ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · GREENFIELD, MA · JANUARY 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.
GET: https://thatcarhitme.com/api/crash-data/reports/data/massachusetts/greenfield/january-2024-report
Monthly Traffic Safety Analysis
38 CRASHES IN
GREENFIELD, MA
JANUARY 2024
In January 2024, Greenfield experienced 38 total crashes, a decrease of 17.4% from 46 crashes in January 2023. Total injuries increased by 23.1%, rising from 13 to 16, while total fatalities remained at 0 in both periods. A notable shift was the 50% reduction in hit-and-run crashes, decreasing from 6 in the prior year to 3 in the current year.
38
▼ -17.4%was 46
Total Crash Events
0
Persons Killed
16
▲ 23.1%was 13
Persons Injured
3
▼ -50.0%was 6
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. 3 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 2024-01-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, total crashes in Greenfield decreased by 17.4%, from 46 in January 2023 to 38 in January 2024. Despite this reduction in crash count, total injuries increased by 23.1%, rising from 13 to 16. Fatalities remained stable at zero in both periods.
3
Hit-and-Run Crashes — January 2024
▼ -50.0% vs prior (6)
Hit-and-run crashes decreased by 50%, from 6 incidents in January 2023 to 3 in January 2024. Correspondingly, the hit-and-run rate decreased from 13% in the prior period to 7.9% in the current period.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
1
Pedestrians Injured
1
Cyclists Injured
14
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-01-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal distribution of crashes shifted year-over-year. In January 2023, the peak day for crashes was Monday with 14 incidents, whereas in January 2024, Wednesday became the peak day with 9 crashes. The peak crash hour also changed from 5 PM with 9 crashes in the prior period to 8 AM with 6 crashes in the current period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-01-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-01-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Total fatalities remained at 0 in both January 2023 and January 2024. However, total injuries increased by 23.1%, from 13 in the prior period to 16 in the current period. The proportion of crashes resulting in serious injury (code A) slightly increased from 2.2% to 2.6%, while minor injuries (code B) saw a count decrease from 9 to 8, and possible injuries (code C) increased from 2 to 3.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-01-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-01-31 · Most severe injury per crash record
Top Contributing Factors
The most frequent contributing factor in January 2024 was 'No improper driving' with 11 crashes, remaining stable from the prior year. 'Inattention' crashes decreased by 38.5%, from 13 in January 2023 to 8 in January 2024. Crashes attributed to 'Failed to yield right of way' saw a significant reduction, decreasing from 6 incidents in the prior period to 1 in the current period.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-01-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The prevalence of 'Clear' weather conditions at the time of crashes decreased from 24 incidents in January 2023 to 20 in January 2024, while 'Snow' conditions saw a slight increase from 5 to 6 incidents. 'Dry' road surface conditions decreased from 27 crashes to 21 crashes year-over-year. 'Daylight' lighting conditions remained the most common, decreasing slightly from 25 crashes to 24 crashes.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-01-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-01-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-01-31 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes decreased from 79 in January 2023 to 63 in January 2024. The top vehicle make involved shifted, with Toyota moving from the most frequent in the prior period (13 vehicles) to 4th in the current period (7 vehicles), while Honda became the most frequent with 10 vehicles. For persons involved, the 35-44 age group saw a decrease from 23 to 18 individuals, while the 65+ age group increased from 13 to 19 individuals.
Top Vehicle Makes (63 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-01-31 · Vehicle unit records
6 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (72 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-01-31 · Person-level records linked to crash events
Speed Limit Zones
No fatalities were recorded in any speed zone for either January 2023 or January 2024. Crashes occurring in 25 mph zones saw a slight increase from 18 to 19 incidents, while 30 mph zones remained stable with 11 crashes in both periods. Crashes in 35 mph zones decreased from 8 in the prior period to 3 in the current period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-01-31 · 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: 2024-01-01 through 2024-01-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2024-01-01 through 2024-01-31 (31 days)
- Geographic scope: GREENFIELD, MA
- Total crash records analyzed: 38
- Total persons involved: 78
- Total vehicles involved: 63
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: January 2024." Published June 21, 2026. Reporting period: 2024-01-01 to 2024-01-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/greenfield/january-2024-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 – 2024-01-31
Generated: June 21, 2026 · All rights reserved