ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · PEABODY, MA · FEBRUARY 2026
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/peabody/february-2026-report
Monthly Traffic Safety Analysis
103 CRASHES IN
PEABODY, MA
FEBRUARY 2026
Total crashes in Peabody increased by 6.19% year-over-year, from 97 in February 2025 to 103 in February 2026. The most notable shift was a 75% increase in crashes involving DUI, rising from 4 to 7 incidents. Despite the increase in total crashes, the total number of injuries remained stable at 24 in both periods.
103
▲ 6.2%was 97
Total Crash Events
0
Persons Killed
24
Persons Injured
4
▼ -42.9%was 7
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 · 2026-02-01 to 2026-02-28 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, crash incidents in Peabody show a slight upward trend, increasing by 6.19% from 97 crashes in February 2025 to 103 crashes in February 2026. While total crashes rose, the number of injuries remained stable at 24 in both periods. Fatalities also remained at zero for both February 2025 and February 2026.
4
Hit-and-Run Crashes — February 2026
▼ -42.9% vs prior (7)
Hit-and-run crashes decreased significantly year-over-year, falling by 42.86% in count from 7 incidents in February 2025 to 4 incidents in February 2026. The hit-and-run rate also declined from 7.2% to 3.9% of all crashes, a decrease of 3.3 percentage points.
Vulnerable Road User Casualties
0
Motorists Killed
24
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · 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 significantly year-over-year. The peak day for crashes moved from Friday with 18 incidents in February 2025 to Saturday with 33 incidents in February 2026, representing a 106.25% increase in Saturday crashes. The peak crash hour also changed from 4 PM with 13 crashes in February 2025 to 8 AM with 11 crashes in February 2026, marking a 266.67% increase in crashes at 8 AM.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While total injuries remained constant at 24 in both February 2025 and February 2026, there was a shift in injury severity. Serious injury crashes increased by 100% in count, from 1 to 2, while minor injury crashes decreased by 36.4% in count, from 11 to 7 incidents. Crashes resulting in no injuries saw a 9% increase in count, rising from 78 to 85.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Most severe injury per crash record
Top Contributing Factors
Several contributing factors showed notable year-over-year changes. Crashes attributed to "Inattention" increased by 75% in count, from 8 to 14, and "Failed to yield right of way" crashes surged by 700% in count, from 1 to 8. Conversely, crashes with "No improper driving" decreased by 20.5% in count, from 39 to 31, and "Distracted" driving crashes decreased by 75% in count, from 4 to 1.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Road surface conditions saw a significant shift, with crashes on snowy roads increasing by 40% in count, from 15 in February 2025 to 21 in February 2026. Crashes on wet roads also increased by 22.2% in count, from 9 to 11. Weather conditions remained largely similar for clear conditions, but crashes during cloudy weather increased by 266.7% in count, from 3 to 11.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes increased by 6.8%, from 190 in February 2025 to 203 in February 2026. Honda remained the top make involved, increasing its count by 16.1% from 31 to 36. Hyundai saw a significant 200% increase in involvement, from 4 to 12 vehicles, while Nissan's involvement decreased by 44.4%, from 18 to 10 vehicles. In terms of persons, the 0-15 age group saw a 57.9% decrease in involvement, from 19 to 8, while the 45-54 age group increased by 27.6%, from 29 to 37.
Top Vehicle Makes (203 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Vehicle unit records
24 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (205 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Person-level records linked to crash events
Speed Limit Zones
Crashes occurring in 25 mph speed zones increased by 50% in count, rising from 34 in February 2025 to 51 in February 2026. Conversely, crashes in 30 mph zones decreased by 55.6% in count, from 27 to 12. Crashes in 50 mph zones also saw a 50% decrease in count, falling from 10 to 5.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · 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: 2026-02-01 through 2026-02-28
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2026-02-01 through 2026-02-28 (28 days)
- Geographic scope: PEABODY, MA
- Total crash records analyzed: 103
- Total persons involved: 230
- Total vehicles involved: 203
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). "PEABODY, MA Crash Intelligence Report: February 2026." Published June 21, 2026. Reporting period: 2026-02-01 to 2026-02-28. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/peabody/february-2026-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: 2026-02-01 – 2026-02-28
Generated: June 21, 2026 · All rights reserved