Yearly Traffic Safety Analysis

131 CRASHES IN
MORRISTOWN, VT
2016

All metrics benchmarked against2015

In 2016, Morristown experienced 131 crashes, marking a 23.6% increase from the 106 crashes recorded in 2015. A particularly notable shift was the 100% increase in DUI crashes, which rose from 6 in 2015 to 12 in 2016.

131

23.6%was 106

Total Crash Events

1

-50.0%was 2

Fatal Crashes

30

57.9%was 19

Injury Crashes

1

-50.0%was 2

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 · 2016-01-01 to 2016-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash incidents in Morristown showed an upward trend, increasing by 23.6% year-over-year. The total number of crashes rose from 106 in 2015 to 131 in 2016.

When Crashes Happen

The peak day for crashes remained Friday in both years, with 31 crashes in 2016 and 22 in 2015. However, the peak hour for crashes shifted from 2 PM in 2015 (10 crashes) to 12 PM in 2016 (15 crashes).

Source: Vermont Crash Data · Arcgis Open Data · 2016-01-01 to 2016-12-31 · Crash date field aggregated by weekday

Source: Vermont Crash Data · Arcgis Open Data · 2016-01-01 to 2016-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatal crashes decreased by 50% year-over-year, from 2 in 2015 to 1 in 2016, leading to a reduction in the fatal crash rate from 1.89% to 0.76%. Conversely, injury crashes increased significantly by 57.9%, rising from 19 in 2015 to 30 in 2016.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.8%
-50.0%prior 2
Injury30minor injury crashes22.9%
57.9%prior 19
No Injury100no injury crashes76.3%
17.6%prior 85

Source: Vermont Crash Data · Arcgis Open Data · 2016-01-01 to 2016-12-31 · Severity derived from reported fatal/injury indicators (no KABCO A/B/C codes)

Severity Distribution (Crash Events)

Source: Vermont Crash Data · Arcgis Open Data · 2016-01-01 to 2016-12-31 · Most severe injury per crash record

Road & Environmental Conditions

Crashes occurring in daylight increased by 31.2% from 77 in 2015 to 101 in 2016, while crashes in the dark remained relatively stable, increasing from 29 to 30. Regarding road surface conditions, crashes on wet roads increased by 100% from 10 in 2015 to 20 in 2016, and crashes on slushy roads increased by 200% from 2 to 6.

Weather

Clear72 (58.5%)
18.0%prior 61
Cloudy32 (26.0%)
28.0%prior 25
Freezing Precipitation14 (11.4%)
27.3%prior 11
Rain4 (3.3%)
-20.0%prior 5
Wind1 (0.8%)

Source: Vermont Crash Data · Arcgis Open Data · 2016-01-01 to 2016-12-31 · Weather condition at time of crash

Lighting

Daylight101 (77.1%)
31.2%prior 77
Dark30 (22.9%)
3.4%prior 29

Source: Vermont Crash Data · Arcgis Open Data · 2016-01-01 to 2016-12-31 · Lighting condition field

Road Surface

Dry88 (68.2%)
15.8%prior 76
Wet20 (15.5%)
100.0%prior 10
Snow12 (9.3%)
-20.0%prior 15
Slush6 (4.7%)
Sand, mud, dirt, oil, gravel1 (0.8%)
Other - Explain in Narrative1 (0.8%)
Ice1 (0.8%)

Source: Vermont Crash Data · Arcgis Open Data · 2016-01-01 to 2016-12-31 · 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: 2016-01-01 through 2016-12-31
  • Report generated: July 5, 2026

Data Coverage

  • Reporting period: 2016-01-01 through 2016-12-31 (366 days)
  • Geographic scope: Morristown, VT
  • Total crash records analyzed: 131

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). "Morristown, VT Crash Intelligence Report: 2016." Published July 5, 2026. Reporting period: 2016-01-01 to 2016-12-31. Data source: Vermont Crash Data, Arcgis Open Data. Available at: https://thatcarhitme.com/crash-data/vermont/morristown/2016-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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Morristown, VT Crash Report — 2016 | ThatCarHitMe.com