Yearly Traffic Safety Analysis

73 CRASHES IN
RUTLAND TOWN, VT
2014

All metrics benchmarked against2013

Rutland Town experienced an increase in total crashes, rising from 64 in 2013 to 73 in 2014, a 14.06% increase year-over-year. The most significant shift was an 80% decrease in DUI crashes, dropping from 5 in 2013 to 1 in 2014. Total injuries saw a slight increase from 18 to 19 during the same period.

73

14.1%was 64

Total Crash Events

0

Fatal Crashes

19

5.6%was 18

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. 15 crashes with unreported severity are not shown in the severity breakdown.

Source: Vermont Crash Data · Arcgis Open Data · 2014-01-01 to 2014-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crashes in Rutland Town showed an upward trend, increasing by 14.06% from 64 total crashes in 2013 to 73 in 2014. Total injuries also saw a modest rise of 5.56%, from 18 injured persons in 2013 to 19 in 2014.

When Crashes Happen

The peak day for crashes shifted from Friday in 2013, with 16 incidents, to Monday in 2014, with 15 incidents. The peak crash hour also changed, moving from 12p with 9 crashes in 2013 to 4p with 7 crashes in 2014.

Source: Vermont Crash Data · Arcgis Open Data · 2014-01-01 to 2014-12-31 · Crash date field aggregated by weekday

Source: Vermont Crash Data · Arcgis Open Data · 2014-01-01 to 2014-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There were no reported fatalities in either 2013 or 2014. The proportion of injury crashes slightly decreased from 28.1% of total crashes in 2013 to 26% in 2014, even as the absolute number of injuries increased from 18 to 19.

Outcome by Severity (Crash Events)

Injury19minor injury crashes26%
5.6%prior 18
No Injury39no injury crashes53.4%
39.3%prior 28

Source: Vermont Crash Data · Arcgis Open Data · 2014-01-01 to 2014-12-31 · Severity derived from reported fatal/injury indicators (no KABCO A/B/C codes)

Severity Distribution (Crash Events)

Source: Vermont Crash Data · Arcgis Open Data · 2014-01-01 to 2014-12-31 · Most severe injury per crash record

Road & Environmental Conditions

Crashes occurring in dark conditions increased from 15 in 2013 to 22 in 2014, rising from 23.4% to 30.1% of all crashes. Concurrently, crashes on wet roads doubled from 3 in 2013 to 6 in 2014, and those on snowy roads tripled from 1 to 3, indicating a proportional increase in crashes under adverse road surface conditions.

Weather

Clear33 (78.6%)
17.9%prior 28
Cloudy5 (11.9%)
-58.3%prior 12
Freezing Precipitation2 (4.8%)
Rain2 (4.8%)

Source: Vermont Crash Data · Arcgis Open Data · 2014-01-01 to 2014-12-31 · Weather condition at time of crash

Lighting

Daylight51 (69.9%)
4.1%prior 49
Dark22 (30.1%)
46.7%prior 15

Source: Vermont Crash Data · Arcgis Open Data · 2014-01-01 to 2014-12-31 · Lighting condition field

Road Surface

Dry31 (73.8%)
-11.4%prior 35
Wet6 (14.3%)
Snow3 (7.1%)
Sand, mud, dirt, oil, gravel2 (4.8%)

Source: Vermont Crash Data · Arcgis Open Data · 2014-01-01 to 2014-12-31 · 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: 2014-01-01 through 2014-12-31
  • Report generated: July 5, 2026

Data Coverage

  • Reporting period: 2014-01-01 through 2014-12-31 (365 days)
  • Geographic scope: Rutland Town, VT
  • Total crash records analyzed: 73

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: 2014." Published July 5, 2026. Reporting period: 2014-01-01 to 2014-12-31. Data source: Vermont Crash Data, Arcgis Open Data. Available at: https://thatcarhitme.com/crash-data/vermont/rutland-town/2014-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 — 2014 | ThatCarHitMe.com