Monthly Traffic Safety Analysis

9 CRASHES IN
MATTAPOISETT, MA
NOVEMBER 2025

All metrics benchmarked againstNovember 2024

In November 2025, Mattapoisett recorded 9 total crashes, marking a 40% decrease compared to the 15 crashes reported in November 2024. A notable shift is the absence of any injuries or fatalities in the current period, down from 2 injuries and 0 fatalities in the prior year.

9

-40.0%was 15

Total Crash Events

0

Persons Killed

0

-100.0%was 2

Persons Injured

0

Fatal Crash Events

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. 9 crashes with unreported severity are not shown in the severity breakdown.

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, crash trends in Mattapoisett show a significant decline year-over-year, with total crashes decreasing by 40% from 15 to 9. This reduction is also reflected in injury counts, which fell from 2 in the prior period to 0 in the current period, while fatalities remained at 0 in both periods.

When Crashes Happen

The temporal patterns of crashes shifted, with the peak day moving from Wednesday (4 crashes) in November 2024 to Saturday and Thursday (3 crashes each) in November 2025. The peak hour for crashes also changed from 11 PM (3 crashes) in the prior period to 5 PM (2 crashes) in the current period.

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)

Top Contributing Factors

The leading contributing factor, "No improper driving," decreased from 9 crashes in the prior period to 7 crashes in the current period. "Driving too fast for conditions" remained consistent at 1 crash in both periods. "Inattention" emerged as a factor in 1 crash in the current period, whereas it was not listed in the prior period.

Officer-Reported Primary Contributing Cause

No improper driving7 (77.8%)-22.2%prior 9
Driving too fast for conditions1 (11.1%)
Inattention1 (11.1%)

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

The proportion of crashes on wet road surfaces increased, with 3 crashes occurring on wet roads in the current period compared to 1 crash in the prior period. Crashes under clear weather conditions decreased from 9 to 6, and crashes occurring in daylight decreased from 6 to 4 year-over-year. Crashes in "Fog, smog, smoke" increased from 0 to 1.

Weather

Clear6 (66.7%)
-33.3%prior 9
Clear/Clear2 (22.2%)
Fog, smog, smoke1 (11.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Weather condition at time of crash

Lighting

Daylight4 (44.4%)
-33.3%prior 6
Dark - roadway not lighted3 (33.3%)
-40.0%prior 5
Dark - lighted roadway2 (22.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Lighting condition field

Road Surface

Dry5 (62.5%)
-64.3%prior 14
Wet3 (37.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (13 vehicles)

1
NISSAN2 (15.4%)
2
SUBARU2 (15.4%)
3
HONDA1 (7.7%)
4
KIA1 (7.7%)
5
MAZDA1 (7.7%)
6
MERCEDES-BENZ1 (7.7%)
7
RAM TRUCKS1 (7.7%)
8
TOYOTA1 (7.7%)
9
BMW1 (7.7%)
10
VOLKSWAGEN1 (7.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Vehicle unit records

4 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (12 persons with recorded sex)

Female6 (50.0%)
-50.0%prior 12
Male6 (50.0%)
-40.0%prior 10

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

The total number of crashes with recorded speed limits decreased from 13 in the prior period to 6 in the current period. Crashes in higher speed zones, such as 45 mph (3 crashes) and 65 mph (7 crashes), which were present in the prior period, are absent in the current period. Conversely, crashes at lower speed limits of 10 mph (2 crashes) and 15 mph (2 crashes) are observed in the current period but were not present in the prior period, while 35 mph zones remained at 2 crashes in both periods.

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: MATTAPOISETT, MA
  • Total crash records analyzed: 9
  • Total persons involved: 17
  • Total vehicles involved: 13

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). "MATTAPOISETT, 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/mattapoisett/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

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Mattapoisett, MA Crash Report — November 2025 | ThatCarHitMe.com