ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · FALL RIVER, MA · JANUARY 2026
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/january-2026-report
Monthly Traffic Safety Analysis
242 CRASHES IN
FALL RIVER, MA
JANUARY 2026
In January 2026, there were 242 total crashes, a 4.72% decrease from the 254 crashes recorded in January 2025. A notable change was the absence of crash fatalities in January 2026, compared to one fatality in January 2025.
242
▼ -4.7%was 254
Total Crash Events
0
▼ -100.0%was 1
Persons Killed
39
▼ -25.0%was 52
Persons Injured
30
▼ -16.7%was 36
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. 9 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, crash data for January 2026 shows a downward trend compared to January 2025. Total crashes decreased by 4.72%, from 254 to 242, while total injuries saw a 25% reduction, falling from 52 to 39. Additionally, there were no fatalities in January 2026, a decrease from one fatality in January 2025.
30
Hit-and-Run Crashes — January 2026
▼ -16.7% vs prior (36)
The number of hit-and-run crashes decreased from 36 in January 2025 to 30 in January 2026. This resulted in a reduction of the hit-and-run rate from 14.2% to 12.4% year-over-year, indicating a downward trend.
Vulnerable Road User Casualties
0
Motorists Killed
39
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
Friday remained the peak day for crashes in both January 2026 and January 2025, though the number of crashes on this day decreased from 53 to 45. The peak crash hour shifted from 2p (25 crashes) in January 2025 to 3p (24 crashes) in January 2026. This indicates a consistent pattern of higher crash frequency towards the end of the work week and during afternoon commuting hours, albeit with slightly reduced numbers.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Fatal crashes decreased from one in January 2025 to zero in January 2026. The proportion of minor injury crashes decreased from 14.2% (36 crashes) to 10.3% (25 crashes) year-over-year, while possible injury crashes also saw a reduction from 3.5% (9 crashes) to 1.7% (4 crashes). Conversely, crashes resulting in no injury increased in proportion from 76.4% to 84.3% of all crashes.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Most severe injury per crash record
Top Contributing Factors
The top three contributing factors remained consistent year-over-year: 'No improper driving,' 'Inattention,' and 'Failed to yield right of way.' While crashes attributed to 'No improper driving' decreased by 10, from 94 to 84, 'Inattention' increased by 10 crashes, from 23 to 33. 'Failed to yield right of way' also saw a slight increase from 21 to 22 crashes.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
There was a notable shift in road conditions, with crashes on dry surfaces decreasing from 209 to 148, while crashes on snowy surfaces significantly increased from 18 to 58. Correspondingly, crashes during clear weather decreased from 157 to 133, and crashes during snow increased from 9 to 32. Crashes occurring in 'Dark - lighted roadway' conditions also increased from 57 to 67.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Road surface condition field
Vehicles & Demographics
Toyota remained the most frequently involved vehicle make, with its count slightly decreasing from 70 to 69. Ford showed a notable increase in involvement, rising from 53 to 64 vehicles and moving into the second position. Conversely, Nissan saw a significant drop in its involvement count, decreasing from 54 to 25.
Top Vehicle Makes (492 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Vehicle unit records
106 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (449 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 25 mph zones increased from 116 in January 2025 to 122 in January 2026, and this zone was associated with the single fatality in the prior period, while no fatalities occurred in any zone in the current period. Conversely, crashes in 30 mph zones decreased from 83 to 55. There was also an increase in crashes in 65 mph zones, rising from 6 to 13.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · 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: 2026-01-01 through 2026-01-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2026-01-01 through 2026-01-31 (31 days)
- Geographic scope: FALL RIVER, MA
- Total crash records analyzed: 242
- Total persons involved: 575
- Total vehicles involved: 492
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: January 2026." Published June 21, 2026. Reporting period: 2026-01-01 to 2026-01-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/fall-river/january-2026-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: 2026-01-01 – 2026-01-31
Generated: June 21, 2026 · All rights reserved