ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · GREENFIELD, 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.
GET: https://thatcarhitme.com/api/crash-data/reports/data/massachusetts/greenfield/2024-annual-report
Yearly Traffic Safety Analysis
438 CRASHES IN
GREENFIELD, MA
2024
In 2024, Greenfield recorded 438 total traffic crashes, a 5.8% decrease from the 465 crashes documented in 2023. While overall collisions declined, the number of reported injuries rose by 14.7% from 129 to 148. The most significant year-over-year change was the elimination of crash-related fatalities, which dropped from two in the prior period to zero in the current period.
438
▼ -5.8%was 465
Total Crash Events
0
▼ -100.0%was 2
Persons Killed
148
▲ 14.7%was 129
Persons Injured
49
▲ 28.9%was 38
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. 29 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
Overall traffic crashes in Greenfield showed a downward trend, decreasing by 5.8% from 465 in 2023 to 438 in 2024. Despite the drop in total collisions, the number of people injured increased by 14.7%, rising from 129 to 148. Notably, there were no fatalities in 2024, compared to two fatalities in the previous year.
49
Hit-and-Run Crashes — 2024
▲ 28.9% vs prior (38)
Hit-and-run incidents increased in both count and as a proportion of total crashes. The number of hit-and-run crashes rose from 38 in 2023 to 49 in 2024, a 28.9% increase in count. The hit-and-run rate also climbed, from 8.2% of all crashes in the prior period to 11.2% in the current period.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
0
Other Killed
7
Pedestrians Injured
10
Cyclists Injured
130
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 patterns of crashes shifted between the two periods. The peak day for crashes moved from Monday (89 crashes) in 2023 to Friday (88 crashes) in 2024. Similarly, the peak hour for collisions shifted from the 3 PM hour in the prior period (48 crashes) to the 12 PM hour in the current period (40 crashes).
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 profiles changed notably year-over-year. In 2024, there were zero fatal crashes, a decrease from two fatal crashes in 2023. However, the proportion of crashes resulting in injury increased, with serious injury crashes rising from 8 to 13 and minor injury crashes increasing from 64 to 71. Consequently, the share of no-injury crashes decreased from 75.1% of all collisions in 2023 to 68% in 2024.
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
Inattention remained the top contributing factor in both periods, though its count decreased from 129 crashes in 2023 to 112 in 2024. 'No improper driving' was the second most common factor, also seeing a decrease in count from 97 to 86. Notably, crashes attributed to an 'Operating vehicle in erratic, reckless, careless, negligent or aggressive manner' increased by 50% in count, from 18 to 27. Conversely, crashes involving 'Distracted' driving saw a significant count reduction of 55.6%, falling from 18 to 8.
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 distribution of crashes across different conditions remained broadly consistent year-over-year. Crashes in daylight accounted for approximately 73% of the total in both 2023 and 2024. Similarly, over 81% of crashes in both periods occurred on dry road surfaces. There was an increase in crashes on unlit dark roadways, rising from 33 to 41, while crashes on lit dark roadways decreased from 66 to 52.
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
The top five vehicle makes involved in crashes remained consistent, with Toyota, Honda, Ford, Chevrolet, and Subaru leading in both years, though the total count for most of these makes decreased. The age demographics of persons involved in crashes saw a slight shift. The proportion of individuals aged 65 and older decreased from 17.6% of all persons in 2023 to 16.0% in 2024. Conversely, the representation of the 35-44 age group increased from 14.1% to 15.8%.
Top Vehicle Makes (744 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records
111 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (793 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
Crashes in 25 mph zones remained the most frequent, accounting for over 43% of all collisions in both years, though the count dropped from 202 to 192. The two fatalities recorded in 2023 both occurred in a 25 mph zone; in 2024, there were no fatalities in any speed zone. Collisions in 30 mph and 65 mph zones saw decreases in both their total counts and their share of overall crashes.
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: GREENFIELD, MA
- Total crash records analyzed: 438
- Total persons involved: 913
- Total vehicles involved: 744
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: 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/greenfield/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