ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · GREENFIELD, 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/greenfield/2022-annual-report
Yearly Traffic Safety Analysis
410 CRASHES IN
GREENFIELD, MA
2022
In 2022, Greenfield recorded 410 total vehicle crashes, an 8.8% increase from the 377 crashes reported in 2021. While the number of fatalities remained stable at two, the most significant year-over-year change was a sharp rise in hit-and-run incidents, which increased from 9 in 2021 to 30 in 2022.
410
▲ 8.8%was 377
Total Crash Events
2
Persons Killed
109
▲ 10.1%was 99
Persons Injured
30
▲ 233.3%was 9
Hit-and-Run Crashes
Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 27 crashes with unreported severity are not shown in the severity breakdown.
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
Crash trends in Greenfield show an upward movement year-over-year. Total crashes increased by 8.8%, from 377 in 2021 to 410 in 2022. Similarly, the number of people injured rose by 10.1% from 99 to 109, while the number of fatalities held steady at two for both years.
30
Hit-and-Run Crashes — 2022
▲ 233.3% vs prior (9)
Hit-and-run crashes increased significantly in 2022 compared to the prior year. The number of hit-and-run incidents rose from 9 in 2021 to 30 in 2022. This represents a more than threefold increase in the hit-and-run rate, which jumped from 2.4% of all crashes in 2021 to 7.3% in 2022.
Vulnerable Road User Casualties
1
Pedestrians Killed
0
Cyclists Killed
1
Motorists Killed
4
Pedestrians Injured
5
Cyclists Injured
100
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
The timing of crashes showed some consistency and some change between the two periods. The peak hour for collisions remained 3 PM in both 2021 and 2022, though the number of crashes in that hour increased from 35 to 44. While Friday was the clear peak day in 2021 with 79 incidents, 2022 saw both Thursday and Friday as peak days, each with 69 crashes.
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
While the number of fatal crashes was unchanged at two in both 2021 and 2022, the crash severity distribution shifted. The rate of fatal crashes decreased slightly from 0.53% to 0.49% of all crashes. However, crashes resulting in serious injuries saw a notable increase, rising from 3 incidents (0.8% of total) in 2021 to 13 incidents (3.2% of total) in 2022.
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 primary contributing factors to crashes shifted between 2021 and 2022. 'Inattention' became the leading factor in 2022 with 107 crashes, a 30.5% increase in count from 82 crashes in 2021. Conversely, crashes where 'No improper driving' was cited decreased by 22.5% in count, from 89 to 69. Other notable increases include crashes attributed to 'Followed too closely,' which rose from 16 to 30, and 'Failed to yield right of way,' which increased from 13 to 23.
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
Crashes in both years predominantly occurred in clear weather and daylight on dry roads. In 2022, 74.1% of crashes happened during daylight, up from 68.7% in 2021. The proportion of crashes on dry road surfaces remained stable, accounting for 79.0% in 2022 compared to 80.6% in 2021. There was a slight increase in crashes occurring on snow or ice, which accounted for 7.3% of incidents in 2022 versus 5.0% in 2021.
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
The top vehicle makes involved in crashes remained consistent year-over-year, with Toyota, Honda, and Ford being the most common in both 2021 and 2022. Analysis of the age of persons involved in crashes shows a demographic shift. The number of individuals aged 65 and older involved in crashes increased from 101 in 2021 to 133 in 2022, while the number of persons in the 16-20 age group decreased from 83 to 65.
Top Vehicle Makes (713 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records
100 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (751 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
The distribution of crashes across speed zones saw increases in 25 mph zones (from 141 to 159 crashes) and 35 mph zones (from 47 to 59 crashes) between 2021 and 2022. Fatal crashes occurred in different speed zones each year. In 2021, fatalities were recorded in 45 mph and 55 mph zones, whereas in 2022, they occurred in 25 mph and 50 mph zones.
Fatal crashes by zone: 25 mph: 1 of 159 (0.629%) · 50 mph: 1 of 8 (12.5%)
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: GREENFIELD, MA
- Total crash records analyzed: 410
- Total persons involved: 848
- Total vehicles involved: 713
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). "GREENFIELD, 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/greenfield/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