ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · FREETOWN, 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/freetown/2025-annual-report
Yearly Traffic Safety Analysis
232 CRASHES IN
FREETOWN, MA
2025
In Freetown, total crashes decreased by 7.9% from 252 in 2024 to 232 in 2025. Despite this overall reduction in incidents and a 6.5% drop in injuries, the number of fatalities rose from 2 to 3. The most notable shift was a significant increase in the severity of crashes, with serious injury incidents more than doubling from 3 to 8.
232
▼ -7.9%was 252
Total Crash Events
3
▲ 50.0%was 2
Persons Killed
86
▼ -6.5%was 92
Persons Injured
10
▲ 42.9%was 7
Hit-and-Run Crashes
Note: "Persons Killed" (3) 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. 8 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
The overall trend shows a decrease in total traffic incidents, with crashes falling by 7.9% from 252 to 232 year-over-year. The number of people injured also declined by 6.5%, from 92 to 86. However, this downward trend did not extend to the most severe outcomes, as the number of fatalities increased from 2 in the prior year to 3 in the current year.
10
Hit-and-Run Crashes — 2025
▲ 42.9% vs prior (7)
Hit-and-run incidents trended upward year-over-year. The total count of hit-and-run crashes increased by 42.9%, from 7 in the prior period to 10 in the current period. The hit-and-run rate, representing the percentage of all crashes that were hit-and-runs, also rose from 2.8% to 4.3%.
Vulnerable Road User Casualties
0
Cyclists Killed
3
Motorists Killed
2
Cyclists Injured
84
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 pattern of crashes shifted between the two periods. The day with the highest number of incidents moved from Tuesday (56 crashes) in the prior year to Friday (42 crashes) in the current year. The peak hour for crashes, however, remained consistent at 4 p.m. in both periods, with 19 incidents recorded during that hour in each 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
While total crashes decreased, the severity of crashes that did occur worsened year-over-year. The number of fatal crashes increased from 2 to 3, and the corresponding fatal crash rate rose from 0.79 to 1.29 per 100 crashes. Similarly, the count of serious injury crashes more than doubled from 3 to 8, and their share of all crashes grew from 1.2% to 3.4%.
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 leading contributing factors saw some shifts year-over-year. While "No improper driving" remained the most common finding in both periods (71 vs. 69 crashes), crashes attributed to "Inattention" saw a notable 35.9% decrease in count, falling from 39 to 25 incidents. This moved "Inattention" from the second-ranked factor to the third. "Failed to yield right of way" became the second-most cited factor, with its count remaining stable at 20 crashes compared to 21 in the prior year.
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 showed a significant shift in lighting. The proportion of crashes occurring in daylight decreased from 64.3% of all incidents in the prior year to 54.3% in the current year. Correspondingly, crashes on dark, unlit roadways increased their share from 24.6% to 33.6%. The distribution of crashes by road surface condition remained relatively stable, with dry roads accounting for over 80% of incidents in both periods.
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
The top vehicle makes involved in crashes changed significantly, with Toyota moving from the fourth most common make (37 vehicles) to the most common (61 vehicles). Ford dropped from first to second place, while Honda's involvement decreased from 46 to 25 vehicles. Among persons involved in crashes, the share of individuals in the 16-20 age group decreased from 15.2% to 12.2%, while the 0-15 age group's share increased from 5.8% to 9.2%.
Top Vehicle Makes (353 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records
31 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (436 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 distribution of crashes across speed zones shifted, with a notable decrease in incidents in 40 mph zones (from 47 to 29) and an increase in 65 mph zones (from 63 to 70). Fatal crashes also occurred in higher speed zones in the current year, with two fatalities in a 65 mph zone and one in a 35 mph zone. This contrasts with the prior year, where one fatal crash was recorded in a 15 mph zone.
Fatal crashes by zone: 35 mph: 1 of 38 (2.632%) · 65 mph: 2 of 70 (2.857%)
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: FREETOWN, MA
- Total crash records analyzed: 232
- Total persons involved: 467
- Total vehicles involved: 353
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). "FREETOWN, 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/freetown/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