Yearly Traffic Safety Analysis

13,552 CRASHES IN
VERMONT, VT
2019

All metrics benchmarked against2018

In 2019, there were 13,552 total crashes, a 31.9% increase from the 10,272 crashes recorded in 2018. While total collisions rose significantly, the number of fatalities decreased from 61 to 45. The most notable shift was the substantial year-over-year increase in the overall volume of reported crashes.

13,552

▲ 31.9%was 10,272

Total Crash Events

45

▼ -26.2%was 61

Fatal Crashes

1,951

▲ 2.5%was 1,904

Injury Crashes

45

▼ -26.2%was 61

Fatal Crash Events

Note: "Fatal Crashes" and "Injury Crashes" count crash events — this source publishes crash-level counts only, not individual persons. 3,785 crashes with unreported severity are not shown in the severity breakdown.

Source: Vermont Crash Data · Arcgis Open Data · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash data from 2019 indicates a rising trend in total collisions compared to the previous year. The total number of crashes increased by 31.9%, from 10,272 in 2018 to 13,552 in 2019. Despite this increase in overall crashes, fatalities saw a significant decrease of 26.2%, while total injuries experienced a slight rise of 2.5%.

When Crashes Happen

The temporal patterns of crashes remained consistent between 2018 and 2019, though the volume of incidents increased. Friday continued to be the peak day for crashes, with 2,387 incidents in 2019 compared to 1,861 in the prior year. Similarly, the 4 p.m. hour remained the peak time for collisions, rising from 878 crashes in 2018 to 1,121 in 2019.

Source: Vermont Crash Data · Arcgis Open Data · 2019-01-01 to 2019-12-31 · Crash date field aggregated by weekday

Source: Vermont Crash Data · Arcgis Open Data · 2019-01-01 to 2019-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes decreased in 2019 compared to the prior year, even as the total number of crashes rose. The fatal crash rate fell from 0.59% in 2018 to 0.33% in 2019, with 45 fatal crashes recorded, down from 61. Similarly, the proportion of crashes resulting in an injury declined from 18.5% in 2018 to 14.4% in 2019. While the absolute number of injuries saw a slight increase from 1,904 to 1,951, they represented a smaller share of the much larger total crash volume.

Outcome by Severity (Crash Events)

Fatal45fatal crashes0.3%
↓-26.2%prior 61
Injury1,951minor injury crashes14.4%
↑2.5%prior 1,904
No Injury7,771no injury crashes57.3%
↓-2.8%prior 7,994

Source: Vermont Crash Data · Arcgis Open Data · 2019-01-01 to 2019-12-31 · Severity derived from reported fatal/injury indicators (no KABCO A/B/C codes)

Severity Distribution (Crash Events)

Source: Vermont Crash Data · Arcgis Open Data · 2019-01-01 to 2019-12-31 · Most severe injury per crash record

Road & Environmental Conditions

The distribution of environmental conditions during crashes remained largely consistent between 2018 and 2019. Crashes in daylight conditions accounted for approximately 75% of the total in both years, with dark conditions making up about 24%. Among crashes where road surface conditions were recorded, dry roads were the most common setting, involved in 62.7% of such incidents in 2019 compared to 63.9% in 2018. The breakdown of weather conditions was also stable, with clear weather being the most frequently reported condition in both periods.

Weather

Clear4,532 (59.3%)
↑1.2%prior 4,480
Cloudy1,455 (19.1%)
↓-12.9%prior 1,671
Freezing Precipitation1,058 (13.9%)
↓-5.7%prior 1,122
Rain562 (7.4%)
↓-12.6%prior 643
Wind30 (0.4%)
↑87.5%prior 16

Source: Vermont Crash Data · Arcgis Open Data · 2019-01-01 to 2019-12-31 · Weather condition at time of crash

Lighting

Daylight10,121 (75.4%)
↑29.8%prior 7,797
Dark3,302 (24.6%)
↑39.9%prior 2,360

Source: Vermont Crash Data · Arcgis Open Data · 2019-01-01 to 2019-12-31 · Lighting condition field

Road Surface

Dry4,853 (62.7%)
↓-4.0%prior 5,053
Wet1,207 (15.6%)
↓-4.4%prior 1,263
Snow973 (12.6%)
↓-0.5%prior 978
Ice437 (5.6%)
↑13.8%prior 384
Slush149 (1.9%)
↓-11.3%prior 168
Sand, mud, dirt, oil, gravel57 (0.7%)
↑18.8%prior 48
Other - Explain in Narrative40 (0.5%)
↓-18.4%prior 49
Water (standing / moving)21 (0.3%)
↓-30.0%prior 30

Source: Vermont Crash Data · Arcgis Open Data · 2019-01-01 to 2019-12-31 · 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: 2019-01-01 through 2019-12-31
  • Report generated: July 5, 2026

Data Coverage

  • Reporting period: 2019-01-01 through 2019-12-31 (365 days)
  • Geographic scope: vermont, VT
  • Total crash records analyzed: 13,552

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: 2019." Published July 5, 2026. Reporting period: 2019-01-01 to 2019-12-31. Data source: Vermont Crash Data, Arcgis Open Data. Available at: https://thatcarhitme.com/crash-data/vermont/statewide/2019-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 — 2019 | ThatCarHitMe.com