ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · GREAT BARRINGTON, MA · 2024
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.
Yearly Traffic Safety Analysis
200 CRASHES IN
GREAT BARRINGTON, MA
2024
In 2024, Great Barrington recorded 200 total crashes, an 8.1% increase from the 185 crashes reported in 2023. The most significant year-over-year change was the number of traffic fatalities, which rose from zero in the prior period to three in the current period. While total crashes and fatalities increased, the number of persons injured in collisions decreased from 40 to 36.
200
▲ 8.1%was 185
Total Crash Events
3
Persons Killed
36
▼ -10.0%was 40
Persons Injured
11
▲ 37.5%was 8
Hit-and-Run Crashes
Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 14 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Crash trends in Great Barrington show an increase year-over-year, with total collisions rising from 185 in 2023 to 200 in 2024. This represents an 8.1% increase in total incidents. While the number of persons injured decreased by 10%, the number of fatalities rose from zero to three.
11
Hit-and-Run Crashes — 2024
▲ 37.5% vs prior (8)
Hit-and-run incidents increased in both absolute numbers and as a percentage of total crashes. The count of hit-and-run crashes rose from 8 in 2023 to 11 in 2024. Consequently, the hit-and-run rate trended upward from 4.3% of all crashes in the prior period to 5.5% in the current period.
Vulnerable Road User Casualties
1
Pedestrians Killed
0
Cyclists Killed
2
Motorists Killed
0
Other Killed
1
Pedestrians Injured
2
Cyclists Injured
32
Motorists Injured
1
Other Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal pattern of crashes shifted between the two periods. The peak day for collisions moved from Saturday (33 crashes) in 2023 to Wednesday (48 crashes) in 2024. The 3 PM hour remained the peak time for crashes in both years, though the number of incidents during that hour increased from 24 to 28.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Crash severity increased in 2024 with the introduction of three fatal crashes, which accounted for 1.5% of all incidents, compared to zero fatal crashes in 2023. The proportion of crashes resulting in any type of injury remained stable, accounting for 14.5% of collisions in 2024 versus 14.6% in 2023. Non-injury crashes made up 77% of events in 2024, a slight increase from 75.7% in the prior year.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factors remained consistent, with 'No improper driving' being the most common finding in both 2024 (97 crashes) and 2023 (87 crashes). 'Inattention' was the second-ranked factor in both periods, but its count decreased by 18.8% from 32 crashes in 2023 to 26 in 2024. Crashes attributed to 'Followed too closely' and 'Failed to yield right of way' both saw an increase in count from 5 to 7 incidents year-over-year.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The majority of crashes in both periods occurred during daylight on dry road surfaces. However, collisions in 'Dark - roadway not lighted' conditions doubled, increasing from 14 incidents in 2023 to 28 in 2024. Crashes reported during snowy weather also increased from 3 in the prior year to 8 in the current year, while crashes on wet road surfaces decreased from 30 to 25.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field
Vehicles & Demographics
Vehicle make involvement shifted, with Subaru becoming the most common make in 2024 crashes (50 vehicles), up from its second-place rank in 2023 (37 vehicles). Toyota, the top make in 2023 with 50 vehicles, was involved in 48 crashes in 2024. For persons involved, the 65+ age group was the largest in both years, while the number of individuals aged 16-20 involved in crashes increased from 20 to 29.
Top Vehicle Makes (342 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records
30 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (316 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Person-level records linked to crash events
Speed Limit Zones
Crash locations by speed limit remained concentrated in 25 mph and 35 mph zones, which together accounted for 96 crashes in 2024 and 98 in 2023. A notable change occurred in higher speed zones, where all three of 2024's fatal crashes were recorded: two in 40 mph zones and one in a 50 mph zone. There were no fatal crashes reported in any speed zone during the prior year.
Fatal crashes by zone: 40 mph: 2 of 14 (14.286%) · 50 mph: 1 of 10 (10%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · 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: 2024-01-01 through 2024-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2024-01-01 through 2024-12-31 (366 days)
- Geographic scope: GREAT BARRINGTON, MA
- Total crash records analyzed: 200
- Total persons involved: 356
- Total vehicles involved: 342
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). "GREAT BARRINGTON, MA Crash Intelligence Report: 2024." Published June 21, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/great-barrington/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
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly
Period: 2024-01-01 – 2024-12-31
Generated: June 21, 2026 · All rights reserved