Yearly Traffic Safety Analysis

13 CRASHES IN
ROCHESTER, VT
2019

All metrics benchmarked against2018

Total crashes in Rochester, VT increased by 116.67% from 6 crashes in 2018 to 13 crashes in 2019. This significant increase in overall crash volume is the most notable year-over-year shift. Despite the rise in crashes, fatalities remained at zero in both years, and injuries decreased from 3 in 2018 to 0 in 2019.

13

116.7%was 6

Total Crash Events

0

Fatal Crashes

0

-100.0%was 3

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. 13 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

Overall, Rochester experienced a significant upward trend in crash incidents, with total crashes more than doubling from 6 in 2018 to 13 in 2019, a 116.67% increase. While total crashes rose, the number of injuries decreased from 3 in 2018 to 0 in 2019, and fatalities remained at 0 in both periods.

When Crashes Happen

The temporal patterns of crashes shifted between 2018 and 2019. In 2018, Saturday was the peak day with 3 crashes, while in 2019, Sunday, Tuesday, and Thursday all recorded 3 crashes, making them the peak days. The peak crash hour also changed, moving from 8 PM with 1 crash in 2018 to 6 PM with 2 crashes 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)

Road & Environmental Conditions

Regarding crash conditions, crashes occurring in daylight increased from 3 in 2018 to 8 in 2019, and those in dark conditions rose from 2 to 5. Crashes during clear weather remained at 3 in both years, while those in freezing precipitation decreased from 2 in 2018 to 1 in 2019. Crashes on dry road surfaces remained consistent at 2 in both periods, with 2 crashes on wet road surfaces reported in 2019 but none in 2018.

Weather

Clear3 (75.0%)
Freezing Precipitation1 (25.0%)

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

Lighting

Daylight8 (61.5%)
Dark5 (38.5%)

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

Road Surface

Dry2 (50.0%)
Wet2 (50.0%)

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: Rochester, VT
  • Total crash records analyzed: 13

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). "Rochester, 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/rochester/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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Rochester, VT Crash Report — 2019 | ThatCarHitMe.com