Yearly Traffic Safety Analysis

7,177 CRASHES IN
VERMONT, VT
2023

All metrics benchmarked against2022

In 2023, Vermont recorded 7,177 total traffic crashes, a 14.9% decrease from the 8,433 crashes documented in 2022. While overall crashes, fatalities, and injuries declined, one notable year-over-year change was a 24.1% increase in motorcycle-involved collisions, which rose from 166 in 2022 to 206 in 2023.

7,177

-14.9%was 8,433

Total Crash Events

65

-11.0%was 73

Fatal Crashes

1,667

-6.5%was 1,783

Injury Crashes

65

-11.0%was 73

Fatal Crash Events

Note: "Fatal Crashes" and "Injury Crashes" count crash events — this source publishes crash-level counts only, not individual persons. 116 crashes with unreported severity are not shown in the severity breakdown.

Source: Vermont Crash Data · Arcgis Open Data · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic safety trends in Vermont showed improvement from 2022 to 2023. Total crashes decreased by 14.9%, falling from 8,433 to 7,177. This downward trend was also reflected in crash outcomes, with total fatalities declining by 11.0% (from 73 to 65) and total injuries decreasing by 6.5% (from 1,783 to 1,667).

When Crashes Happen

The temporal patterns of crashes showed some consistency and minor shifts between 2022 and 2023. Friday remained the peak day for crashes in both years, though the count fell from 1,435 in 2022 to 1,263 in 2023. The peak hour for collisions shifted slightly later in the day, moving from the 3 p.m. hour in 2022 (735 crashes) to the 4 p.m. hour in 2023 (636 crashes).

Source: Vermont Crash Data · Arcgis Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday

Source: Vermont Crash Data · Arcgis Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the total number of crashes decreased, the severity profile of those crashes shifted. The proportion of crashes resulting in a fatality remained stable at 0.9% in both 2022 and 2023. However, the share of crashes involving an injury increased, rising from 21.1% of all collisions in 2022 to 23.2% in 2023. The fatal crash rate per 100 crashes also saw a slight increase from 0.87 to 0.91.

Outcome by Severity (Crash Events)

Fatal65fatal crashes0.9%
-11.0%prior 73
Injury1,667minor injury crashes23.2%
-6.5%prior 1,783
No Injury5,329no injury crashes74.3%
-4.2%prior 5,564

Source: Vermont Crash Data · Arcgis Open Data · 2023-01-01 to 2023-12-31 · Severity derived from reported fatal/injury indicators (no KABCO A/B/C codes)

Severity Distribution (Crash Events)

Source: Vermont Crash Data · Arcgis Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record

Road & Environmental Conditions

Crash conditions showed some proportional shifts despite the overall decrease in crash volume. The distribution of crashes by lighting conditions remained nearly unchanged, with approximately 75% of collisions in both years occurring during daylight. In terms of road surface, a larger share of 2023's crashes occurred on dry roads (53.7%) compared to 2022 (48.2%). Similarly, the proportion of crashes in clear weather was slightly higher in 2023 (46.7%) than in 2022 (44.7%).

Weather

Clear3,355 (61.1%)
-11.0%prior 3,771
Cloudy1,091 (19.9%)
9.3%prior 998
Freezing Precipitation550 (10.0%)
-15.4%prior 650
Rain494 (9.0%)
37.2%prior 360
Wind5 (0.1%)
-72.2%prior 18

Source: Vermont Crash Data · Arcgis Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash

Lighting

Daylight5,376 (75.8%)
-15.5%prior 6,359
Dark1,718 (24.2%)
-14.4%prior 2,008

Source: Vermont Crash Data · Arcgis Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field

Road Surface

Dry3,855 (69.9%)
-5.2%prior 4,066
Wet895 (16.2%)
13.7%prior 787
Snow441 (8.0%)
-31.8%prior 647
Ice179 (3.2%)
5.3%prior 170
Slush69 (1.3%)
-9.2%prior 76
Sand, mud, dirt, oil, gravel38 (0.7%)
-17.4%prior 46
Other - Explain in Narrative23 (0.4%)
-34.3%prior 35
Water (standing / moving)17 (0.3%)
13.3%prior 15

Source: Vermont Crash Data · Arcgis Open Data · 2023-01-01 to 2023-12-31 · 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: 2023-01-01 through 2023-12-31
  • Report generated: July 5, 2026

Data Coverage

  • Reporting period: 2023-01-01 through 2023-12-31 (365 days)
  • Geographic scope: vermont, VT
  • Total crash records analyzed: 7,177

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: 2023." Published July 5, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Vermont Crash Data, Arcgis Open Data. Available at: https://thatcarhitme.com/crash-data/vermont/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

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Vermont (Statewide) Crash Report — 2023 | ThatCarHitMe.com