Monthly Traffic Safety Analysis

868 CRASHES IN
MONTGOMERY, MD
JANUARY 2024

All metrics benchmarked againstJanuary 2023

In January 2024, Montgomery County recorded 868 total crashes, a 4.6% increase from the 830 crashes in January 2023. While overall crashes and injuries rose, fatalities fell from two to zero. The most significant year-over-year change was a dramatic decrease in reported hit-and-run incidents, which fell from 183 to 17.

868

4.6%was 830

Total Crash Events

0

-100.0%was 2

Persons Killed

277

10.8%was 250

Persons Injured

17

-90.7%was 183

Hit-and-Run Crashes

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 36 crashes with unreported severity are not shown in the severity breakdown.

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-01-01 to 2024-01-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash trends in Montgomery County showed a year-over-year increase for the month of January. Total crashes rose by 4.6%, from 830 to 868, and the number of people injured increased by 10.8%, from 250 to 277. In contrast, the number of fatalities recorded decreased from two in January 2023 to zero in January 2024.

17

Hit-and-Run Crashes — January 2024

-90.7% vs prior (183)

There was a substantial year-over-year decrease in reported hit-and-run crashes. The number of incidents fell from 183 in January 2023 to 17 in January 2024. This corresponds to a sharp decline in the hit-and-run rate, which dropped from 22% of all crashes in the prior year period to just 2% in the current period.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

0

Motorists Killed

Prior: 2-100.0%

0

Other Killed

Prior: 00.0%

45

Pedestrians Injured

Prior: 450.0%

1

Cyclists Injured

Prior: 4-75.0%

228

Motorists Injured

Prior: 20014.0%

3

Other Injured

Prior: 1200.0%

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-01-01 to 2024-01-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes shifted slightly between the two periods. In January 2024, the peak day for crashes was Monday with 158 incidents, a change from the prior year's peak on Tuesday, which saw 150 crashes. The peak hour also moved slightly earlier, from 6 PM (63 crashes) in 2023 to 5 PM (62 crashes) in 2024.

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-01-01 to 2024-01-31 · Crash date field aggregated by weekday

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-01-01 to 2024-01-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes increased, their severity profile showed a positive trend regarding fatalities, with fatal crashes dropping from two to one and total fatalities falling from two to zero. The proportion of crashes involving serious injuries increased slightly from 1.2% to 1.6% of all incidents, and minor injury crashes rose from 10.6% to 14.4%.

Severity is per crash event (most severe injury). 1 fatal crash events resulted in 0 persons killed.

Outcome by Severity (Crash Events)

Serious Injury14serious injury crashes1.6%
40.0%prior 10
Minor Injury125minor injury crashes14.4%
42.0%prior 88
Possible Injury88possible injury crashes10.1%
-22.8%prior 114
No Injury605no injury crashes69.7%
-1.1%prior 612

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-01-01 to 2024-01-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-01-01 to 2024-01-31 · Most severe injury per crash record

Top Contributing Factors

Officer-Reported Primary Contributing Cause

Failed to Yield Right-of-Way87 (10%)
Too Fast For Conditions48 (5.5%)
Other Improper Action48 (5.5%)
Followed Too Closely44 (5.1%)
Operated Motor Vehicle in Inattentive, Careless, Negligent, or Erratic Manner26 (3%)
Failed to Keep in Proper Lane18 (2.1%)
Swerved or Avoided Due to Wind, Slippery Surface, Motor Vehicle, Object, Non-Motorist in Roadway, etc17 (2%)
Ran Red Light16 (1.8%)
Improper Turn16 (1.8%)
Improper Backing16 (1.8%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-01-01 to 2024-01-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The most notable difference in conditions was the impact of winter weather. January 2024 saw 98 crashes occur in snow or freezing rain, with 108 total crashes on roads affected by snow, ice, or slush; these conditions were largely absent in the prior year's data. Consequently, the proportion of crashes on dry roads decreased from 59.0% to 51.6% year-over-year. Crashes in daylight remained the most common scenario, accounting for 53.6% of incidents in 2024 versus 51.4% in 2023.

Weather

Clear559 (65.2%)
12.0%prior 499
Rain108 (12.6%)
-19.4%prior 134
Cloudy87 (10.2%)
-13.9%prior 101
Snow73 (8.5%)
Blowing Snow16 (1.9%)
Freezing Rain Or Freezing Drizzle7 (0.8%)
Fog, Smog, Smoke4 (0.5%)
-55.6%prior 9
Sleet Or Hail2 (0.2%)
Severe Crosswinds1 (0.1%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-01-01 to 2024-01-31 · Weather condition at time of crash

Lighting

Daylight465 (54.2%)
8.9%prior 427
Dark - Lighted292 (34.0%)
-3.3%prior 302
Dark - Not Lighted51 (5.9%)
75.9%prior 29
Dawn21 (2.4%)
-8.7%prior 23
Other12 (1.4%)
Dusk12 (1.4%)
-42.9%prior 21
Dark - Unknown Lighting5 (0.6%)
-54.5%prior 11

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-01-01 to 2024-01-31 · Lighting condition field

Road Surface

Dry448 (61.0%)
-8.6%prior 490
Wet171 (23.3%)
-16.6%prior 205
Snow52 (7.1%)
Ice/Frost41 (5.6%)
Slush15 (2.0%)
Other4 (0.5%)
Sand2 (0.3%)
Water (standing, moving)1 (0.1%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-01-01 to 2024-01-31 · Road surface condition field

Vehicles & Demographics

The top three vehicle makes involved in crashes—Toyota, Honda, and Ford—remained the same in both January 2023 and January 2024. A notable shift occurred in vehicle types, with Sport Utility Vehicles (SUVs) seeing a 47% increase in crash involvement, from 148 incidents to 218. Conversely, the number of passenger cars involved in crashes decreased from 1,007 to 970.

Top Vehicle Makes (1,483 vehicles)

1
TOYOTA274 (18.5%)
44.2%prior 190
2
HONDA203 (13.7%)
23.8%prior 164
3
FORD137 (9.2%)
9.6%prior 125
4
NISSAN113 (7.6%)
88.3%prior 60
5
CHEVROLET84 (5.7%)
180.0%prior 30
6
HYUNDAI66 (4.5%)
100.0%prior 33
7
MERCEDES-BENZ37 (2.5%)
8
VOLKSWAGEN37 (2.5%)
428.6%prior 7
9
JEEP35 (2.4%)
-25.5%prior 47
10
DODGE34 (2.3%)
-8.1%prior 37

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-01-01 to 2024-01-31 · 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: 2024-01-01 through 2024-01-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2024-01-01 through 2024-01-31 (31 days)
  • Geographic scope: montgomery, MD
  • Total crash records analyzed: 868
  • Total persons involved: 1,540
  • Total vehicles involved: 1,483

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: January 2024." Published September 9, 2026. Reporting period: 2024-01-01 to 2024-01-31. 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/january-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

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