Monthly Traffic Safety Analysis

12 CRASHES IN
SUTTON, MA
APRIL 2026

All metrics benchmarked againstApril 2025

In April 2026, Sutton experienced 12 total crashes, a notable decrease of 29.4% compared to the 17 crashes recorded in April 2025. Fatalities remained at zero in both periods, while total injuries held steady at 4. The most significant year-over-year shift was the increase in DUI-related crashes, rising from 0 in April 2025 to 4 in April 2026.

12

-29.4%was 17

Total Crash Events

0

Persons Killed

4

Persons Injured

1

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.

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 indicates a decrease in total crashes, with a 29.4% reduction from 17 crashes in April 2025 to 12 crashes in April 2026. Despite this decline in crash volume, the number of total injuries remained stable at 4 in both periods. Fatalities were not reported in either April 2025 or April 2026.

1

Hit-and-Run Crashes — April 2026

8.3% hit-and-run rate this period vs 0.0% prior. Prior period: 0.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

4

Motorists Injured

Prior: 40.0%

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

Temporal patterns shifted year-over-year, with Monday becoming the peak day for crashes in April 2026 with 4 incidents, compared to Saturday's peak of 6 crashes in April 2025. While the peak hour remained 4 p.m. with 2 crashes in both periods, crashes occurring on Saturdays decreased from 6 to 2, and crashes on Thursdays decreased from 2 to 0. Conversely, crashes on Mondays increased from 0 to 4.

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

The severity distribution saw minor and possible injury crashes maintaining the same counts year-over-year, with 2 minor injury crashes and 1 possible injury crash in both periods. Consequently, the share of minor injury crashes increased from 11.8% in April 2025 to 16.7% in April 2026, and possible injury crashes rose from 5.9% to 8.3%. The proportion of no-injury crashes slightly decreased from 76.5% to 75% as total crashes fell.

Outcome by Severity (Crash Events)

Minor Injury2minor injury crashes16.7%
0.0%prior 2
Possible Injury1possible injury crashes8.3%
0.0%prior 1
No Injury9no injury crashes75%
-30.8%prior 13

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

Contributing factors saw notable shifts, with 'Failure to keep in proper lane or running off road' increasing by 3 crashes (from 0 to 3) in April 2026, becoming a top factor with a 25% share. Conversely, 'Other improper action' decreased by 3 crashes (from 3 to 0), and 'Visibility obstructed' decreased by 2 crashes (from 2 to 0). 'No improper driving' and 'Followed too closely' maintained consistent counts of 3 and 2 crashes respectively across both periods.

Officer-Reported Primary Contributing Cause

Failure to keep in proper lane or running off road3 (25%)
No improper driving3 (25%)
Followed too closely2 (16.7%)
Distracted1 (8.3%)
Inattention1 (8.3%)
Failed to yield right of way1 (8.3%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (8.3%)

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

Regarding crash conditions, there was a notable decrease in crashes occurring under adverse weather, with 'Rain' incidents falling from 2 to 0, and 'Snow/Rain' and 'Snow/Sleet, hail' incidents also dropping from 1 to 0 each. Crashes on 'Dry' road surfaces increased by 1 crash (from 10 to 11), while those on 'Wet' surfaces decreased by 5 crashes (from 6 to 1). Crashes during 'Daylight' conditions decreased from 15 to 8, while those in 'Dark - roadway not lighted' conditions increased from 1 to 2.

Weather

Clear6 (50.0%)
-33.3%prior 9
Clear/Clear4 (33.3%)
Cloudy2 (16.7%)

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

Lighting

Daylight8 (66.7%)
-46.7%prior 15
Dark - roadway not lighted2 (16.7%)
Dark - lighted roadway1 (8.3%)
Dark - unknown roadway lighting1 (8.3%)

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

Road Surface

Dry11 (91.7%)
10.0%prior 10
Wet1 (8.3%)
-83.3%prior 6

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

Vehicles & Demographics

Top Vehicle Makes (21 vehicles)

1
TOYOTA5 (23.8%)
0.0%prior 5
2
MAZDA3 (14.3%)
3
BMW3 (14.3%)
4
HONDA2 (9.5%)
5
FORD2 (9.5%)
6
VOLVO1 (4.8%)
7
CHRYSLER1 (4.8%)
8
FREIGHTLINER CO1 (4.8%)
9
JEEP1 (4.8%)
10
SUBARU1 (4.8%)

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

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

Sex Distribution (22 persons with recorded sex)

Male14 (63.6%)
-12.5%prior 16
Female8 (36.4%)
-42.9%prior 14

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 15 mph speed zones decreased by 2 crashes, from 3 in April 2025 to 1 in April 2026, and crashes in 65 mph zones also fell by 2 crashes (from 3 to 1). Conversely, crashes in 50 mph zones increased by 1 crash, from 1 to 2. No fatal crashes were recorded in any speed zone during 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: SUTTON, MA
  • Total crash records analyzed: 12
  • Total persons involved: 24
  • Total vehicles involved: 21

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). "SUTTON, 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/sutton/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

ThatCarHitMe.com · An Injuria.ai Company