Monthly Traffic Safety Analysis

870 CRASHES IN
VERMONT, VT
JANUARY 2026

All metrics benchmarked againstJanuary 2025

In January 2026, Vermont recorded 870 traffic crashes, representing a 13.7% increase from the 765 crashes documented in January 2025. Despite this rise in total collisions, the most notable year-over-year shift was a significant decrease in fatalities, which fell from 8 in the prior year to 3 in the current period. The number of injuries saw a modest increase from 150 to 160.

870

▲ 13.7%was 765

Total Crash Events

3

▼ -62.5%was 8

Fatal Crashes

160

▲ 6.7%was 150

Injury Crashes

3

▼ -62.5%was 8

Fatal Crash Events

Note: "Fatal Crashes" and "Injury Crashes" count crash events — this source publishes crash-level counts only, not individual persons. 31 crashes with unreported severity are not shown in the severity breakdown.

Source: Vermont Crash Data · Arcgis Open Data · 2026-01-01 to 2026-01-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Year-over-year data indicates a rising trend in the total number of traffic incidents for the month of January. Crashes increased by 13.7%, from 765 in January 2025 to 870 in January 2026. While total collisions and reported injuries (up 6.7%) both increased, fatalities saw a notable decrease of 62.5% over the same period.

When Crashes Happen

The temporal patterns of crashes remained broadly consistent year-over-year, with Thursday being the peak day for collisions in both January 2025 (129 crashes) and January 2026 (151 crashes). The peak hour for crashes shifted slightly earlier in the day, moving from 4 PM in the prior year (67 crashes) to 3 PM in the current year (77 crashes). The afternoon hours consistently represented the period with the highest crash frequency in both years.

Source: Vermont Crash Data · Arcgis Open Data · 2026-01-01 to 2026-01-31 · Crash date field aggregated by weekday

Source: Vermont Crash Data · Arcgis Open Data · 2026-01-01 to 2026-01-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes increased, the overall severity of incidents decreased compared to the previous year. The number of fatal crashes fell from 8 in January 2025 to 3 in January 2026, with the proportion of crashes involving a fatality dropping from 1.0% to 0.3%. Similarly, while the absolute number of injury-related crashes rose from 150 to 160, their share of all crashes declined from 19.6% to 18.4%.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.3%
↓-62.5%prior 8
Injury160minor injury crashes18.4%
↑6.7%prior 150
No Injury676no injury crashes77.7%
↑12.9%prior 599

Source: Vermont Crash Data · Arcgis Open Data · 2026-01-01 to 2026-01-31 · Severity derived from reported fatal/injury indicators (no KABCO A/B/C codes)

Severity Distribution (Crash Events)

Source: Vermont Crash Data · Arcgis Open Data · 2026-01-01 to 2026-01-31 · Most severe injury per crash record

Road & Environmental Conditions

An analysis of crash conditions reveals a significant shift in the prevalent road surface types between the two periods. In January 2026, snowy roads were the most common condition, accounting for 32.4% of crashes, whereas dry roads were most common in the prior year at 32.4% of crashes. Notably, the proportion of crashes occurring on icy surfaces more than doubled, increasing from 2.7% of all crashes in January 2025 to 7.2% in January 2026. Lighting conditions remained comparable, with daylight crashes accounting for 73.2% and 75.6% of incidents in the prior and current periods, respectively.

Weather

Clear234 (39.3%)
↓-14.9%prior 275
Freezing Precipitation215 (36.1%)
↑12.0%prior 192
Cloudy140 (23.5%)
↑13.8%prior 123
Rain5 (0.8%)
→0.0%prior 5
Wind2 (0.3%)
↓-60.0%prior 5

Source: Vermont Crash Data · Arcgis Open Data · 2026-01-01 to 2026-01-31 · Weather condition at time of crash

Lighting

Daylight658 (76.6%)
↑17.5%prior 560
Dark201 (23.4%)
↑1.0%prior 199

Source: Vermont Crash Data · Arcgis Open Data · 2026-01-01 to 2026-01-31 · Lighting condition field

Road Surface

Snow282 (46.0%)
↑25.9%prior 224
Dry144 (23.5%)
↓-41.9%prior 248
Wet96 (15.7%)
↑37.1%prior 70
Ice63 (10.3%)
↑200.0%prior 21
Slush18 (2.9%)
↓-18.2%prior 22
Other - Explain in Narrative7 (1.1%)
→0.0%prior 7
Sand, mud, dirt, oil, gravel3 (0.5%)

Source: Vermont Crash Data · Arcgis Open Data · 2026-01-01 to 2026-01-31 · Road surface condition field

Data Sources & Methodology

Primary Data Source

All crash data in this report is sourced from Vermont Crash Data, accessed programmatically via the Arcgis 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: Arcgis Open Data API (SoQL queries)
  • Data format: Structured JSON via REST API
  • Record types queried: Crash events, person records, and vehicle unit records
  • Date filter applied: 2026-01-01 through 2026-01-31
  • Report generated: July 5, 2026

Data Coverage

  • Reporting period: 2026-01-01 through 2026-01-31 (31 days)
  • Geographic scope: vermont, VT
  • Total crash records analyzed: 870

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). "vermont, VT Crash Intelligence Report: January 2026." Published July 5, 2026. Reporting period: 2026-01-01 to 2026-01-31. Data source: Vermont Crash Data, Arcgis Open Data. Available at: https://thatcarhitme.com/crash-data/vermont/statewide/january-2026-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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Vermont (Statewide) Crash Report — January 2026 | ThatCarHitMe.com