Monthly Traffic Safety Analysis

2 CRASHES IN
SHELBURNE, MA
JANUARY 2023

All metrics benchmarked againstJanuary 2022

In January 2023, SHELBURNE experienced 2 crashes, a significant decrease compared to the 6 crashes recorded in January 2022. This represents a 66.67% reduction in total crashes year-over-year. The most notable shift was the complete absence of injuries in January 2023, down from 3 injuries in the prior year.

2

-66.7%was 6

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 · 2023-01-01 to 2023-01-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates a substantial decrease in crash activity in SHELBURNE, with total crashes falling by 66.67% from 6 in January 2022 to 2 in January 2023. This reduction also extended to injuries, which decreased by 100%, from 3 in the prior year to 0 in the current period.

When Crashes Happen

The temporal patterns of crashes shifted year-over-year. In January 2023, the peak day for crashes was Tuesday with 1 crash, whereas in January 2022, Wednesday was the peak day with 2 crashes. The peak hour also shifted from 9 PM in January 2022 (1 crash) to 7 PM in January 2023 (1 crash).

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Crash date field aggregated by weekday

Road & Environmental Conditions

Weather conditions for crashes in January 2023 were exclusively Cloudy (1 crash) and Snow (1 crash), while January 2022 saw a more varied distribution including Clear (3 crashes), Cloudy/Other (1 crash), Rain/Snow (1 crash), and Snow (1 crash). Regarding lighting, crashes in January 2023 occurred under Dark - lighted roadway (1 crash) and Dark - roadway not lighted (1 crash) conditions, differing from January 2022 where Daylight (3 crashes) was the most frequent condition. Road surface conditions in January 2023 involved Dry (1 crash) and Snow (1 crash), a change from January 2022 where Ice (3 crashes) was the most common condition, followed by Dry (2 crashes) and Snow (1 crash).

Weather

Cloudy1 (50.0%)
Snow1 (50.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Weather condition at time of crash

Lighting

Dark - lighted roadway1 (50.0%)
Dark - roadway not lighted1 (50.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Lighting condition field

Road Surface

Dry1 (50.0%)
Snow1 (50.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Road surface condition field

Vehicles & Demographics

Sex Distribution (4 persons with recorded sex)

Male3 (75.0%)
-50.0%prior 6
Female1 (25.0%)
0.0%prior 1

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Person-level records linked to crash events

Speed Limit Zones

The distribution of crashes across speed zones changed between the two periods. In January 2023, the 2 crashes occurred in 45 mph (1 crash) and 50 mph (1 crash) zones. In contrast, January 2022 crashes were distributed across 25 mph (1 crash), 30 mph (2 crashes), 40 mph (1 crash), and 50 mph (1 crash) zones, indicating a shift away from lower speed limits in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · 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: 2023-01-01 through 2023-01-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-01-01 through 2023-01-31 (31 days)
  • Geographic scope: SHELBURNE, MA
  • Total crash records analyzed: 2
  • Total persons involved: 4
  • Total vehicles involved: 2

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: January 2023." Published June 21, 2026. Reporting period: 2023-01-01 to 2023-01-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/shelburne/january-2023-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 — January 2023 | ThatCarHitMe.com