Monthly Traffic Safety Analysis

53 CRASHES IN
PLYMOUTH, MA
MARCH 2022

All metrics benchmarked againstMarch 2021

In March 2022, PLYMOUTH, MA experienced 53 crashes, an increase of 17.78% from the 45 crashes reported in March 2021. Total injuries saw a substantial increase, rising from 14 in the prior period to 31 in the current period, representing a 121.43% increase. Hit-and-run crashes also increased significantly by 200% year-over-year.

53

17.8%was 45

Total Crash Events

0

Persons Killed

31

121.4%was 14

Persons Injured

3

200.0%was 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 · 2022-03-01 to 2022-03-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash data for March indicates an upward trend in PLYMOUTH, MA. Total crashes increased by 17.78%, from 45 in March 2021 to 53 in March 2022. Concurrently, the number of injured persons rose sharply by 121.43%, from 14 to 31.

3

Hit-and-Run Crashes — March 2022

200.0% vs prior (1)

Hit-and-run crashes increased from 1 in March 2021 to 3 in March 2022, representing a 200% increase. The hit-and-run rate also rose from 2.2% of total crashes in the prior period to 5.7% in the current period. This indicates an upward trend in hit-and-run incidents year-over-year.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

31

Motorists Injured

Prior: 14121.4%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · 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 in March 2021, with 8 crashes, to Friday in March 2022, with 13 crashes. While the peak hour remained 2 PM in both periods, the number of crashes at this hour increased from 5 to 7. There was also a notable increase in crashes on Wednesday, rising from 7 to 12.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There were no fatalities reported in either March 2021 or March 2022. However, the total number of injuries increased from 14 to 31 year-over-year. The proportion of crashes resulting in minor injuries (B) rose from 15.6% to 20.8%, and crashes with possible injuries (C) increased from 8.9% to 17% of total crashes.

Outcome by Severity (Crash Events)

Minor Injury11minor injury crashes20.8%
57.1%prior 7
Possible Injury9possible injury crashes17%
125.0%prior 4
No Injury33no injury crashes62.3%
6.5%prior 31

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Most severe injury per crash record

Top Contributing Factors

The contributing factor 'Followed too closely' saw a 125% increase in crash count, rising from 4 in March 2021 to 9 in March 2022. 'Inattention' also increased in count from 7 to 8 crashes, a 14.3% change. The factor 'No improper driving' increased from 6 to 8 crashes, a 33.3% increase in count.

Officer-Reported Primary Contributing Cause

Followed too closely9 (17%)
Inattention8 (15.1%)14.3%prior 7
No improper driving8 (15.1%)33.3%prior 6
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner5 (9.4%)
Failed to yield right of way5 (9.4%)0.0%prior 5
Disregarded traffic signs, signals, road markings2 (3.8%)
Driving too fast for conditions2 (3.8%)
Fatigued/asleep2 (3.8%)
Operating defective equipment1 (1.9%)
Made an improper turn1 (1.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring on wet road surfaces increased from 4 in March 2021 to 10 in March 2022. This aligns with an increase in crashes during rainy conditions, which rose from 1 to 4. Additionally, there were 2 crashes on snowy roads in the current period, compared to none in the prior period.

Weather

Clear39 (75.0%)
2.6%prior 38
Rain4 (7.7%)
Rain/Snow3 (5.8%)
Cloudy3 (5.8%)
Cloudy/Snow1 (1.9%)
Rain/Cloudy1 (1.9%)
Clear/Unknown1 (1.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Weather condition at time of crash

Lighting

Daylight37 (69.8%)
15.6%prior 32
Dark - lighted roadway9 (17.0%)
12.5%prior 8
Dark - roadway not lighted5 (9.4%)
Dawn1 (1.9%)
Dusk1 (1.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Lighting condition field

Road Surface

Dry39 (73.6%)
-4.9%prior 41
Wet10 (18.9%)
Sand, mud, dirt, oil, gravel2 (3.8%)
Snow2 (3.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Road surface condition field

Vehicles & Demographics

Toyota remained a top vehicle make involved in crashes, although its count decreased from 15 to 13. Honda vehicles saw a significant increase in involvement, rising from 2 to 10 crashes, moving it into the top ranks. The number of persons aged 16-20 involved in crashes increased from 6 to 11, while those aged 21-25 increased from 7 to 15.

Top Vehicle Makes (94 vehicles)

1
TOYOTA13 (13.8%)
-13.3%prior 15
2
FORD11 (11.7%)
22.2%prior 9
3
HONDA10 (10.6%)
4
NISSAN9 (9.6%)
0.0%prior 9
5
CHEVROLET8 (8.5%)
60.0%prior 5
6
JEEP8 (8.5%)
33.3%prior 6
7
GMC5 (5.3%)
0.0%prior 5
8
HYUNDAI4 (4.3%)
9
KIA4 (4.3%)
10
DODGE3 (3.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Vehicle unit records

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

Sex Distribution (113 persons with recorded sex)

Female59 (52.2%)
31.1%prior 45
Male54 (47.8%)
25.6%prior 43

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in the 30 mph speed zone decreased from 25 in March 2021 to 18 in March 2022. Conversely, crashes in the 60 mph speed zone increased from 2 to 10, and those in the 40 mph speed zone rose from 3 to 9. No fatal crashes were recorded in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · 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: 2022-03-01 through 2022-03-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2022-03-01 through 2022-03-31 (31 days)
  • Geographic scope: PLYMOUTH, MA
  • Total crash records analyzed: 53
  • Total persons involved: 117
  • Total vehicles involved: 94

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: March 2022." Published June 21, 2026. Reporting period: 2022-03-01 to 2022-03-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/plymouth/march-2022-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 — March 2022 | ThatCarHitMe.com