ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · SHELBURNE, MA · 2022
Purpose: Machine-readable JSON endpoint for AI agents, LLMs, researchers, and programmatic consumers. Returns all underlying crash data and AI-generated commentary without HTML.
Authentication: None required. Public endpoint.
GET: https://thatcarhitme.com/api/crash-data/reports/data/massachusetts/shelburne/2022-annual-report
Yearly Traffic Safety Analysis
48 CRASHES IN
SHELBURNE, MA
2022
In Shelburne, total traffic crashes decreased from 62 in 2021 to 48 in 2022, a 22.6% reduction. The most significant year-over-year change was the elimination of traffic fatalities, which dropped from two in the prior period to zero in the current period. Overall injuries also saw a substantial decline, falling from 21 to 10.
48
▼ -22.6%was 62
Total Crash Events
0
▼ -100.0%was 2
Persons Killed
10
▼ -52.4%was 21
Persons Injured
0
▼ -100.0%was 1
Hit-and-Run Crashes
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.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend in traffic incidents in Shelburne shows a significant year-over-year improvement. Total crashes fell by 22.6%, from 62 incidents in 2021 to 48 in 2022. This positive trend was even more pronounced in crash outcomes, with total injuries decreasing by 52.4% and fatalities dropping from two to zero.
Vulnerable Road User Casualties
0
Motorists Killed
10
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
Crash timing patterns shifted between the two periods. While Friday remained the peak day for crashes in both 2021 (12 crashes) and 2022 (14 crashes), the peak hour for incidents moved from 2 p.m. in the prior year (8 crashes) to the 6 p.m. hour in the current year (5 crashes). Crashes on Mondays increased slightly from 7 to 8, while incidents on Tuesdays and Saturdays both decreased from 10 to 4.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Crash severity improved significantly year-over-year. Fatal crashes were eliminated, dropping from 2 incidents (a 3.2% share of all crashes) in 2021 to zero in 2022. The proportion of crashes resulting in any injury also decreased, with serious and minor injury crashes combined accounting for 16.7% of incidents in 2022, down from a 20.9% share in 2021. Consequently, the share of non-injury crashes increased from 74.2% to 81.3%.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factor cited in both periods was 'No improper driving,' with its count increasing from 18 in 2021 to 20 in 2022. However, the ranking of improper driving factors shifted; crashes attributed to 'Inattention' saw a sharp decline in count from 14 to 4. Conversely, incidents involving an 'Operating vehicle in erratic, reckless, careless, negligent or aggressive manner' increased in count from 2 to 6, becoming the second-most cited improper action in 2022.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
While total crashes decreased, the proportion of incidents occurring in adverse conditions increased. Crashes on non-dry road surfaces (snow, wet, or ice) rose from 12 incidents (19.4% share of total) in 2021 to 15 incidents (31.3% share) in 2022. Similarly, the share of crashes in darkness on unlit roadways grew from 21.0% to 29.2% year-over-year. Crashes in clear weather remained the most frequent condition, but their count dropped from 38 to 30.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Road surface condition field
Vehicles & Demographics
Toyota remained the vehicle make most frequently involved in crashes, though its count dropped from 22 in 2021 to 10 in 2022. Honda, the second-most common make in the prior year with 17 vehicles, fell to seventh place with only 4 vehicles in the current year. The demographic profile of persons involved in crashes also shifted, with the 65+ age group's representation increasing from a 9.3% share of all persons in 2021 to a 19.2% share in 2022.
Top Vehicle Makes (66 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records
3 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (75 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in the 50 mph speed zone remained the most common in both periods, but their count decreased from 30 in 2021 to 18 in 2022. This zone also accounted for both of the prior year's fatalities, while no fatal crashes occurred in any zone in the current year. The share of crashes in lower speed zones (30 mph or less) increased, representing 35.9% of crashes with recorded speed limits in 2022, up from a 20.7% share in 2021.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · 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: 2022-01-01 through 2022-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-01-01 through 2022-12-31 (365 days)
- Geographic scope: SHELBURNE, MA
- Total crash records analyzed: 48
- Total persons involved: 78
- Total vehicles involved: 66
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: 2022." Published June 21, 2026. Reporting period: 2022-01-01 to 2022-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/shelburne/2022-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
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly
Period: 2022-01-01 – 2022-12-31
Generated: June 21, 2026 · All rights reserved