Monthly Traffic Safety Analysis

83 CRASHES IN
LEOMINSTER, MA
JUNE 2025

All metrics benchmarked againstJune 2024

Total crashes in June 2025 were 83, a decrease of 17.82% compared to the 101 crashes recorded in June 2024. Fatalities remained at zero in both periods, while total injuries decreased by 8.57% from 35 to 32. A notable shift was the 200% increase in bicycle crashes, rising from 1 to 3. This indicates an overall reduction in crash frequency, but with a specific increase in bicycle-involved incidents.

83

-17.8%was 101

Total Crash Events

0

Persons Killed

32

-8.6%was 35

Persons Injured

8

14.3%was 7

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. 1 crash with unreported severity is not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates a decrease in crash incidents, with total crashes falling by 17.82% from 101 in June 2024 to 83 in June 2025. Total injuries also decreased by 8.57%, from 35 to 32, while fatalities remained at zero in both periods. This suggests a general improvement in traffic safety metrics year-over-year.

8

Hit-and-Run Crashes — June 2025

14.3% vs prior (7)

The number of hit-and-run crashes increased from 7 in June 2024 to 8 in June 2025. Concurrently, the hit-and-run rate rose from 6.9% of all crashes to 9.6%. This indicates an upward trend in the proportion of crashes involving a hit-and-run incident.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

1

Pedestrians Injured

Prior: 0%

3

Cyclists Injured

Prior: 1200.0%

28

Motorists Injured

Prior: 32-12.5%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes remained Tuesday in both periods, with 18 crashes in June 2025 and 17 in June 2024. The peak hour for crashes also remained 1 PM, although the count decreased from 13 crashes in June 2024 to 9 crashes in June 2025. Significant decreases in crash counts were observed on Thursday (from 16 to 7) and Saturday (from 15 to 7).

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatal crashes remained at zero in both June 2024 and June 2025. Serious injuries (Severity A) decreased from 2 in June 2024 to 0 in June 2025. Minor injuries (Severity B) increased from 13 to 17, while possible injuries (Severity C) decreased from 9 to 6.

Outcome by Severity (Crash Events)

Minor Injury17minor injury crashes20.5%
30.8%prior 13
Possible Injury6possible injury crashes7.2%
-33.3%prior 9
No Injury59no injury crashes71.1%
-23.4%prior 77

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Most severe injury per crash record

Top Contributing Factors

Inattention remained the most frequent contributing factor, increasing slightly from 34 crashes in June 2024 to 35 crashes in June 2025. Failed to yield right of way saw a decrease in count from 11 to 7 crashes, and Followed too closely decreased from 11 to 10 crashes. The share of crashes attributed to Inattention increased from 33.7% to 42.2% year-over-year.

Officer-Reported Primary Contributing Cause

Inattention35 (42.2%)2.9%prior 34
No improper driving13 (15.7%)8.3%prior 12
Followed too closely10 (12%)-9.1%prior 11
Failed to yield right of way7 (8.4%)-36.4%prior 11
Failure to keep in proper lane or running off road5 (6%)
Other improper action4 (4.8%)
Disregarded traffic signs, signals, road markings2 (2.4%)
Distracted2 (2.4%)
Fatigued/asleep1 (1.2%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (1.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in Clear weather conditions decreased from 87 to 66, and those on Dry road surfaces decreased from 93 to 78. Conversely, crashes in Dark - lighted roadway conditions increased from 8 in June 2024 to 11 in June 2025. Overall, there was a reduction in crashes under optimal weather and road surface conditions.

Weather

Clear66 (79.5%)
-24.1%prior 87
Clear/Clear5 (6.0%)
Cloudy5 (6.0%)
-44.4%prior 9
Cloudy/Rain3 (3.6%)
Rain/Rain1 (1.2%)
Clear/Other1 (1.2%)
Cloudy/Fog, smog, smoke1 (1.2%)
Rain1 (1.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Weather condition at time of crash

Lighting

Daylight69 (83.1%)
-21.6%prior 88
Dark - lighted roadway11 (13.3%)
37.5%prior 8
Dusk2 (2.4%)
Dawn1 (1.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Lighting condition field

Road Surface

Dry78 (94.0%)
-16.1%prior 93
Wet5 (6.0%)
-37.5%prior 8

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes decreased from 195 to 158 year-over-year. Toyota remained the top vehicle make involved, increasing from 25 to 26, while Ford saw a significant decrease from 24 to 13. The 45-54 age group experienced the largest decrease in persons involved, dropping from 34 to 17, whereas the 65+ age group saw an increase from 34 to 39.

Top Vehicle Makes (158 vehicles)

1
TOYOTA26 (16.5%)
4.0%prior 25
2
HONDA19 (12%)
46.2%prior 13
3
CHEVROLET15 (9.5%)
7.1%prior 14
4
NISSAN13 (8.2%)
-31.6%prior 19
5
FORD13 (8.2%)
-45.8%prior 24
6
HYUNDAI11 (7%)
37.5%prior 8
7
JEEP11 (7%)
-15.4%prior 13
8
SUBARU9 (5.7%)
28.6%prior 7
9
RAM4 (2.5%)
10
GMC3 (1.9%)
-62.5%prior 8

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Vehicle unit records

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

Sex Distribution (195 persons with recorded sex)

Male98 (50.3%)
-26.3%prior 133
Female97 (49.7%)
-11.8%prior 110

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 30 mph zones decreased from 40 to 30, and in 35 mph zones from 29 to 22. There were no fatal crashes reported in any speed zone during either period. Crashes in 20 mph zones appeared in June 2025 with 4 incidents, while 5 mph and 65 mph zones, each with 1 crash in June 2024, had no reported crashes in June 2025.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · 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: 2025-06-01 through 2025-06-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2025-06-01 through 2025-06-30 (30 days)
  • Geographic scope: LEOMINSTER, MA
  • Total crash records analyzed: 83
  • Total persons involved: 208
  • Total vehicles involved: 158

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). "LEOMINSTER, MA Crash Intelligence Report: June 2025." Published June 21, 2026. Reporting period: 2025-06-01 to 2025-06-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/leominster/june-2025-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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Leominster, MA Crash Report — June 2025 | ThatCarHitMe.com