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YEAR-OVER-YEAR CRASH REPORT · SEEKONK, MA · JANUARY 2022
Purpose: Machine-readable JSON endpoint for AI agents, LLMs, researchers, and programmatic consumers. Returns all underlying crash data and AI-generated commentary without HTML.
Authentication: None required. Public endpoint.
GET: https://thatcarhitme.com/api/crash-data/reports/data/massachusetts/seekonk/january-2022-report
Monthly Traffic Safety Analysis
59 CRASHES IN
SEEKONK, MA
JANUARY 2022
In January 2022, SEEKONK, MA experienced 59 total crashes, an increase from the 51 crashes reported in January 2021, representing a 15.69% rise. The most significant year-over-year shift was the emergence of fatal crashes, with 2 fatalities in January 2022 compared to zero in the prior year.
59
▲ 15.7%was 51
Total Crash Events
2
Persons Killed
24
▲ 100.0%was 12
Persons Injured
2
Hit-and-Run Crashes
Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 5 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, crash incidents in SEEKONK, MA showed an upward trend, with total crashes increasing from 51 in January 2021 to 59 in January 2022, a 15.69% rise. This period also saw a notable increase in total injuries, which doubled from 12 to 24, and the occurrence of 2 fatal crashes where none were recorded in the prior year.
2
Hit-and-Run Crashes — January 2022
▼ 0.0% vs prior (2)
The number of hit-and-run crashes remained constant at 2 in both January 2021 and January 2022. However, the hit-and-run crash rate decreased slightly from 3.9% in January 2021 to 3.4% in January 2022, reflecting the overall increase in total crashes.
Vulnerable Road User Casualties
2
Motorists Killed
24
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · 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 January 2021 (12 crashes) to Friday and Monday in January 2022 (both with 12 crashes). The peak hour for crashes remained 4 PM in both periods, though the count decreased slightly from 10 crashes in January 2021 to 9 crashes in January 2022.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity distribution changed significantly, with January 2022 recording 2 fatal crashes (3.39% fatal rate) and 1 serious injury crash, compared to zero fatal or serious injury crashes in January 2021. Minor injury crashes increased from 5 (9.8% of total crashes) to 8 (13.6%), and possible injury crashes rose from 3 (5.9%) to 6 (10.2%).
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Most severe injury per crash record
Top Contributing Factors
Among specific contributing factors, crashes due to 'Followed too closely' saw a substantial increase, rising from 2 in January 2021 to 13 in January 2022. 'Inattention' also rose significantly from 3 to 12 crashes, and 'Failed to yield right of way' increased from 2 to 5 crashes. Conversely, crashes attributed to 'Failure to keep in proper lane or running off road' decreased from 4 to 1, and 'Driving too fast for conditions' decreased from 2 to 1.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring on 'Dry' road surfaces increased from 40 to 43 year-over-year, while those on 'Wet' surfaces saw a notable rise from 6 to 11. Incidents under 'Dark - roadway not lighted' conditions doubled from 3 to 6 crashes. There was also an increase in crashes during 'Clear' weather, rising from 37 to 43.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Road surface condition field
Vehicles & Demographics
The overall number of persons involved in crashes increased from 111 to 130 year-over-year. The age distribution of persons involved showed notable shifts, with the 0-15 age group increasing from 3 to 10, and the 35-44 age group rising from 16 to 25. Regarding vehicle makes, HONDA vehicles involved in crashes increased significantly from 6 to 14, while TOYOTA vehicles decreased from 17 to 14, and FORD vehicles increased from 12 to 15, resulting in FORD becoming the top-ranked make in January 2022.
Top Vehicle Makes (100 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Vehicle unit records
9 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (123 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 35 mph speed zones decreased from 11 in January 2021 to 7 in January 2022, while crashes in 40 mph zones increased from 18 to 20. Notably, both 35 mph and 40 mph zones recorded 1 fatal crash each in January 2022, whereas no fatal crashes were reported in any speed zone in January 2021.
Fatal crashes by zone: 35 mph: 1 of 7 (14.286%) · 40 mph: 1 of 20 (5%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · 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: 2022-01-01 through 2022-01-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-01-01 through 2022-01-31 (31 days)
- Geographic scope: SEEKONK, MA
- Total crash records analyzed: 59
- Total persons involved: 130
- Total vehicles involved: 100
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: January 2022." Published June 21, 2026. Reporting period: 2022-01-01 to 2022-01-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/seekonk/january-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
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly
Period: 2022-01-01 – 2022-01-31
Generated: June 21, 2026 · All rights reserved