Yearly Traffic Safety Analysis

38 CRASHES IN
WILLIAMSTOWN, VT
2018

All metrics benchmarked against2017

In 2018, Williamstown experienced 38 total crashes, a substantial decrease compared to the 111 crashes recorded in 2017. This represents a 65.8% reduction in total crashes year-over-year. A notable shift is the introduction of 1 fatality in 2018, whereas 2017 reported no fatalities.

38

-65.8%was 111

Total Crash Events

1

Fatal Crashes

14

16.7%was 12

Injury Crashes

1

Fatal Crash Events

Note: "Fatal Crashes" and "Injury Crashes" count crash events — this source publishes crash-level counts only, not individual persons. 5 crashes with unreported severity are not shown in the severity breakdown.

Source: Vermont Crash Data · Arcgis Open Data · 2018-01-01 to 2018-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash trends in Williamstown show a significant decline from 2017 to 2018. The total number of crashes decreased from 111 in 2017 to 38 in 2018. This represents a substantial 65.8% reduction in crashes year-over-year.

When Crashes Happen

The temporal patterns of crashes shifted between 2017 and 2018. In 2017, the peak day for crashes was Saturday with 18 incidents, while in 2018, both Monday and Sunday recorded the highest number of crashes with 10 each. The peak crash hour also changed, moving from 4 PM with 14 crashes in 2017 to 3 PM with 5 crashes in 2018.

Source: Vermont Crash Data · Arcgis Open Data · 2018-01-01 to 2018-12-31 · Crash date field aggregated by weekday

Source: Vermont Crash Data · Arcgis Open Data · 2018-01-01 to 2018-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity distributions saw a significant change year-over-year. In 2018, there was 1 fatal crash, resulting in 1 fatality, which was not present in 2017. Injury crashes accounted for 36.8% of all crashes in 2018 (14 incidents), a notable increase in proportion compared to 10.8% of crashes (12 incidents) in 2017, despite a lower total number of crashes.

Outcome by Severity (Crash Events)

Fatal1fatal crashes2.6%
Injury14minor injury crashes36.8%
16.7%prior 12
No Injury18no injury crashes47.4%
-25.0%prior 24

Source: Vermont Crash Data · Arcgis Open Data · 2018-01-01 to 2018-12-31 · Severity derived from reported fatal/injury indicators (no KABCO A/B/C codes)

Severity Distribution (Crash Events)

Source: Vermont Crash Data · Arcgis Open Data · 2018-01-01 to 2018-12-31 · Most severe injury per crash record

Road & Environmental Conditions

The conditions under which crashes occurred shifted between 2017 and 2018. While 'Clear' weather remained the most frequent condition, crashes in 'Freezing Precipitation' increased from 9 in 2017 to 11 in 2018, despite the overall crash reduction. Crashes occurring in 'Dark' conditions decreased from 44 in 2017 to 12 in 2018, aligning with the overall decrease in total crashes.

Weather

Clear14 (43.8%)
-12.5%prior 16
Freezing Precipitation11 (34.4%)
22.2%prior 9
Cloudy5 (15.6%)
-28.6%prior 7
Rain2 (6.3%)

Source: Vermont Crash Data · Arcgis Open Data · 2018-01-01 to 2018-12-31 · Weather condition at time of crash

Lighting

Daylight26 (68.4%)
-61.2%prior 67
Dark12 (31.6%)
-72.7%prior 44

Source: Vermont Crash Data · Arcgis Open Data · 2018-01-01 to 2018-12-31 · Lighting condition field

Road Surface

Dry13 (39.4%)
-7.1%prior 14
Snow9 (27.3%)
-30.8%prior 13
Wet4 (12.1%)
Ice3 (9.1%)
Sand, mud, dirt, oil, gravel2 (6.1%)
Slush1 (3.0%)
Other - Explain in Narrative1 (3.0%)

Source: Vermont Crash Data · Arcgis Open Data · 2018-01-01 to 2018-12-31 · 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: 2018-01-01 through 2018-12-31
  • Report generated: July 5, 2026

Data Coverage

  • Reporting period: 2018-01-01 through 2018-12-31 (365 days)
  • Geographic scope: Williamstown, VT
  • Total crash records analyzed: 38

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). "Williamstown, VT Crash Intelligence Report: 2018." Published July 5, 2026. Reporting period: 2018-01-01 to 2018-12-31. Data source: Vermont Crash Data, Arcgis Open Data. Available at: https://thatcarhitme.com/crash-data/vermont/williamstown/2018-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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