ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · GREENFIELD, MA · MAY 2023
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/may-2023-report
Monthly Traffic Safety Analysis
47 CRASHES IN
GREENFIELD, MA
MAY 2023
In May 2023, GREENFIELD, MA experienced 47 crashes, a notable increase from the 34 crashes reported in May 2022. This represents a 38.24% increase in total crashes year-over-year. The most significant shift was the occurrence of 1 fatality in May 2023, compared to 0 fatalities in May 2022.
47
▲ 38.2%was 34
Total Crash Events
1
Persons Killed
11
▲ 37.5%was 8
Persons Injured
4
▲ 100.0%was 2
Hit-and-Run Crashes
Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 2 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend indicates a significant increase in crash activity in GREENFIELD, MA, with total crashes rising from 34 in May 2022 to 47 in May 2023. This marks a 38.24% increase in the number of crashes year-over-year. Fatalities also increased from 0 to 1 during this period.
4
Hit-and-Run Crashes — May 2023
▲ 100.0% vs prior (2)
Hit-and-run crashes increased year-over-year, with 4 incidents reported in May 2023 compared to 2 in May 2022. The hit-and-run rate also rose from 5.9% of total crashes in May 2022 to 8.5% in May 2023, indicating an upward trend in such incidents.
Vulnerable Road User Casualties
0
Pedestrians Killed
1
Motorists Killed
1
Pedestrians Injured
10
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
Temporal patterns show a shift in peak crash times and days. In May 2023, the peak day for crashes was Tuesday with 12 incidents, while in May 2022, Monday had the highest count with 7 crashes. The peak hour for crashes also shifted from 1 PM with 6 crashes in May 2022 to 5 PM with 7 crashes in May 2023.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity distribution changed significantly year-over-year, with 1 fatal crash and 1 fatality recorded in May 2023, compared to 0 fatal crashes and 0 fatalities in May 2022. Total injuries increased from 8 in May 2022 to 11 in May 2023, with serious injuries appearing in May 2023 (1) where none were reported in May 2022.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Most severe injury per crash record
Top Contributing Factors
Comparing contributing factors, 'Inattention' crashes increased from 10 to 12 (a 20% increase in count), and 'No improper driving' crashes rose from 5 to 11 (a 120% increase in count). 'Disregarded traffic signs, signals, road markings' crashes saw a substantial increase from 1 to 4 (a 300% increase in count). Conversely, crashes attributed to 'Followed too closely' decreased from 4 to 2 (a 50% decrease in count), and 'Failed to yield right of way' decreased from 3 to 2 (a 33.33% decrease in count).
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Regarding crash conditions, crashes occurring in 'Clear' weather increased from 24 in May 2022 to 35 in May 2023. Crashes on 'Dry' road surfaces increased from 30 to 40, while 'Wet' road surface crashes increased slightly from 4 to 5. The proportion of crashes occurring in 'Daylight' conditions remained dominant, increasing from 27 to 39 incidents year-over-year.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes increased from 59 in May 2022 to 76 in May 2023, representing a 28.8% increase. Among top vehicle makes, TOYOTA increased from 10 to 13, FORD increased from 5 to 9, and NISSAN saw a significant rise from 3 to 9. HONDA vehicles involved decreased slightly from 9 to 8, and CHEVROLET decreased from 7 to 4.
Top Vehicle Makes (76 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Vehicle unit records
6 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (80 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 25 mph speed zones increased from 14 in May 2022 to 19 in May 2023, and this zone accounted for the single fatal crash in May 2023, whereas no fatal crashes occurred in any speed zone in May 2022. Crashes in 30 mph zones doubled from 5 to 10, and 40 mph zones saw a 200% increase from 1 to 3 crashes. Conversely, 35 mph zones experienced a decrease from 5 to 3 crashes.
Fatal crashes by zone: 25 mph: 1 of 19 (5.263%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · 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: 2023-05-01 through 2023-05-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-05-01 through 2023-05-31 (31 days)
- Geographic scope: GREENFIELD, MA
- Total crash records analyzed: 47
- Total persons involved: 87
- Total vehicles involved: 76
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: May 2023." Published June 21, 2026. Reporting period: 2023-05-01 to 2023-05-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/greenfield/may-2023-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: 2023-05-01 – 2023-05-31
Generated: June 21, 2026 · All rights reserved