Yearly Traffic Safety Analysis

47 CRASHES IN
CHESTER, VT
2024

All metrics benchmarked against2023

In 2024, Chester recorded 47 traffic crashes, an 80.8% increase from the 26 crashes documented in 2023. While no fatalities were reported in either year, the number of injuries rose from 6 to 17. The most significant change was the overall surge in collision volume, accompanied by a nearly threefold increase in reported injuries.

47

80.8%was 26

Total Crash Events

0

Fatal Crashes

17

183.3%was 6

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.

Source: Vermont Crash Data · Arcgis Open Data · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash data for Chester shows a significant upward trend year-over-year. Total collisions increased by 80.8%, from 26 in 2023 to 47 in 2024. This rise was mirrored in crash outcomes, with the number of people injured increasing from 6 to 17.

When Crashes Happen

Temporal crash patterns in Chester shifted between 2023 and 2024. The most frequent day for crashes moved from Thursday, with 6 incidents in 2023, to Saturday, with 12 incidents in 2024. Similarly, the peak hour for collisions shifted from 4 p.m. in the prior year to 1 p.m. in the current year, which saw 6 crashes during that hour.

Source: Vermont Crash Data · Arcgis Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday

Source: Vermont Crash Data · Arcgis Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While Chester reported zero fatal crashes in both 2023 and 2024, the severity of non-fatal collisions increased. The proportion of crashes resulting in at least one injury grew from 23.1% in 2023 to 36.2% in 2024. Correspondingly, the share of crashes with no reported injuries decreased from 76.9% to 63.8% over the same period.

Outcome by Severity (Crash Events)

Injury17minor injury crashes36.2%
183.3%prior 6
No Injury30no injury crashes63.8%
50.0%prior 20

Source: Vermont Crash Data · Arcgis Open Data · 2024-01-01 to 2024-12-31 · Severity derived from reported fatal/injury indicators (no KABCO A/B/C codes)

Severity Distribution (Crash Events)

Source: Vermont Crash Data · Arcgis Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record

Road & Environmental Conditions

The majority of crashes in both periods occurred in clear weather and on dry roads. In 2024, 59.6% of crashes happened in clear weather and 63.8% were on dry surfaces, proportions similar to 2023's figures of 57.7% and 65.4%, respectively. Crashes during daylight hours accounted for 78.7% of incidents in 2024, a slight proportional increase from 73.1% in the prior year, indicating that environmental conditions were not a primary driver of the overall crash increase.

Weather

Clear28 (65.1%)
86.7%prior 15
Freezing Precipitation6 (14.0%)
Cloudy5 (11.6%)
Rain4 (9.3%)

Source: Vermont Crash Data · Arcgis Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash

Lighting

Daylight37 (78.7%)
94.7%prior 19
Dark10 (21.3%)
42.9%prior 7

Source: Vermont Crash Data · Arcgis Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field

Road Surface

Dry30 (71.4%)
76.5%prior 17
Snow6 (14.3%)
Sand, mud, dirt, oil, gravel2 (4.8%)
Wet2 (4.8%)
Slush1 (2.4%)
Water (standing / moving)1 (2.4%)

Source: Vermont Crash Data · Arcgis Open Data · 2024-01-01 to 2024-12-31 · 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: 2024-01-01 through 2024-12-31
  • Report generated: July 5, 2026

Data Coverage

  • Reporting period: 2024-01-01 through 2024-12-31 (366 days)
  • Geographic scope: Chester, VT
  • Total crash records analyzed: 47

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). "Chester, VT Crash Intelligence Report: 2024." Published July 5, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Vermont Crash Data, Arcgis Open Data. Available at: https://thatcarhitme.com/crash-data/vermont/chester/2024-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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Chester, VT Crash Report — 2024 | ThatCarHitMe.com