Yearly Traffic Safety Analysis

56 CRASHES IN
RANDOLPH, VT
2016

All metrics benchmarked against2015

In 2016, Randolph experienced 56 crashes, a decrease from the 74 crashes recorded in 2015, representing a 24.3% reduction. A notable shift was the increase in DUI crashes from 0 in 2015 to 3 in 2016.

56

▼ -24.3%was 74

Total Crash Events

0

Fatal Crashes

18

▲ 63.6%was 11

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

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, total crashes in Randolph decreased by 24.3%, from 74 crashes in 2015 to 56 crashes in 2016. Despite this reduction in total crashes, the number of injuries increased by 63.6%, rising from 11 injuries in 2015 to 18 injuries in 2016.

When Crashes Happen

The peak day for crashes shifted from Saturday and Wednesday in 2015, each with 12 crashes, to Friday in 2016, which recorded 11 crashes. Similarly, the peak hour for crashes moved from 10a with 7 crashes in 2015 to 3p with 9 crashes in 2016. This indicates a shift in the timing of crash occurrences year-over-year.

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

There were no fatalities reported in either 2015 or 2016. However, the proportion of crashes resulting in injury increased significantly, rising from 14.9% of all crashes in 2015 to 32.1% in 2016. This represents an increase in injury crashes from 11 in 2015 to 18 in 2016, despite an overall decrease in total crashes.

Outcome by Severity (Crash Events)

Injury18minor injury crashes32.1%
↑63.6%prior 11
No Injury22no injury crashes39.3%
↓-8.3%prior 24

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

The number of crashes occurring in clear weather remained consistent at 19 for both years, while crashes during cloudy conditions increased from 2 in 2015 to 8 in 2016. Crashes on dry road surfaces increased from 16 in 2015 to 19 in 2016, and crashes on wet surfaces more than doubled from 3 to 7. Conversely, crashes during snowy conditions decreased from 7 to 4, and those during freezing precipitation decreased from 9 to 5.

Weather

Clear19 (51.4%)
→0.0%prior 19
Cloudy8 (21.6%)
Freezing Precipitation5 (13.5%)
↓-44.4%prior 9
Rain4 (10.8%)
Wind1 (2.7%)

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

Lighting

Daylight44 (78.6%)
↓-24.1%prior 58
Dark12 (21.4%)
↓-25.0%prior 16

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

Road Surface

Dry19 (50.0%)
↑18.8%prior 16
Wet7 (18.4%)
Ice5 (13.2%)
Snow4 (10.5%)
↓-42.9%prior 7
Sand, mud, dirt, oil, gravel2 (5.3%)
Other - Explain in Narrative1 (2.6%)

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: Randolph, VT
  • Total crash records analyzed: 56

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). "Randolph, 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/randolph/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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