲ 8.2% vs prior (2,089)
Hit-and-run incidents increased in both absolute count and as a percentage of total crashes from 2022 to 2023. The total number of hit-and-run crashes rose by 8.2%, from 2,089 to 2,261. This growth slightly outpaced the overall increase in collisions, causing the hit-and-run rate to edge upward from 20.8% of all crashes in 2022 to 21.0% in 2023.
Vulnerable Road User Casualties
13
Pedestrians Killed
1
Cyclists Killed
17
Motorists Killed
0
Other Killed
431
Pedestrians Injured
111
Cyclists Injured
3,063
Motorists Injured
39
Other Injured
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes showed some shifts between 2022 and 2023. While Friday remained the peak day for crashes in both years (1,666 in 2022 and 1,689 in 2023), the peak hour for collisions moved earlier in the day. In 2023, the highest number of crashes occurred at 3 PM with 815 incidents, a change from the 5 PM peak, which saw 735 incidents in 2022.
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes shifted slightly year-over-year, with a notable decrease in the most severe outcomes. The fatal crash rate fell from 0.41% of all crashes in 2022 to 0.32% in 2023, with total fatalities dropping from 39 to 31. The proportion of crashes resulting in serious or minor injuries remained stable at 1.9% and 11.4% respectively for both years, though the absolute number of these injuries increased with the overall rise in crashes.
Severity is per crash event (most severe injury). 34 fatal crash events resulted in 31 persons killed.
Outcome by Severity (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factors related to road and environmental conditions remained consistent between 2022 and 2023. The top factor in both years was 'RAIN, SNOW, WET,' with the count of associated crashes increasing by 4.8% from 650 to 681. The second-ranked factor, 'N/A, WET,' also saw a growth in incidents, with its crash count rising 9.7% from 473 to 519. Notably, crashes involving animals ('ANIMAL, N/A') increased by 23.3% in count, from 60 to 74 incidents, making it the third most common factor in 2023.
Officer-Reported Primary Contributing Cause
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The distribution of crashes across different environmental conditions remained largely stable from 2022 to 2023. Crashes in clear weather and on dry roads continued to constitute the vast majority, accounting for 70.8% and 69.4% of all incidents in 2023, respectively, similar to their shares in the prior year. The number of crashes occurring in the rain increased from 1,131 to 1,254, and those on wet surfaces rose from 1,535 to 1,663, tracking the overall increase in total crashes.
Weather
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash
Lighting
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field
Road Surface
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field
Vehicles & Demographics
The types of vehicles involved in crashes showed a consistent pattern year-over-year, with increases in volume across most major categories. Passenger cars remained the most frequently involved vehicle type, with their count rising from 12,042 in 2022 to 13,184 in 2023. The top three vehicle makes involved in collisions were unchanged: Toyota, Honda, and Ford. While the count for Toyota-involved vehicles remained nearly flat (from 2,485 to 2,465), crashes involving Hondas increased from 1,960 to 2,078.
Top Vehicle Makes (18,929 vehicles)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-01-01 to 2023-12-31 · 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: 2023-01-01 through 2023-12-31
- Report generated: September 9, 2026
Data Coverage
- Reporting period: 2023-01-01 through 2023-12-31 (365 days)
- Geographic scope: montgomery, MD
- Total crash records analyzed: 10,776
- Total persons involved: 19,599
- Total vehicles involved: 18,929
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: 2023." Published September 9, 2026. Reporting period: 2023-01-01 to 2023-12-31. 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/2023-annual-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: 2023-01-01 – 2023-12-31
Generated: September 9, 2026 · All rights reserved