Monthly Traffic Safety Analysis

86 CRASHES IN
PLYMOUTH, MA
NOVEMBER 2023

All metrics benchmarked againstNovember 2022

Total crashes in Plymouth increased by 22.9% year-over-year, rising from 70 crashes in November 2022 to 86 crashes in November 2023. Despite this increase in total crashes, the number of injuries saw a slight decrease from 29 to 27. This period saw no fatal crashes in either year.

86

22.9%was 70

Total Crash Events

0

Persons Killed

27

-6.9%was 29

Persons Injured

1

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 · 2023-11-01 to 2023-11-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates a significant increase in crashes year-over-year, with total incidents rising from 70 in November 2022 to 86 in November 2023, a 22.9% increase. This suggests a notable upward trend in crash frequency for the month of November.

1

Hit-and-Run Crashes — November 2023

0.0% vs prior (1)

The number of hit-and-run crashes remained stable at 1 incident in both November 2022 and November 2023. The hit-and-run rate slightly decreased from 1.4% to 1.2% due to the overall increase in total crashes.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

27

Motorists Injured

Prior: 258.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · 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 Saturday (15 crashes) in November 2022 to Thursday (20 crashes) in November 2023. The peak hour also changed, moving from 4 PM with 10 crashes in the prior period to 12 PM with 9 crashes 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)

Crash Severity Breakdown

There were no fatal crashes or fatalities reported in either November 2022 or November 2023. While serious injury crashes remained stable at 3 incidents in both periods, minor injury crashes decreased from 15 to 13, and possible injury crashes decreased from 6 to 4. Conversely, crashes with no injuries increased significantly from 46 to 66, representing a 43.5% rise in count.

Outcome by Severity (Crash Events)

Serious Injury3serious injury crashes3.5%
0.0%prior 3
Minor Injury13minor injury crashes15.1%
-13.3%prior 15
Possible Injury4possible injury crashes4.7%
-33.3%prior 6
No Injury66no injury crashes76.7%
43.5%prior 46

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Most severe injury per crash record

Top Contributing Factors

Crashes attributed to 'Inattention' saw a 100% increase in count, rising from 8 crashes in November 2022 to 16 crashes in November 2023. 'Followed too closely' also significantly increased by 133.3% in count, from 3 crashes to 7 crashes. Conversely, 'Failed to yield right of way' decreased by 30% in count, from 10 crashes to 7 crashes.

Officer-Reported Primary Contributing Cause

No improper driving18 (20.9%)-5.3%prior 19
Inattention16 (18.6%)100.0%prior 8
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner8 (9.3%)14.3%prior 7
Followed too closely7 (8.1%)
Failed to yield right of way7 (8.1%)-30.0%prior 10
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway7 (8.1%)
Visibility obstructed4 (4.7%)
Failure to keep in proper lane or running off road4 (4.7%)
Glare2 (2.3%)
Other improper action2 (2.3%)

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 55 to 69, while crashes in wet road conditions doubled from 7 to 15. Daylight crashes increased from 33 to 51, and crashes in dark conditions on unlighted roadways increased from 13 to 17. Crashes during dusk saw a decrease from 9 to 3.

Weather

Clear69 (80.2%)
25.5%prior 55
Rain6 (7.0%)
Cloudy3 (3.5%)
Cloudy/Rain3 (3.5%)
Rain/Severe crosswinds2 (2.3%)
Clear/Cloudy1 (1.2%)
Rain/Clear1 (1.2%)
Rain/Cloudy1 (1.2%)

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

Lighting

Daylight51 (59.3%)
54.5%prior 33
Dark - roadway not lighted17 (19.8%)
30.8%prior 13
Dark - lighted roadway11 (12.8%)
-21.4%prior 14
Dawn3 (3.5%)
Dusk3 (3.5%)
-66.7%prior 9
Dark - unknown roadway lighting1 (1.2%)

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

Road Surface

Dry69 (80.2%)
13.1%prior 61
Wet15 (17.4%)
114.3%prior 7
Sand, mud, dirt, oil, gravel2 (2.3%)

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

Vehicles & Demographics

Toyota and Ford remained the top two vehicle makes involved in crashes, with Toyota increasing from 18 to 21 and Ford from 17 to 19. The number of persons aged 55-64 involved in crashes surged by 230%, from 10 in November 2022 to 33 in November 2023. Additionally, persons aged 16-20 involved in crashes increased by 78.9%, from 19 to 34.

Top Vehicle Makes (151 vehicles)

1
TOYOTA21 (13.9%)
16.7%prior 18
2
FORD19 (12.6%)
11.8%prior 17
3
JEEP13 (8.6%)
62.5%prior 8
4
HONDA11 (7.3%)
-26.7%prior 15
5
CHEVROLET10 (6.6%)
0.0%prior 10
6
NISSAN10 (6.6%)
100.0%prior 5
7
SUBARU9 (6%)
8
HYUNDAI8 (5.3%)
33.3%prior 6
9
VOLKSWAGEN5 (3.3%)
10
KIA4 (2.6%)

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

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

Sex Distribution (176 persons with recorded sex)

Male97 (55.1%)
47.0%prior 66
Female79 (44.9%)
16.2%prior 68

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 60 mph speed zones experienced the largest increase, rising by 5 crashes from 11 to 16, a 45.5% increase. Crashes in 30 mph zones saw a slight increase from 22 to 23. There were no fatal crashes recorded in any speed zone during either period.

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: PLYMOUTH, MA
  • Total crash records analyzed: 86
  • Total persons involved: 190
  • 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 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/plymouth/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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Plymouth, MA Crash Report — November 2023 | ThatCarHitMe.com