ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · SEEKONK, MA · 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/2022-annual-report
Yearly Traffic Safety Analysis
699 CRASHES IN
SEEKONK, MA
2022
In 2022, Seekonk recorded 699 total crashes, a 6.7% increase from the 655 crashes reported in 2021. While total incidents rose, the number of people injured decreased from 184 to 150. The most significant year-over-year change was a doubling in traffic fatalities, which rose from 2 in 2021 to 4 in 2022.
699
▲ 6.7%was 655
Total Crash Events
4
▲ 100.0%was 2
Persons Killed
150
▼ -18.5%was 184
Persons Injured
12
▼ -36.8%was 19
Hit-and-Run Crashes
Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 121 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 2022-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend shows an increase in total crashes in Seekonk, rising by 6.7% from 655 in 2021 to 699 in 2022. However, this increase in crashes was accompanied by a 18.5% decrease in total injuries (from 184 to 150). Conversely, the number of fatalities doubled from 2 to 4 over the same period.
12
Hit-and-Run Crashes — 2022
▼ -36.8% vs prior (19)
The number of hit-and-run incidents in Seekonk decreased from 2021 to 2022. There were 12 hit-and-run crashes recorded in 2022, a reduction from the 19 incidents reported in the prior year. Consequently, the hit-and-run rate as a percentage of all crashes fell from 2.9% in 2021 to 1.7% in 2022.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
4
Motorists Killed
0
Other Killed
1
Pedestrians Injured
1
Cyclists Injured
146
Motorists Injured
2
Other Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · 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 between the two periods. In 2022, the peak day for crashes was Tuesday with 120 incidents, a change from 2021 when Saturday was the peak day with 108 crashes. Similarly, the busiest hour for collisions moved from 2 p.m. in 2021 (74 crashes) to the 5 p.m. rush hour in 2022 (78 crashes).
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes showed a mixed trend year-over-year. The number of fatal crashes increased from 2 in 2021 to 3 in 2022, with the fatal crash rate rising from 0.31 to 0.43 per 100 crashes. In contrast, the proportion of crashes resulting in any level of injury (serious, minor, or possible) decreased from 18.6% of all crashes in 2021 to 16.3% in 2022, driven by a drop in both serious and minor injury crashes.
Severity is per crash event (most severe injury). 3 fatal crash events resulted in 4 persons killed.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Most severe injury per crash record
Top Contributing Factors
Inattention remained the leading contributing factor in both years, with its count increasing by 65.5% from 119 in 2021 to 197 in 2022. The count of crashes where 'No improper driving' was cited also grew substantially, from 70 to 137. 'Failed to yield right of way' saw its count increase from 52 to 82, while 'Followed too closely' incidents rose from 71 to 87.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The distribution of environmental conditions at the time of crashes remained largely consistent between 2021 and 2022. Crashes on dry road surfaces accounted for 83.3% of incidents in 2022, compared to 82.6% in 2021. Similarly, daylight crashes made up 71.8% of the total in 2022, nearly identical to the 71.1% share in the prior year, indicating no significant shift in conditions.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Road surface condition field
Vehicles & Demographics
The top vehicle makes involved in crashes were consistent, with Toyota, Honda, and Ford being the most common in both 2021 and 2022. While Toyota's involvement decreased from 196 vehicles to 178, Chevrolet's involvement increased from 74 vehicles to 124. The age distribution of persons involved in crashes also remained stable, with the 26-34 age group being the largest in both years, accounting for 261 individuals in both periods.
Top Vehicle Makes (1,247 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records
61 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (1,508 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across speed zones was similar year-over-year, with the 40 mph zone seeing the most incidents in both 2021 (200 crashes) and 2022 (229 crashes). Fatal crashes shifted to different speed zones between the periods. In 2022, fatal crashes were recorded in 35, 40, and 50 mph zones, whereas in 2021, they occurred in the 50 and 65 mph zones.
Fatal crashes by zone: 35 mph: 1 of 129 (0.775%) · 40 mph: 1 of 229 (0.437%) · 50 mph: 1 of 19 (5.263%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · 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 2022-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-01-01 through 2022-12-31 (365 days)
- Geographic scope: SEEKONK, MA
- Total crash records analyzed: 699
- Total persons involved: 1,590
- Total vehicles involved: 1,247
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: 2022." Published June 21, 2026. Reporting period: 2022-01-01 to 2022-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/seekonk/2022-annual-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 – 2022-12-31
Generated: June 21, 2026 · All rights reserved