Monthly Traffic Safety Analysis

10 CRASHES IN
PLYMPTON, MA
NOVEMBER 2023

All metrics benchmarked againstNovember 2022

Total crashes in Plympton for November 2023 were 10, marking a 66.67% increase from the 6 crashes reported in November 2022. The most notable year-over-year shift was the significant rise in crashes involving a collision with an animal, specifically deer, which increased from 3 in the prior period to 8 in the current period.

10

66.7%was 6

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Total crashes in Plympton increased by 66.67% year-over-year, rising from 6 crashes in November 2022 to 10 crashes in November 2023. During the same period, total injuries decreased by 100%, from 2 injuries to 0 injuries. Fatalities remained stable at 0 in both November 2022 and November 2023.

When Crashes Happen

The peak day for crashes shifted from Saturday, with 1 crash in November 2022, to Tuesday, with 3 crashes in November 2023. The peak hour also changed, moving from 6 p.m. with 1 crash in November 2022 to 5 p.m. with 2 crashes in November 2023. Crashes occurring during dawn hours increased from 1 in the prior period to 3 in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Crash time field aggregated by hour (0-23)

Top Contributing Factors

The contributing factor 'No improper driving' increased significantly from 2 crashes in November 2022 to 8 crashes in November 2023, representing a 300% increase in count. This factor's share of crashes rose from 33.3% to 80%. Factors such as 'Failure to keep in proper lane or running off road,' 'Fatigued/asleep,' and 'Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway' were each associated with 1 crash in November 2022 but were not present in November 2023. 'Failed to yield right of way' appeared as a contributing factor in 1 crash in November 2023, whereas it was not present in the prior period.

Officer-Reported Primary Contributing Cause

No improper driving8 (80%)
Failed to yield right of way1 (10%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear' weather conditions increased from 5 in November 2022 to 7 in November 2023. The number of crashes on 'Dry' road surfaces rose from 5 to 9 year-over-year, while crashes on 'Wet' surfaces remained at 1 for both periods. In terms of lighting, crashes during 'Dark - lighted roadway' conditions increased from 1 to 4, and 'Dawn' crashes increased from 1 to 3, while crashes in 'Dark - roadway not lighted' conditions decreased from 3 to 0.

Weather

Clear7 (70.0%)
40.0%prior 5
Clear/Other1 (10.0%)
Clear/Unknown1 (10.0%)
Cloudy1 (10.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Weather condition at time of crash

Lighting

Dark - lighted roadway4 (40.0%)
Dawn3 (30.0%)
Daylight2 (20.0%)
Dusk1 (10.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Lighting condition field

Road Surface

Dry9 (90.0%)
80.0%prior 5
Wet1 (10.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (11 vehicles)

1
TOYOTA4 (36.4%)
2
NISSAN2 (18.2%)
3
HONDA1 (9.1%)
4
CHEVROLET1 (9.1%)
5
SUBARU1 (9.1%)
6
MERCEDES-BENZ1 (9.1%)
7
GMC1 (9.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Vehicle unit records

Sex Distribution (12 persons with recorded sex)

Male8 (66.7%)
33.3%prior 6
Female4 (33.3%)
100.0%prior 2

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes in the 35 mph speed limit zone increased from 1 in November 2022 to 2 in November 2023. The 40 mph zone saw an increase from 4 crashes to 5 crashes, and the 45 mph zone experienced a 200% increase in crashes, rising from 1 to 3 year-over-year. No fatalities were reported in any speed limit zone for either November 2022 or November 2023.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · 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: 2023-11-01 through 2023-11-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-11-01 through 2023-11-30 (30 days)
  • Geographic scope: PLYMPTON, MA
  • Total crash records analyzed: 10
  • Total persons involved: 12
  • Total vehicles involved: 11

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). "PLYMPTON, MA Crash Intelligence Report: November 2023." Published June 21, 2026. Reporting period: 2023-11-01 to 2023-11-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/plympton/november-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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Plympton, MA Crash Report — November 2023 | ThatCarHitMe.com