Monthly Traffic Safety Analysis

15 CRASHES IN
WESTMINSTER, MA
MARCH 2026

All metrics benchmarked againstMarch 2025

In March 2026, WESTMINSTER experienced 15 crashes, a 50% increase compared to the 10 crashes reported in March 2025. Fatalities remained at 0 in both periods, while total injuries decreased from 2 to 0. A notable shift includes the appearance of 1 DUI crash and 1 bicycle crash in the current period, neither of which were present in the prior period.

15

50.0%was 10

Total Crash Events

0

Persons Killed

0

-100.0%was 2

Persons Injured

0

Fatal Crash Events

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash incidents in WESTMINSTER show a rising trend, increasing by 50% from 10 crashes in March 2025 to 15 crashes in March 2026. Despite this increase in total crashes, fatalities remained at 0, and total injuries decreased from 2 to 0.

When Crashes Happen

The temporal distribution of crashes shifted year-over-year. In March 2026, the peak day for crashes was Friday with 6 incidents, whereas in March 2025, Thursday was the peak day with 3 incidents. The peak hour also shifted from 8 AM in March 2025 (2 crashes) to 8 PM in March 2026 (2 crashes), though 7 AM and 12 PM also recorded 2 crashes in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Crash time field aggregated by hour (0-23)

Top Contributing Factors

The leading contributing factor in March 2026 was "No improper driving" with 7 crashes, an increase from 2 crashes in March 2025. "Inattention" emerged as a significant factor in the current period with 5 crashes, not being a top factor in the prior period. Factors like "Failed to yield right of way" decreased from 2 crashes in March 2025 to 0 in March 2026, and "Glare" decreased from 1 crash to 0.

Officer-Reported Primary Contributing Cause

No improper driving7 (46.7%)
Inattention5 (33.3%)
Driving too fast for conditions1 (6.7%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (6.7%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway1 (6.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Adverse weather conditions were a factor in 5 crashes in March 2026, including Sleet, hail, and Snow, compared to 1 crash involving Rain/Cloudy conditions in March 2025. Crashes occurring in "Dark - roadway not lighted" conditions increased from 0 in March 2025 to 6 in March 2026. Similarly, adverse road surface conditions such as Ice and Snow were present in 6 crashes in the current period, up from 2 crashes involving Sand, mud, dirt, oil, gravel or Wet conditions in the prior period.

Weather

Clear9 (60.0%)
28.6%prior 7
Sleet, hail (freezing rain or drizzle)2 (13.3%)
Snow2 (13.3%)
Cloudy1 (6.7%)
Sleet, hail (freezing rain or drizzle)/Snow1 (6.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Weather condition at time of crash

Lighting

Daylight8 (53.3%)
0.0%prior 8
Dark - roadway not lighted6 (40.0%)
Dawn1 (6.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Lighting condition field

Road Surface

Dry9 (60.0%)
12.5%prior 8
Ice4 (26.7%)
Snow2 (13.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (20 vehicles)

1
TOYOTA3 (15%)
2
SUBARU3 (15%)
3
JEEP2 (10%)
4
DODGE2 (10%)
5
MAZDA2 (10%)
6
HONDA1 (5%)
7
BMW1 (5%)
8
LEXUS1 (5%)
9
CHEVROLET1 (5%)
10
MITS1 (5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Vehicle unit records

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

Sex Distribution (21 persons with recorded sex)

Male14 (66.7%)
55.6%prior 9
Female7 (33.3%)
0.0%prior 7

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Person-level records linked to crash events

Speed Limit Zones

The distribution of crashes across speed zones changed between the two periods. Crashes in 30 mph zones increased from 3 in March 2025 to 5 in March 2026, and 40 mph zones saw an increase from 1 to 3 crashes. Conversely, crashes in 35 mph zones decreased from 3 to 1. New crash activity was observed in 25 mph (2 crashes) and 45 mph (2 crashes) zones in the current period, which had no crashes in the prior period. All reported crashes in both periods had 0 fatalities.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · 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: 2026-03-01 through 2026-03-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2026-03-01 through 2026-03-31 (31 days)
  • Geographic scope: WESTMINSTER, MA
  • Total crash records analyzed: 15
  • Total persons involved: 23
  • Total vehicles involved: 20

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). "WESTMINSTER, MA Crash Intelligence Report: March 2026." Published June 21, 2026. Reporting period: 2026-03-01 to 2026-03-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/westminster/march-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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