Yearly Traffic Safety Analysis

34 CRASHES IN
RUTLAND TOWN, VT
2025

All metrics benchmarked against2024

In 2025, Rutland Town recorded 34 traffic crashes, a 26.1% decrease from the 46 crashes reported in 2024. Despite the overall reduction in collisions, the number of injuries increased slightly from 12 to 13. This resulted in a higher proportion of crashes involving injury, which rose from 26.1% of all incidents in the prior year to 38.2% in the current year.

34

-26.1%was 46

Total Crash Events

0

Fatal Crashes

13

8.3%was 12

Injury Crashes

0

Fatal Crash Events

Note: "Fatal Crashes" and "Injury Crashes" count crash events — this source publishes crash-level counts only, not individual persons.

Source: Vermont Crash Data · Arcgis Open Data · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crashes in Rutland Town showed a downward trend, decreasing by 26.1% from 46 incidents in 2024 to 34 in 2025. While the total number of crashes fell, the number of reported injuries increased from 12 to 13 year-over-year. This indicates that while collisions were less frequent, they were proportionally more likely to result in an injury.

When Crashes Happen

The temporal patterns of crashes shifted between the two periods. In 2025, the most frequent days for crashes were Monday and Friday, each with 9 incidents, whereas in 2024, Tuesday was the peak day with 11 crashes. The peak hour for collisions also changed, moving from 12 p.m. (9 crashes) in the prior year to 11 a.m. (5 crashes) in the current year.

Source: Vermont Crash Data · Arcgis Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday

Source: Vermont Crash Data · Arcgis Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity analysis shows that while there were no fatal crashes in either 2025 or 2024, the proportion of injury-related incidents increased. In 2025, 38.2% of crashes (13 out of 34) resulted in an injury. This is a notable increase from 2024, when 26.1% of crashes (12 out of 46) involved an injury.

Outcome by Severity (Crash Events)

Injury13minor injury crashes38.2%
8.3%prior 12
No Injury21no injury crashes61.8%
-38.2%prior 34

Source: Vermont Crash Data · Arcgis Open Data · 2025-01-01 to 2025-12-31 · Severity derived from reported fatal/injury indicators (no KABCO A/B/C codes)

Severity Distribution (Crash Events)

Source: Vermont Crash Data · Arcgis Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record

Road & Environmental Conditions

In both 2025 and 2024, the majority of crashes occurred in daylight on dry roads. The proportion of crashes happening in daylight conditions increased from 80.4% in 2024 to 85.3% in 2025. Collisions on dry road surfaces also saw a proportional increase, accounting for 67.6% of crashes in 2025 compared to 63.0% in the prior year, while crashes on wet roads decreased from 5 to 2.

Weather

Clear14 (53.8%)
-17.6%prior 17
Cloudy10 (38.5%)
-23.1%prior 13
Freezing Precipitation2 (7.7%)

Source: Vermont Crash Data · Arcgis Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash

Lighting

Daylight29 (85.3%)
-21.6%prior 37
Dark5 (14.7%)
-44.4%prior 9

Source: Vermont Crash Data · Arcgis Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field

Road Surface

Dry23 (85.2%)
-20.7%prior 29
Snow2 (7.4%)
Wet2 (7.4%)
-60.0%prior 5

Source: Vermont Crash Data · Arcgis Open Data · 2025-01-01 to 2025-12-31 · 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: 2025-01-01 through 2025-12-31
  • Report generated: July 5, 2026

Data Coverage

  • Reporting period: 2025-01-01 through 2025-12-31 (365 days)
  • Geographic scope: Rutland Town, VT
  • Total crash records analyzed: 34

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). "Rutland Town, VT Crash Intelligence Report: 2025." Published July 5, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Vermont Crash Data, Arcgis Open Data. Available at: https://thatcarhitme.com/crash-data/vermont/rutland-town/2025-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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Rutland Town, VT Crash Report — 2025 | ThatCarHitMe.com