Monthly Traffic Safety Analysis

26 CRASHES IN
GLOUCESTER, MA
APRIL 2026

All metrics benchmarked againstApril 2025

In April 2026, Gloucester recorded 26 total crashes, a 50% decrease compared to the 52 crashes in April 2025. Total injuries also saw a significant reduction, dropping from 13 to 6 over the same period. The most notable year-over-year shift was a 100% decrease in DUI, speeding, and bicycle-involved crashes, each falling from a non-zero count to zero.

26

-50.0%was 52

Total Crash Events

0

Persons Killed

6

-53.8%was 13

Persons Injured

1

-80.0%was 5

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. 2 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 indicates a substantial decrease in crash activity in Gloucester, with total crashes falling from 52 in April 2025 to 26 in April 2026. This represents a 50% reduction in crashes year-over-year. Concurrently, total injuries decreased by 53.8%, from 13 to 6.

1

Hit-and-Run Crashes — April 2026

-80.0% vs prior (5)

Hit-and-run crashes decreased significantly by 80% year-over-year, falling from 5 incidents in April 2025 to 1 in April 2026. The hit-and-run rate also decreased from 9.6% of total crashes in the prior period to 3.8% in the current period.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

6

Motorists Injured

Prior: 10-40.0%

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

The peak day for crashes shifted from Saturday in April 2025 (11 crashes) to Friday in April 2026 (10 crashes). The peak hour also changed, moving from 5 PM with 5 crashes in the prior period to 2 PM with 5 crashes in the current period. Overall, crash counts on most days of the week and hours of the day decreased year-over-year.

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

There were no fatal crashes or fatalities reported in either April 2025 or April 2026. Minor injuries decreased by 66.7%, from 6 to 2, while possible injuries also decreased by 66.7%, from 6 to 2. The proportion of crashes resulting in no injury increased from 65.4% in April 2025 to 76.9% in April 2026.

Outcome by Severity (Crash Events)

Minor Injury2minor injury crashes7.7%
-66.7%prior 6
Possible Injury2possible injury crashes7.7%
-66.7%prior 6
No Injury20no injury crashes76.9%
-41.2%prior 34

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

The count of 'No improper driving' as a contributing factor decreased by 25%, from 12 crashes in April 2025 to 9 crashes in April 2026, though its share of total crashes increased from 23.1% to 34.6%. 'Inattention' decreased by 12.5% in count, from 8 to 7 crashes, while its share rose from 15.4% to 26.9%. 'Operating vehicle in erratic, reckless, careless, negligent or aggressive manner' saw a 75% decrease in count, from 4 crashes to 1.

Officer-Reported Primary Contributing Cause

No improper driving9 (34.6%)-25.0%prior 12
Inattention7 (26.9%)-12.5%prior 8
Failure to keep in proper lane or running off road2 (7.7%)
Illness1 (3.8%)
Failed to yield right of way1 (3.8%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (3.8%)

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 decreased by 56.25%, from 32 in April 2025 to 14 in April 2026. Crashes during 'Daylight' conditions also decreased by 47.4%, from 38 to 20. Similarly, crashes on 'Dry' road surfaces decreased by 39.5%, from 38 to 23, and on 'Wet' road surfaces by 78.6%, from 14 to 3.

Weather

Clear14 (53.8%)
-56.3%prior 32
Clear/Other3 (11.5%)
Clear/Cloudy2 (7.7%)
Cloudy/Clear1 (3.8%)
Cloudy/Other1 (3.8%)
Cloudy/Rain1 (3.8%)
Cloudy/Unknown1 (3.8%)
Rain1 (3.8%)
-85.7%prior 7
Clear/Unknown1 (3.8%)
Cloudy1 (3.8%)

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

Lighting

Daylight20 (76.9%)
-47.4%prior 38
Dark - lighted roadway3 (11.5%)
-72.7%prior 11
Dark - roadway not lighted1 (3.8%)
Dark - unknown roadway lighting1 (3.8%)
Dawn1 (3.8%)

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

Road Surface

Dry23 (88.5%)
-39.5%prior 38
Wet3 (11.5%)
-78.6%prior 14

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 (45 vehicles)

1
TOYOTA6 (13.3%)
-40.0%prior 10
2
FORD6 (13.3%)
-40.0%prior 10
3
HONDA5 (11.1%)
-61.5%prior 13
4
MAZDA3 (6.7%)
5
CHEVROLET3 (6.7%)
-57.1%prior 7
6
JEEP3 (6.7%)
-40.0%prior 5
7
HYUNDAI2 (4.4%)
8
NISSAN2 (4.4%)
-60.0%prior 5
9
GMC2 (4.4%)
10
MERCEDES-BENZ2 (4.4%)

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

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

Sex Distribution (40 persons with recorded sex)

Male24 (60.0%)
-50.0%prior 48
Female16 (40.0%)
-56.8%prior 37

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 occurring in 25 mph speed zones decreased by 54.5%, from 22 in April 2025 to 10 in April 2026. No fatal crashes were reported in any speed zone during either period. The number of crashes reported in speed zones where data was available decreased from 33 to 18 year-over-year.

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: GLOUCESTER, MA
  • Total crash records analyzed: 26
  • Total persons involved: 50
  • Total vehicles involved: 45

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). "GLOUCESTER, 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/gloucester/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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Gloucester, MA Crash Report — April 2026 | ThatCarHitMe.com