ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · PLYMOUTH, MA · NOVEMBER 2025
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/plymouth/november-2025-report
Monthly Traffic Safety Analysis
89 CRASHES IN
PLYMOUTH, MA
NOVEMBER 2025
Total crashes in PLYMOUTH, MA increased by 3.49%, from 86 in November 2024 to 89 in November 2025. Total injuries also rose by 21.74%, from 23 to 28. A notable shift includes the complete absence of pedestrian and bicycle crashes in the current period, down from 5 pedestrian crashes and 1 bicycle crash in the prior period.
89
▲ 3.5%was 86
Total Crash Events
0
Persons Killed
28
▲ 21.7%was 23
Persons Injured
6
▼ -40.0%was 10
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.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, crashes in PLYMOUTH increased year-over-year, with total crashes rising from 86 in November 2024 to 89 in November 2025, a 3.49% increase. Total injuries also increased by 5, from 23 to 28, representing a 21.74% rise. Fatalities remained at 0 in both periods, indicating no change in fatal crash outcomes.
6
Hit-and-Run Crashes — November 2025
▼ -40.0% vs prior (10)
Hit-and-run crashes decreased by 40%, from 10 incidents in the prior period to 6 in the current period. This reduction is also reflected in the hit-and-run crash rate, which declined from 11.6% to 6.7% year-over-year.
Vulnerable Road User Casualties
0
Motorists Killed
28
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The peak day for crashes shifted from Friday with 18 crashes in the prior period to Tuesday with 16 crashes in the current period. Similarly, the peak hour changed from 7 p.m. with 10 crashes in the prior period to 8 p.m. with 9 crashes in the current period. Crashes on Monday increased by 5, from 8 to 13, while Friday saw a decrease of 4 crashes, from 18 to 14.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The number of serious injury crashes (code A) remained constant at 1 in both periods. Minor injury crashes (code B) increased by 7, from 11 in the prior period to 18 in the current period, a 63.6% increase. Conversely, possible injury crashes (code C) decreased by 4, from 7 to 3, representing a 57.1% reduction.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Most severe injury per crash record
Top Contributing Factors
Crashes attributed to 'Followed too closely' increased by 3, from 8 to 11, representing a 37.5% rise in count. Conversely, crashes due to 'Operating vehicle in erratic, reckless, careless, negligent or aggressive manner' decreased by 3 (from 11 to 8), and 'Failed to yield right of way' also decreased by 3 (from 11 to 8). 'Exceeded authorized speed limit' and 'Driving too fast for conditions' each accounted for 2 crashes in the current period, having registered 0 in the prior period.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in 'Clear' weather conditions decreased from 61 to 51, while those in 'Clear/Clear' conditions increased from 7 to 18. Crashes during 'Rain' conditions doubled from 5 to 10. The number of crashes on 'Wet' road surfaces increased by 4, from 13 to 17, while 'Dry' road surface crashes saw a minor increase from 71 to 72.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Road surface condition field
Vehicles & Demographics
The number of persons involved in crashes aged 0-15 increased from 4 to 10, and those aged 16-20 more than doubled from 8 to 22. Conversely, persons aged 26-34 and 35-44 each saw a decrease of 5, from 26 to 21 and 27 to 22 respectively. Among vehicle makes, Toyota saw a notable increase in involvement from 15 to 22, as did Lexus and BMW, both increasing from 1 to 7, while Jeep involvement decreased from 10 to 3.
Top Vehicle Makes (151 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Vehicle unit records
15 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (170 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Person-level records linked to crash events
Speed Limit Zones
Crashes occurring in 60 mph speed zones increased by 6, from 11 in the prior period to 17 in the current period. Crashes in 30 mph zones also saw an increase of 3, from 26 to 29. Conversely, crashes in 35 mph zones decreased by 5, from 13 to 8, and in 20 mph zones decreased by 4, from 5 to 1. No fatalities were reported in any speed limit category during either period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · 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: 2025-11-01 through 2025-11-30
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2025-11-01 through 2025-11-30 (30 days)
- Geographic scope: PLYMOUTH, MA
- Total crash records analyzed: 89
- Total persons involved: 187
- Total vehicles involved: 151
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). "PLYMOUTH, MA Crash Intelligence Report: November 2025." Published June 21, 2026. Reporting period: 2025-11-01 to 2025-11-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/plymouth/november-2025-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: 2025-11-01 – 2025-11-30
Generated: June 21, 2026 · All rights reserved