ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · SEEKONK, MA · MARCH 2024
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/march-2024-report
Monthly Traffic Safety Analysis
59 CRASHES IN
SEEKONK, MA
MARCH 2024
In March 2024, SEEKONK experienced 59 crashes, a 47.5% increase compared to the 40 crashes recorded in March 2023. The most notable year-over-year shift was a 183.3% increase in total injuries, rising from 6 in the prior period to 17 in the current period. Fatalities remained at zero in both periods.
59
▲ 47.5%was 40
Total Crash Events
0
Persons Killed
17
▲ 183.3%was 6
Persons Injured
2
▼ -50.0%was 4
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. 8 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-03-01 to 2024-03-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, crash activity in SEEKONK shows an increasing trend year-over-year, with total crashes rising by 47.5% from 40 to 59. This increase was accompanied by a significant rise in total injuries, which climbed from 6 to 17. Despite the increase in crashes and injuries, fatalities remained stable at zero for both periods.
2
Hit-and-Run Crashes — March 2024
▼ -50.0% vs prior (4)
Hit-and-run crashes decreased by 50% in count, from 4 incidents in March 2023 to 2 in March 2024. Consequently, the hit-and-run rate decreased from 10% of all crashes in the prior period to 3.4% in the current period. This indicates a downward trend in the proportion of crashes involving a hit-and-run.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Motorists Killed
1
Pedestrians Injured
16
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-03-01 to 2024-03-31 · 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 the prior period, which had 11 crashes, to Thursday in the current period, also with 11 crashes. The peak hour for crashes shifted from 4p with 7 crashes in the prior period to 5p with 6 crashes in the current period. Crash distribution across the week and day saw varied changes, with some hours experiencing more crashes while others saw fewer.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-03-01 to 2024-03-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-03-01 to 2024-03-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Fatalities remained at 0 in both March 2023 and March 2024. Total injuries, however, increased substantially from 6 in the prior period to 17 in the current period. The current period also saw 2 serious injury crashes, accounting for 3.4% of all crashes, a category that had no reported incidents in the prior period.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-03-01 to 2024-03-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-03-01 to 2024-03-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factor, Inattention, saw a 225% increase in count, rising from 8 crashes in the prior period to 26 in the current period, and its share of crashes grew from 20% to 44.1%. Failed to yield right of way decreased by 28.6% in count, from 7 crashes to 5. Driving too fast for conditions increased by 66.7% in count, from 3 crashes to 5 crashes.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-03-01 to 2024-03-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in clear weather conditions increased from 31 to 40, though their proportion of total crashes decreased from 77.5% to 67.8%. Crashes on wet road surfaces increased from 9 to 14, maintaining a similar proportion of total crashes at approximately 23-24%. The proportion of crashes occurring during daylight hours increased from 57.5% in the prior period to 72.9% in the current period.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-03-01 to 2024-03-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-03-01 to 2024-03-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-03-01 to 2024-03-31 · Road surface condition field
Vehicles & Demographics
Toyota remained the most frequently involved vehicle make, with its count increasing from 11 in the prior period to 20 in the current period. Honda also maintained its second position, rising from 8 to 10 vehicles involved. The number of persons involved in crashes increased across all reported age groups, with the 0-15 age group seeing a notable increase from 2 to 14 persons.
Top Vehicle Makes (108 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-03-01 to 2024-03-31 · Vehicle unit records
4 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (143 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-03-01 to 2024-03-31 · Person-level records linked to crash events
Speed Limit Zones
The 40 mph speed zone consistently recorded the highest number of crashes in both periods, with 18 crashes in March 2023 and 18 crashes in March 2024. Crashes in the 35 mph zone more than doubled, increasing from 7 in the prior period to 16 in the current period. No fatalities were recorded in any speed zone during either period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-03-01 to 2024-03-31 · 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: 2024-03-01 through 2024-03-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2024-03-01 through 2024-03-31 (31 days)
- Geographic scope: SEEKONK, MA
- Total crash records analyzed: 59
- Total persons involved: 152
- Total vehicles involved: 108
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: March 2024." Published June 21, 2026. Reporting period: 2024-03-01 to 2024-03-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/seekonk/march-2024-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: 2024-03-01 – 2024-03-31
Generated: June 21, 2026 · All rights reserved