ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · FALL RIVER, 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/fall-river/2025-annual-report
Yearly Traffic Safety Analysis
3,030 CRASHES IN
FALL RIVER, MA
2025
In the most recent period, Fall River recorded 3,030 total vehicle crashes, a 1.6% decrease from the 3,080 crashes documented in the same period one year prior. While overall crashes saw a slight decline, the most significant year-over-year change was a 69% reduction in pedestrian-involved crashes, which fell from 84 to 26.
3,030
▼ -1.6%was 3,080
Total Crash Events
3
▼ -25.0%was 4
Persons Killed
850
▼ -20.6%was 1,071
Persons Injured
448
▲ 4.7%was 428
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. 199 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
Overall crash trends in Fall River show a slight year-over-year decline, with total incidents decreasing by 1.6% from 3,080 to 3,030. The severity of these incidents also lessened, as the number of people injured fell by 20.6% from 1,071 to 850, and total fatalities decreased from 4 to 3.
448
Hit-and-Run Crashes — 2025
▲ 4.7% vs prior (428)
The total number of hit-and-run crashes increased by 4.7%, rising from 428 in the prior year to 448 in the current year. This change reflects an upward trend in the hit-and-run rate as a percentage of all crashes, which grew from 13.9% to 14.8% year-over-year.
Vulnerable Road User Casualties
1
Pedestrians Killed
0
Cyclists Killed
2
Motorists Killed
19
Pedestrians Injured
3
Cyclists Injured
828
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 showed a shift in the peak day of the week, moving from Friday (501 crashes) in the prior year to Monday (501 crashes) in the current year, though the peak volume remained identical. The peak hour for collisions was consistent across both periods, remaining at 4 PM. Crashes during this peak hour saw a slight increase from 266 to 274 incidents.
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 fatal crash count decreased from 4 to 3 year-over-year, though the rate as a share of all crashes held steady at 0.1%. There was a notable shift toward less severe outcomes, with the proportion of crashes involving any injury (Serious, Minor, or Possible) decreasing from 24.5% to 19.9%. Consequently, the share of crashes resulting in no injury increased from 69.6% to 73.4% 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 top three contributing factors remained consistent across both periods: 'No improper driving,' 'Inattention,' and 'Failed to yield right of way.' While the count of crashes attributed to 'Inattention' decreased by 10.7% (from 382 to 341), incidents involving a 'Failed to yield right of way' increased by 8.2% (from 232 to 251). Notably, crashes attributed to an 'Operating vehicle in erratic, reckless, careless, negligent or aggressive manner' grew by 31.4%, from 70 to 92 incidents.
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
The majority of crashes in both periods occurred under favorable conditions: during daylight, on dry roads, and in clear weather. The proportion of crashes happening in daylight hours increased slightly from 69.2% to 71.4% year-over-year. Collisions on wet road surfaces became less frequent, accounting for 11.0% of incidents in the current year compared to 13.1% in the prior 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
The top three vehicle makes involved in crashes were unchanged year-over-year: Toyota, Honda, and Ford. In the current period, Chevrolet (449 vehicles) and Nissan (447 vehicles) swapped rankings for the fourth and fifth positions compared to the prior year. Analyzing the demographics of all persons involved, the 26-34 age group was the most frequently represented in both periods, with their involvement remaining relatively stable at 1,153 individuals compared to 1,167 in the prior year.
Top Vehicle Makes (6,001 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records
1,427 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (5,723 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 25 mph speed zone was the location for the highest number of crashes in both periods, with the count of incidents in this zone increasing from 1,219 to 1,447 year-over-year. Crashes in the 55 mph zone also rose from 89 to 112. The location of fatal crashes shifted; the current year saw one fatality in a 25 mph zone and one in a 55 mph zone, whereas the prior year recorded two in the 25 mph zone and one in the 30 mph zone.
Fatal crashes by zone: 25 mph: 1 of 1,447 (0.069%) · 55 mph: 1 of 112 (0.893%)
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: FALL RIVER, MA
- Total crash records analyzed: 3,030
- Total persons involved: 7,315
- Total vehicles involved: 6,001
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). "FALL RIVER, 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/fall-river/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