Yearly Traffic Safety Analysis

9 CRASHES IN
ELMORE, VT
2019

All metrics benchmarked against2018

In 2019, Elmore recorded 9 total crashes, a 28.6% increase from the 7 crashes reported in 2018. Despite the rise in total collisions, the most significant change was a reduction in crash severity, with fatal crashes decreasing from one in the prior year to zero in the current year.

▲ 28.6%was 7

Total Crash Events

▼ -100.0%was 1

Fatal Crashes

▼ -50.0%was 2

Injury Crashes

▼ -100.0%was 1

Fatal Crash Events

Note: "Fatal Crashes" and "Injury Crashes" count crash events — this source publishes crash-level counts only, not individual persons. 4 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

Year-over-year data indicates an upward trend in the total number of crashes in Elmore, which rose by 28.6% from 7 in 2018 to 9 in 2019. However, the outcomes of these crashes became less severe, as total injuries fell from 2 to 1 and fatalities dropped from one to zero.

When Crashes Happen

The timing of crashes in Elmore shifted between 2018 and 2019. The peak day for collisions moved from Monday and Thursday (2 crashes each) in the prior year to Tuesday (3 crashes) in the current year. Similarly, the peak hour for crashes changed from the 8 AM and 6 PM hours in 2018 to the midday hour of 12 PM 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)

Crash Severity Breakdown

Crash severity decreased significantly from 2018 to 2019. In the prior year, one fatal crash accounted for 14.3% of all collisions, but in 2019, there were no fatal crashes. The proportion of crashes resulting in an injury also declined, dropping from 28.6% (2 crashes) in 2018 to 11.1% (1 crash) in 2019.

Outcome by Severity (Crash Events)

Injury1minor injury crashes11.1%

↓-50.0%prior 2

No Injury4no injury crashes44.4%

→0.0%prior 4

Source: Vermont Crash Data · Arcgis Open Data · 2019-01-01 to 2019-12-31 · Severity derived from reported fatal/injury indicators (no KABCO A/B/C codes)

Severity Distribution (Crash Events)

Source: Vermont Crash Data · Arcgis Open Data · 2019-01-01 to 2019-12-31 · Most severe injury per crash record

Road & Environmental Conditions

The conditions under which crashes occurred saw a notable shift year-over-year. The proportion of collisions happening in dark lighting conditions increased substantially, from 14.3% of crashes in 2018 to 44.4% in 2019. Regarding road surface, crashes on snowy roads were reported in 2019 (2 incidents), whereas the prior year saw crashes on icy or wet surfaces but none specifically on snow.

Lighting

Daylight5 (55.6%)

↓-16.7%prior 6

Dark4 (44.4%)

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

Road Surface

Dry3 (60.0%)

Snow2 (40.0%)

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: Elmore, VT
Total crash records analyzed: 9

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). "Elmore, 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/elmore/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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9 CRASHES INELMORE, VT2019