Monthly Traffic Safety Analysis

4 CRASHES IN
MATTAPOISETT, MA
SEPTEMBER 2023

All metrics benchmarked againstSeptember 2022

Total crashes in MATTAPOISETT decreased significantly from 18 in September 2022 to 4 in September 2023, representing a 77.78% reduction. While fatalities remained at 0 in both periods, total injuries decreased from 5 to 1. The most notable shift was the substantial drop in overall crash volume.

4

-77.8%was 18

Total Crash Events

0

Persons Killed

1

-80.0%was 5

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.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash incidents in MATTAPOISETT showed a strong downward trend year-over-year, falling from 18 crashes in September 2022 to 4 crashes in September 2023. This represents a 77.78% decrease in total crashes. Similarly, total injuries decreased from 5 to 1, while fatalities remained at 0 in both periods.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

1

Motorists Injured

Prior: 5-80.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · 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 year-over-year. In September 2023, the peak day for crashes was Thursday with 2 incidents, compared to Wednesday with 4 incidents in September 2022. The peak hour also changed, with 8 PM recording 1 crash in September 2023, whereas 12 PM was the peak hour in September 2022 with 3 crashes.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatalities remained at 0 in both September 2022 and September 2023. Total injuries decreased from 5 in September 2022 to 1 in September 2023. In the current period, 25% of crashes involved a minor injury, compared to 11.1% involving minor injuries and 11.1% involving possible injuries in the prior period.

Outcome by Severity (Crash Events)

Minor Injury1minor injury crashes25%
-50.0%prior 2
No Injury3no injury crashes75%
-75.0%prior 12

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Most severe injury per crash record

Top Contributing Factors

The contributing factor 'Inattention' decreased significantly, from 5 crashes in September 2022 to 1 crash in September 2023. Crashes attributed to 'No improper driving' also saw a reduction, dropping from 4 to 1 year-over-year. Conversely, 'Distracted' driving, which was not explicitly listed in the prior period's top factors, was a factor in 1 crash in September 2023.

Officer-Reported Primary Contributing Cause

Distracted1 (25%)
Inattention1 (25%)-80.0%prior 5
No improper driving1 (25%)
Over-correcting/over-steering1 (25%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Regarding lighting conditions, the proportion of crashes occurring in daylight decreased from 66.7% (12 of 18 crashes) in September 2022 to 50% (2 of 4 crashes) in September 2023. Conversely, the share of crashes occurring in dark conditions increased from 33.3% (6 of 18 crashes) to 50% (2 of 4 crashes) year-over-year. Data for weather and road surface conditions were not consistently available for comparison.

Lighting

Daylight2 (50.0%)
-83.3%prior 12
Dark - lighted roadway1 (25.0%)
Dark - roadway not lighted1 (25.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Lighting condition field

Vehicles & Demographics

Top Vehicle Makes (5 vehicles)

1
CHEVROLET1 (20%)
2
HONDA1 (20%)
3
LEXUS1 (20%)
4
NISSAN1 (20%)
5
VOLKSWAGEN1 (20%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Vehicle unit records

Sex Distribution (7 persons with recorded sex)

Male4 (57.1%)
-66.7%prior 12
Female3 (42.9%)
-83.3%prior 18

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes at the 5 mph, 35 mph, and 65 mph speed limits remained consistent year-over-year, with 1, 2, and 1 crash respectively. However, many speed zones that recorded crashes in September 2022, such as 10 mph (2 crashes), 20 mph (2 crashes), 40 mph (2 crashes), 45 mph (4 crashes), and 50 mph (2 crashes), reported no crashes in September 2023. Fatal crash rates remained 0 across all speed zones in both periods.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · 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: 2023-09-01 through 2023-09-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-09-01 through 2023-09-30 (30 days)
  • Geographic scope: MATTAPOISETT, MA
  • Total crash records analyzed: 4
  • Total persons involved: 7
  • Total vehicles involved: 5

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: September 2023." Published June 21, 2026. Reporting period: 2023-09-01 to 2023-09-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/mattapoisett/september-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

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Mattapoisett, MA Crash Report — September 2023 | ThatCarHitMe.com