Monthly Traffic Safety Analysis

201 CRASHES IN
FALL RIVER, MA
APRIL 2026

All metrics benchmarked againstApril 2025

In April 2026, FALL RIVER, MA recorded 201 total crashes, a decrease of 9.05% from the 221 crashes reported in April 2025. Total injuries saw a substantial reduction, dropping from 82 in April 2025 to 41 in April 2026, marking a 50% decrease. This significant decline in injuries is the most notable year-over-year shift.

201

-9.0%was 221

Total Crash Events

0

Persons Killed

41

-50.0%was 82

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. 12 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend for crashes in FALL RIVER, MA shows a decrease, with total crashes falling by 9.05% from 221 in April 2025 to 201 in April 2026. This reduction indicates a positive trend in traffic safety for the period.

30

Hit-and-Run Crashes — April 2026

-16.7% vs prior (36)

Hit-and-run crashes decreased by 16.67%, falling from 36 incidents in April 2025 to 30 in April 2026. The hit-and-run rate also saw a slight decline, moving from 16.3% in April 2025 to 14.9% in April 2026, indicating a downward trend.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

41

Motorists Injured

Prior: 81-49.4%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · 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 Tuesday in April 2025 (41 crashes) to Thursday in April 2026 (43 crashes). The peak hour for crashes remained consistent at 3 PM for both periods, though the count increased from 22 crashes in April 2025 to 33 crashes in April 2026.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatalities remained at zero for both April 2025 and April 2026. Injury crashes saw a notable decline, with serious injury crashes decreasing from 4 (1.8% of total) to 2 (1.0% of total), and minor injury crashes dropping from 38 (17.2% of total) to 25 (12.4% of total). Overall, total injuries decreased by 50%, from 82 to 41.

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes1%
-50.0%prior 4
Minor Injury25minor injury crashes12.4%
-34.2%prior 38
Possible Injury3possible injury crashes1.5%
-66.7%prior 9
No Injury159no injury crashes79.1%
2.6%prior 155

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Most severe injury per crash record

Top Contributing Factors

Several contributing factors saw significant changes in crash counts year-over-year. 'No improper driving' increased by 13 crashes (from 53 to 66), while 'Failed to yield right of way' rose by 6 crashes (from 17 to 23). Conversely, 'Followed too closely' decreased by 12 crashes (from 23 to 11), and 'Inattention' decreased by 7 crashes (from 23 to 16).

Officer-Reported Primary Contributing Cause

No improper driving66 (32.8%)24.5%prior 53
Failed to yield right of way23 (11.4%)35.3%prior 17
Failure to keep in proper lane or running off road17 (8.5%)-26.1%prior 23
Inattention16 (8%)-30.4%prior 23
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner13 (6.5%)
Followed too closely11 (5.5%)-52.2%prior 23
Disregarded traffic signs, signals, road markings10 (5%)0.0%prior 10
Other improper action8 (4%)-55.6%prior 18
Distracted4 (2%)
Made an improper turn3 (1.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear' weather conditions increased from 106 in April 2025 to 129 in April 2026, while those in 'Rain' decreased from 17 to 7. Crashes on 'Dry' road surfaces decreased from 180 to 173, and those on 'Wet' surfaces decreased from 39 to 23. For lighting conditions, crashes during 'Daylight' decreased from 172 to 155, and those in 'Dark - lighted roadway' decreased from 37 to 28.

Weather

Clear129 (65.5%)
21.7%prior 106
Clear/Cloudy14 (7.1%)
-50.0%prior 28
Cloudy11 (5.6%)
-31.3%prior 16
Clear/Clear8 (4.1%)
-71.4%prior 28
Rain7 (3.6%)
-58.8%prior 17
Clear/Other6 (3.0%)
Rain/Cloudy5 (2.5%)
Cloudy/Cloudy5 (2.5%)
Cloudy/Rain5 (2.5%)
Clear/Unknown2 (1.0%)
-80.0%prior 10

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Weather condition at time of crash

Lighting

Daylight155 (79.1%)
-9.9%prior 172
Dark - lighted roadway28 (14.3%)
-24.3%prior 37
Dawn6 (3.1%)
Dusk5 (2.6%)
Dark - roadway not lighted2 (1.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Lighting condition field

Road Surface

Dry173 (87.8%)
-3.9%prior 180
Wet23 (11.7%)
-41.0%prior 39
Water (standing, moving)1 (0.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes decreased by 7.55%, from 437 in April 2025 to 404 in April 2026. Among top makes, Honda saw a significant increase in involvement from 30 to 49, while Nissan decreased from 40 to 29 and Chevrolet from 43 to 26. In terms of persons involved, the 45-54 age group saw a decrease from 53 to 30, and the 55-64 age group decreased from 50 to 34.

Top Vehicle Makes (404 vehicles)

1
TOYOTA66 (16.3%)
-9.6%prior 73
2
FORD49 (12.1%)
11.4%prior 44
3
HONDA49 (12.1%)
63.3%prior 30
4
NISSAN29 (7.2%)
-27.5%prior 40
5
CHEVROLET26 (6.4%)
-39.5%prior 43
6
KIA22 (5.4%)
-8.3%prior 24
7
HYUNDAI21 (5.2%)
-22.2%prior 27
8
JEEP14 (3.5%)
16.7%prior 12
9
MAZDA10 (2.5%)
-9.1%prior 11
10
BMW10 (2.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Vehicle unit records

104 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (364 persons with recorded sex)

Male201 (55.2%)
-17.3%prior 243
Female163 (44.8%)
-12.8%prior 187

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes in the 25 mph speed zone remained relatively stable, with 100 crashes in April 2025 and 101 in April 2026. A notable shift occurred in the 55 mph speed zone, where crashes decreased significantly from 16 to 4. There were no fatal crashes reported in any speed zone for either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · 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: 2026-04-01 through 2026-04-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2026-04-01 through 2026-04-30 (30 days)
  • Geographic scope: FALL RIVER, MA
  • Total crash records analyzed: 201
  • Total persons involved: 478
  • Total vehicles involved: 404

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: April 2026." Published June 21, 2026. Reporting period: 2026-04-01 to 2026-04-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/fall-river/april-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

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Fall River, MA Crash Report — April 2026 | ThatCarHitMe.com