Monthly Traffic Safety Analysis

2 CRASHES IN
SHELBURNE, MA
FEBRUARY 2026

All metrics benchmarked againstFebruary 2025

SHELBURNE experienced a 33.33% decrease in total crashes, falling from 3 in February 2025 to 2 in February 2026. This period also saw a significant improvement in safety outcomes, with total injuries dropping by 100% from 3 to 0.

2

-33.3%was 3

Total Crash Events

0

Persons Killed

0

-100.0%was 3

Persons Injured

0

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 2 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash trends in SHELBURNE showed a positive direction, with total crashes decreasing by 33.33% from 3 in February 2025 to 2 in February 2026. Notably, total injuries saw a complete elimination, falling from 3 to 0, while fatalities remained stable at 0 in both periods.

When Crashes Happen

The peak day for crashes shifted from Monday in February 2025 to Wednesday in February 2026, though both days recorded 2 crashes. Similarly, the peak crash hour moved from 2p in February 2025 to 3p in February 2026, with 1 crash occurring at the peak hour in both periods.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Crash date field aggregated by weekday

Top Contributing Factors

The contributing factor 'No improper driving' decreased by 50% in count, from 2 crashes in February 2025 to 1 crash in February 2026. 'Failure to keep in proper lane or running off road' remained consistent, accounting for 1 crash in both periods, representing 50% of crashes in February 2026 compared to 33.3% in February 2025.

Officer-Reported Primary Contributing Cause

Failure to keep in proper lane or running off road1 (50%)
No improper driving1 (50%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Snow' conditions decreased by 50%, from 2 in February 2025 to 1 in February 2026. February 2026 recorded 1 crash under 'Wet' road surface conditions and 1 under 'Cloudy/Blowing sand, snow' weather, conditions not present in the prior period's data. Conversely, February 2025 included 1 crash each under 'Ice' road conditions and 'Clear/Clear' or 'Cloudy' weather, which were not observed in February 2026.

Weather

Clear1 (50.0%)
Cloudy/Blowing sand, snow1 (50.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Weather condition at time of crash

Lighting

Dawn1 (50.0%)
Daylight1 (50.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Lighting condition field

Road Surface

Snow1 (50.0%)
Wet1 (50.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (3 vehicles)

1
TOYOTA2 (66.7%)
2
KIA1 (33.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Vehicle unit records

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Person-level records linked to crash events

Speed Limit Zones

There were no common speed limits involved in crashes across both periods. In February 2026, crashes occurred in 15 mph and 50 mph zones, each with 1 crash, while in February 2025, crashes occurred in 25 mph and 35 mph zones, also with 1 crash each. All crashes in both periods recorded 0 fatalities regardless of the speed zone.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Posted speed limit at crash location

Data Sources & Methodology

Primary Data Source

All crash data in this report is sourced from Massachusetts Crash Data (MassDOT CDV), accessed programmatically via the Arcgis_yearly 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_yearly 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: 2026-02-01 through 2026-02-28
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2026-02-01 through 2026-02-28 (28 days)
  • Geographic scope: SHELBURNE, MA
  • Total crash records analyzed: 2
  • Total persons involved: 3
  • Total vehicles involved: 3

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). "SHELBURNE, MA Crash Intelligence Report: February 2026." Published June 21, 2026. Reporting period: 2026-02-01 to 2026-02-28. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/shelburne/february-2026-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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Shelburne, MA Crash Report — February 2026 | ThatCarHitMe.com