ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · SEEKONK, MA · 2025
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/2025-annual-report
Yearly Traffic Safety Analysis
678 CRASHES IN
SEEKONK, MA
2025
In Seekonk, total traffic crashes decreased by 11.9% from 770 incidents in 2024 to 678 in 2025. While overall crashes and injuries declined, the number of incidents involving suspected drunk driving increased from 3 to 11. Fatalities remained unchanged year-over-year, with two deaths recorded in both periods.
678
▼ -11.9%was 770
Total Crash Events
2
Persons Killed
172
▼ -22.9%was 223
Persons Injured
65
▲ 32.7%was 49
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. 24 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Traffic safety trends in Seekonk show a notable improvement, with total crashes falling by 11.9% from 770 in 2024 to 678 in 2025. This decline was accompanied by a 22.9% reduction in total injuries, which dropped from 223 to 172. However, the number of fatalities held steady at two for both years.
65
Hit-and-Run Crashes — 2025
▲ 32.7% vs prior (49)
Hit-and-run incidents trended upward, bucking the overall decline in crashes. The absolute number of hit-and-run crashes increased by 32.7%, rising from 49 in the prior year to 65 in the current year. Consequently, the hit-and-run rate as a percentage of all crashes also grew, increasing from 6.4% to 9.6%.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
2
Motorists Killed
2
Pedestrians Injured
2
Cyclists Injured
168
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · 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 slightly year-over-year. The peak day for crashes moved from Friday (123 crashes) in the prior year to Saturday (117 crashes) in the current year. The afternoon commute remained the most common time for incidents, with the peak hour concentrating at 3 p.m. (80 crashes) in the current period, compared to a shared peak at 2 p.m. and 3 p.m. (70 crashes each) in the prior year.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes showed a mixed profile year-over-year. The number of fatal crashes and resulting fatalities remained constant at 2 for both periods, though the fatal crash rate increased slightly from 0.26% to 0.29% due to the lower total crash volume. The proportion of crashes resulting in any level of injury decreased from 19.9% to 18.9%, with a notable drop in 'Possible Injury' crashes from 69 to 40. Conversely, the share of non-injury crashes rose from 75.5% to 77.3% of all incidents.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record
Top Contributing Factors
The ranking of top contributing factors changed between the two periods. 'Inattention,' the leading factor in the prior year with 240 incidents, saw its count drop by 38% to 148, making it the second-leading factor in the current year. 'No improper driving' became the most cited factor, with its count increasing from 151 to 199. 'Failed to yield right of way' remained the third-most-common factor, though its count decreased from 105 to 92.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crash conditions remained largely consistent year-over-year, with the vast majority of incidents occurring in clear weather and on dry roads in both periods. Crashes during daylight hours accounted for 74.0% of incidents in the current year, a slight increase in share from 72.5% in the prior year. Notably, crashes on dark, lighted roadways decreased from 112 incidents in the prior year to 78 in the current year.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field
Vehicles & Demographics
Vehicle and person demographics saw minor shifts. Toyota and Honda remained the top two vehicle makes involved in crashes in both years, though their counts decreased from 259 to 213 and 198 to 136, respectively. The most-involved age group for persons in crashes shifted from 26-34 years old in the prior period (306 people) to 35-44 years old in the current period (261 people).
Top Vehicle Makes (1,269 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records
108 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (1,437 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Person-level records linked to crash events
Speed Limit Zones
The 40 mph speed zone continued to have the highest number of crashes in both periods, though the count fell from 258 to 217 year-over-year. A notable shift occurred in the location of fatal crashes. In the current year, both fatal crashes happened in 65 mph zones, whereas in the prior year, the two fatalities were split between a 35 mph zone and a 65 mph zone.
Fatal crashes by zone: 65 mph: 2 of 47 (4.255%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · 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: 2025-01-01 through 2025-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2025-01-01 through 2025-12-31 (365 days)
- Geographic scope: SEEKONK, MA
- Total crash records analyzed: 678
- Total persons involved: 1,613
- Total vehicles involved: 1,269
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: 2025." Published June 21, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/seekonk/2025-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: 2025-01-01 – 2025-12-31
Generated: June 21, 2026 · All rights reserved