Monthly Traffic Safety Analysis

75 CRASHES IN
PLYMOUTH, MA
JUNE 2025

All metrics benchmarked againstJune 2024

In June 2025, PLYMOUTH experienced 75 crashes, a decrease from 93 crashes in June 2024. This represents a 19.35% reduction in total crashes year-over-year. The most notable shift was an 80% decrease in hit-and-run crashes, falling from 10 to 2 incidents.

75

▼ -19.4%was 93

Total Crash Events

1

Persons Killed

31

▲ 29.2%was 24

Persons Injured

2

▼ -80.0%was 10

Hit-and-Run Crashes

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 1 crash with unreported severity is not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, total crashes in PLYMOUTH decreased year-over-year, from 93 crashes in June 2024 to 75 crashes in June 2025. This represents a reduction of 18 crashes, or a 19.35% decline.

2

Hit-and-Run Crashes — June 2025

▼ -80.0% vs prior (10)

Hit-and-run crashes significantly decreased year-over-year, from 10 incidents in June 2024 to 2 incidents in June 2025. This represents an 80% reduction in hit-and-run incidents. The hit-and-run rate also decreased from 10.8% to 2.7%.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 1→0.0%

2

Pedestrians Injured

Prior: 2→0.0%

2

Cyclists Injured

Prior: 2→0.0%

27

Motorists Injured

Prior: 20↑35.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes shifted year-over-year. In June 2025, the peak day for crashes was Thursday with 17 incidents, whereas in June 2024, Saturday was the peak day with 23 crashes. The peak crash hour also changed from 3 PM with 13 crashes in June 2024 to 1 PM with 10 crashes in June 2025.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the total number of fatalities remained constant at 1 in both periods, the fatal crash rate increased from 1.08% in June 2024 to 1.33% in June 2025. Total injuries increased by 7, from 24 in June 2024 to 31 in June 2025. Serious injury crashes (A) doubled from 2 to 4, and minor injury crashes (B) increased from 14 to 17, while possible injury crashes (C) decreased from 6 to 3.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.3%
→0.0%prior 1
Serious Injury4serious injury crashes5.3%
↑100.0%prior 2
Minor Injury17minor injury crashes22.7%
↑21.4%prior 14
Possible Injury3possible injury crashes4%
↓-50.0%prior 6
No Injury49no injury crashes65.3%
↓-27.9%prior 68

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Most severe injury per crash record

Top Contributing Factors

The most frequently cited contributing factor in June 2025 was "Inattention" with 24 crashes, an increase of 10 crashes (71.4%) from 14 in June 2024. Conversely, "No improper driving" decreased by 15 crashes (65.2%), from 23 in June 2024 to 8 in June 2025, falling from the top factor to the second. "Failed to yield right of way" also saw a decrease of 4 crashes (36.4%), from 11 to 7.

Officer-Reported Primary Contributing Cause

Inattention24 (32%)↑71.4%prior 14
No improper driving8 (10.7%)↓-65.2%prior 23
Failed to yield right of way7 (9.3%)↓-36.4%prior 11
Followed too closely6 (8%)↑20.0%prior 5
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner5 (6.7%)↓-16.7%prior 6
Failure to keep in proper lane or running off road4 (5.3%)
Exceeded authorized speed limit3 (4%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway3 (4%)
Distracted3 (4%)
Driving too fast for conditions2 (2.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in "Clear" weather conditions decreased from 79 in June 2024 to 52 in June 2025. Similarly, crashes on "Dry" road surfaces decreased from 86 to 72. Crashes during "Daylight" conditions also saw a reduction, from 73 to 52.

Weather

Clear52 (69.3%)
↓-34.2%prior 79
Clear/Clear14 (18.7%)
Cloudy6 (8.0%)
→0.0%prior 6
Cloudy/Rain2 (2.7%)
Cloudy/Fog, smog, smoke1 (1.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Weather condition at time of crash

Lighting

Daylight52 (69.3%)
↓-28.8%prior 73
Dark - roadway not lighted10 (13.3%)
↑11.1%prior 9
Dark - lighted roadway9 (12.0%)
↑80.0%prior 5
Dusk3 (4.0%)
Dawn1 (1.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Lighting condition field

Road Surface

Dry72 (96.0%)
↓-16.3%prior 86
Wet3 (4.0%)
↓-50.0%prior 6

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Road surface condition field

Vehicles & Demographics

The total number of persons involved in crashes decreased from 220 in June 2024 to 159 in June 2025. The age group 55-64 experienced the largest reduction in persons involved, decreasing by 15 from 25 to 10. Toyota became the top vehicle make involved in crashes with 19 incidents in June 2025, surpassing Ford, which decreased from 23 to 15 incidents.

Top Vehicle Makes (126 vehicles)

1
TOYOTA19 (15.1%)
↑18.8%prior 16
2
FORD15 (11.9%)
↓-34.8%prior 23
3
HONDA14 (11.1%)
↑16.7%prior 12
4
JEEP9 (7.1%)
→0.0%prior 9
5
HYUNDAI9 (7.1%)
6
CHEVROLET9 (7.1%)
↓-43.8%prior 16
7
NISSAN6 (4.8%)
↓-25.0%prior 8
8
SUBARU6 (4.8%)
↓-33.3%prior 9
9
MERCEDES-BENZ6 (4.8%)
↑20.0%prior 5
10
KIA4 (3.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Vehicle unit records

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

Sex Distribution (152 persons with recorded sex)

Male91 (59.9%)
↓-19.5%prior 113
Female61 (40.1%)
↓-26.5%prior 83

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Person-level records linked to crash events

Speed Limit Zones

The single fatal crash in June 2025 occurred in a 25 mph speed zone, which had a fatal crash rate of 14.286% within that zone, compared to the single fatal crash in June 2024 occurring in a 60 mph zone. Crashes in 60 mph zones decreased by 8, from 16 to 8, while crashes in 30 mph zones decreased by 2, from 26 to 24.

Fatal crashes by zone: 25 mph: 1 of 7 (14.286%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · 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: 2025-06-01 through 2025-06-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2025-06-01 through 2025-06-30 (30 days)
  • Geographic scope: PLYMOUTH, MA
  • Total crash records analyzed: 75
  • Total persons involved: 159
  • Total vehicles involved: 126

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: June 2025." Published June 21, 2026. Reporting period: 2025-06-01 to 2025-06-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/plymouth/june-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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