ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · SEEKONK, MA · NOVEMBER 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/november-2022-report
Monthly Traffic Safety Analysis
61 CRASHES IN
SEEKONK, MA
NOVEMBER 2022
Total crashes in Seekonk remained stable at 61 in November 2022, matching the 61 crashes reported in November 2021. However, total injuries saw a significant decrease of 75%, falling from 12 to 3. Notably, speeding-related crashes increased from 0 to 2, and pedestrian and bicycle crashes, previously at 0, each registered 1 incident.
61
Total Crash Events
0
Persons Killed
3
▼ -75.0%was 12
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. 14 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall number of crashes in Seekonk remained stable year-over-year, with 61 incidents recorded in both November 2021 and November 2022. Despite this stability in crash count, total injuries decreased substantially by 75%, from 12 in November 2021 to 3 in November 2022. Fatalities remained at 0 in both periods.
1
Hit-and-Run Crashes — November 2022
▼ 0.0% vs prior (1)
The number of hit-and-run crashes remained stable at 1 in both November 2021 and November 2022. Consequently, the hit-and-run rate also remained unchanged at 1.6% for both periods.
Vulnerable Road User Casualties
0
Cyclists Killed
0
Motorists Killed
1
Cyclists Injured
2
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · 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 Tuesday, with 13 incidents in November 2021, to Wednesday, with 18 incidents in November 2022. The peak crash hour also shifted, from 4 PM with 9 crashes in November 2021, to 6 PM with 8 crashes in November 2022.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Total injuries decreased by 75%, from 12 in November 2021 to 3 in November 2022. Minor injuries decreased from 4 to 2, while possible injuries dropped from 5 to 1. The proportion of crashes resulting in no injury remained high, at 77% in November 2021 and 72.1% in November 2022.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Most severe injury per crash record
Top Contributing Factors
Crashes attributed to "Inattention" decreased slightly from 18 in November 2021 to 17 in November 2022. Conversely, crashes with "No improper driving" as a factor increased from 14 to 17. "Followed too closely" crashes saw a 50% reduction, decreasing from 8 to 4, and "Failed to yield right of way" crashes decreased from 7 to 6.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in clear weather conditions decreased from 58 in November 2021 to 48 in November 2022, while rain-related crashes increased from 0 to 7. Crashes on dry road surfaces decreased from 59 to 50, whereas crashes on wet surfaces increased from 2 to 9, with an additional 2 crashes occurring on roads with standing water in November 2022. Crashes during daylight hours increased from 27 to 30, and those in dark-lighted roadway conditions increased from 15 to 21.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes slightly increased from 104 in November 2021 to 106 in November 2022. Toyota remained the most frequently involved make, increasing from 18 to 21 vehicles, and Honda involvement doubled from 8 to 16 vehicles. The 0-15 age group saw an increase from 7 to 11 persons involved in crashes, and the 26-34 age group increased from 15 to 26 persons, becoming the largest age group represented in November 2022.
Top Vehicle Makes (106 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Vehicle unit records
7 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (126 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Person-level records linked to crash events
Speed Limit Zones
Crashes in the 40 mph speed zone increased from 11 in November 2021 to 19 in November 2022. Similarly, crashes in the 30 mph zone rose from 4 to 12 incidents. Conversely, crashes in the 35 mph zone decreased from 16 to 12, and the 65 mph zone saw a reduction from 9 to 7 crashes.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · 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: 2022-11-01 through 2022-11-30
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-11-01 through 2022-11-30 (30 days)
- Geographic scope: SEEKONK, MA
- Total crash records analyzed: 61
- Total persons involved: 135
- Total vehicles involved: 106
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: November 2022." Published June 21, 2026. Reporting period: 2022-11-01 to 2022-11-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/seekonk/november-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: 2022-11-01 – 2022-11-30
Generated: June 21, 2026 · All rights reserved