Monthly Traffic Safety Analysis

47 CRASHES IN
SEEKONK, MA
FEBRUARY 2025

All metrics benchmarked againstFebruary 2024

Total crashes in SEEKONK, MA decreased from 60 in February 2024 to 47 in February 2025, representing a 21.7% reduction year-over-year. This period also saw a significant positive shift, as fatalities dropped from 1 in the prior year to 0 in the current year. The most notable shift was the decrease in total crashes and the absence of fatalities in the current period.

47

-21.7%was 60

Total Crash Events

0

-100.0%was 1

Persons Killed

14

-12.5%was 16

Persons Injured

2

-33.3%was 3

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash data for SEEKONK, MA shows a declining trend year-over-year. Total crashes decreased by 13 incidents, from 60 in February 2024 to 47 in February 2025, marking a 21.7% reduction. Concurrently, total injuries decreased by 12.5%, from 16 to 14, and fatalities decreased from 1 to 0.

2

Hit-and-Run Crashes — February 2025

-33.3% vs prior (3)

Hit-and-run crashes decreased from 3 incidents in February 2024 to 2 incidents in February 2025. The hit-and-run rate also saw a slight decrease, moving from 5.0% in the prior period to 4.3% in the current period. This indicates a downward trend in both the count and rate of hit-and-run incidents.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 1-100.0%

14

Motorists Injured

Prior: 16-12.5%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · 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 Wednesday with 14 crashes in February 2024 to Saturday with 10 crashes in February 2025. The peak crash hour also changed, moving from 4 PM with 8 crashes in the prior period to 3 PM with 5 crashes in the current period. While the prior period showed higher counts on weekdays, the current period shows a more even distribution across weekdays, with Saturday emerging as the new peak.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

A significant improvement in crash severity was observed, with total fatalities decreasing from 1 in February 2024 to 0 in February 2025. Fatal crashes also decreased from 1 to 0. The number of minor injuries increased from 3 crashes (5% of total) to 6 crashes (12.8% of total), while possible injuries increased from 2 crashes (3.3% of total) to 3 crashes (6.4% of total).

Outcome by Severity (Crash Events)

Minor Injury6minor injury crashes12.8%
100.0%prior 3
Possible Injury3possible injury crashes6.4%
50.0%prior 2
No Injury35no injury crashes74.5%
-28.6%prior 49

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factor shifted from 'Inattention' in February 2024, which accounted for 20 crashes, to 'No improper driving' in February 2025, which rose from 11 to 23 crashes, an increase of 109.1%. 'Inattention' crashes decreased by 11 incidents, from 20 to 9, while 'Failed to yield right of way' crashes decreased from 9 to 5, a 44.4% reduction. This indicates a notable change in the primary contributing factors reported for crashes.

Officer-Reported Primary Contributing Cause

No improper driving23 (48.9%)109.1%prior 11
Inattention9 (19.1%)-55.0%prior 20
Failed to yield right of way5 (10.6%)-44.4%prior 9
Other improper action2 (4.3%)
Illness2 (4.3%)
Distracted1 (2.1%)
Driving too fast for conditions1 (2.1%)
Fatigued/asleep1 (2.1%)
Followed too closely1 (2.1%)-83.3%prior 6
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (2.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear' weather conditions decreased from 52 in February 2024 to 30 in February 2025. Crashes during 'Daylight' conditions also decreased from 41 to 30. While 'Dry' road surface crashes decreased from 53 to 33, crashes on 'Wet' roads increased from 6 to 10, representing a 66.7% increase. Additionally, 'Snow' related crashes increased from 1 to 4.

Weather

Clear30 (63.8%)
-42.3%prior 52
Rain5 (10.6%)
Snow4 (8.5%)
Clear/Clear3 (6.4%)
Cloudy2 (4.3%)
Unknown/Clear1 (2.1%)
Cloudy/Rain1 (2.1%)
Cloudy/Snow1 (2.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Weather condition at time of crash

Lighting

Daylight30 (65.2%)
-26.8%prior 41
Dark - lighted roadway8 (17.4%)
-46.7%prior 15
Dark - roadway not lighted6 (13.0%)
Dusk2 (4.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Lighting condition field

Road Surface

Dry33 (71.7%)
-37.7%prior 53
Wet10 (21.7%)
66.7%prior 6
Snow2 (4.3%)
Ice1 (2.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Road surface condition field

Vehicles & Demographics

The total number of persons involved in crashes decreased from 155 in February 2024 to 116 in February 2025. The age group 16-20 saw a notable decrease in involvement, from 23 persons to 11 persons. Toyota remained the top vehicle make involved in crashes, though its count decreased from 27 to 15, while Honda also saw a decrease from 17 to 8.

Top Vehicle Makes (85 vehicles)

1
TOYOTA15 (17.6%)
-44.4%prior 27
2
HONDA8 (9.4%)
-52.9%prior 17
3
FORD7 (8.2%)
-36.4%prior 11
4
CHEVROLET6 (7.1%)
-14.3%prior 7
5
HYUNDAI5 (5.9%)
6
MAZDA4 (4.7%)
7
GMC4 (4.7%)
8
JEEP4 (4.7%)
9
ACURA4 (4.7%)
10
INFI3 (3.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Vehicle unit records

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

Sex Distribution (107 persons with recorded sex)

Male70 (65.4%)
-16.7%prior 84
Female37 (34.6%)
-43.1%prior 65

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 35 mph zones saw the largest decrease, dropping from 17 in February 2024 to 4 in February 2025, a 76.5% reduction. Crashes in 40 mph zones also decreased from 15 to 9. Conversely, crashes in 50 mph zones increased from 3 to 6, and crashes in 5 mph zones increased from 5 to 6. There were no fatalities recorded across any speed zone in the current period, compared to one fatality in a 35 mph zone in the prior period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · 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: 2025-02-01 through 2025-02-28
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2025-02-01 through 2025-02-28 (28 days)
  • Geographic scope: SEEKONK, MA
  • Total crash records analyzed: 47
  • Total persons involved: 116
  • Total vehicles involved: 85

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). "SEEKONK, MA Crash Intelligence Report: February 2025." Published June 21, 2026. Reporting period: 2025-02-01 to 2025-02-28. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/seekonk/february-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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Seekonk, MA Crash Report — February 2025 | ThatCarHitMe.com