ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · MONTGOMERY, MD · MARCH 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/march-2023-report
Monthly Traffic Safety Analysis
880 CRASHES IN
MONTGOMERY, MD
MARCH 2023
In March 2023, Montgomery County recorded 880 total crashes, a 5.5% increase from the 834 crashes in March 2022. While overall crashes rose modestly, the most significant year-over-year change was a rise in crash-related fatalities, which increased from one in the prior period to three in the current period. Total injuries also saw an increase from 273 to 294.
880
▲ 5.5%was 834
Total Crash Events
3
▲ 200.0%was 1
Persons Killed
294
▲ 7.7%was 273
Persons Injured
163
▼ -17.3%was 197
Hit-and-Run Crashes
Note: "Persons Killed" (3) 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. 2 crashes with unreported severity are not shown in the severity breakdown.
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-03-01 to 2023-03-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Crash trends in Montgomery County for March showed a year-over-year increase. Total crashes rose by 5.5%, from 834 in March 2022 to 880 in March 2023. This upward trend was also reflected in crash outcomes, with total injuries increasing by 7.7% from 273 to 294, and fatalities rising from one to three.
163
Hit-and-Run Crashes — March 2023
▼ -17.3% vs prior (197)
The number of hit-and-run incidents decreased in March 2023 compared to the same month in the prior year. The total count of hit-and-run crashes fell by 17.3%, from 197 to 163. This downward trend is also reflected in the hit-and-run rate, which dropped from 23.6% of all crashes in March 2022 to 18.5% in March 2023.
Vulnerable Road User Casualties
2
Pedestrians Killed
0
Cyclists Killed
1
Motorists Killed
0
Other Killed
42
Pedestrians Injured
4
Cyclists Injured
246
Motorists Injured
2
Other Injured
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-03-01 to 2023-03-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes remained largely consistent year-over-year. Friday was the peak day for crashes in both March 2023 (179 crashes) and March 2022 (142 crashes). The peak hour for collisions shifted slightly, moving from the 3 p.m. hour in the prior year (71 crashes) to the 4 p.m. hour in the current year (86 crashes).
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-03-01 to 2023-03-31 · Crash date field aggregated by weekday
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-03-01 to 2023-03-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Crash severity worsened in March 2023 compared to the previous year. The number of fatal crashes increased from one to four, and the total number of fatalities rose from one to three. Consequently, the fatal crash rate increased from 0.12% to 0.45% of all crashes. While the share of serious injury crashes remained stable at 2.2%, the count of possible injury crashes grew from 118 to 138.
Severity is per crash event (most severe injury). 4 fatal crash events resulted in 3 persons killed.
Outcome by Severity (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-03-01 to 2023-03-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-03-01 to 2023-03-31 · Most severe injury per crash record
Top Contributing Factors
An analysis of contributing factors shows a shift in the most common crash circumstances between the two periods. In March 2023, the leading factor was 'RAIN, SNOW, WET' with 73 crashes, a 151.7% increase in count from 29 crashes in the prior year. The second most common factor, 'N/A, WET', also saw its count increase from 45 to 70. This indicates a notable rise in crashes where wet conditions were cited as a factor.
Officer-Reported Primary Contributing Cause
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-03-01 to 2023-03-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Comparing environmental conditions, crashes occurring in the rain more than doubled, increasing from 61 in March 2022 to 144 in March 2023. Correspondingly, collisions on wet road surfaces increased from 127 to 184. Crashes in daylight conditions also rose from 533 to 606, while those in 'Dark - Lighted' conditions decreased from 226 to 202.
Weather
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-03-01 to 2023-03-31 · Weather condition at time of crash
Lighting
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-03-01 to 2023-03-31 · Lighting condition field
Road Surface
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-03-01 to 2023-03-31 · Road surface condition field
Vehicles & Demographics
The types of vehicles involved in crashes remained consistent, with passenger cars, (sport) utility vehicles, and pickup trucks being the most frequent in both periods. The top three vehicle makes were Toyota, Honda, and Ford in both March 2023 and March 2022. There was a minor change in ranking, with Honda (174 vehicles) surpassing Ford (148 vehicles) for the second position in the current period, compared to the prior period where Ford (165 vehicles) was second and Honda (142 vehicles) was third.
Top Vehicle Makes (1,562 vehicles)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-03-01 to 2023-03-31 · 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: 2023-03-01 through 2023-03-31
- Report generated: September 9, 2026
Data Coverage
- Reporting period: 2023-03-01 through 2023-03-31 (31 days)
- Geographic scope: montgomery, MD
- Total crash records analyzed: 880
- Total persons involved: 1,616
- Total vehicles involved: 1,562
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: March 2023." Published September 9, 2026. Reporting period: 2023-03-01 to 2023-03-31. 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/march-2023-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: 2023-03-01 – 2023-03-31
Generated: September 9, 2026 · All rights reserved