Monthly Traffic Safety Analysis

11 CRASHES IN
MILLIS, MA
JANUARY 2026

All metrics benchmarked againstJanuary 2025

In January 2026, MILLIS experienced 11 total crashes, a 37.5% increase from the 8 crashes reported in January 2025. Fatalities remained at zero in both periods, while injuries decreased from 2 to 0. A notable shift occurred in contributing factors, with 'No improper driving' becoming the most frequent factor in 2026.

11

37.5%was 8

Total Crash Events

0

Persons Killed

0

-100.0%was 2

Persons Injured

0

-100.0%was 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. 11 crashes with unreported severity are not shown in the severity breakdown.

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

Trend Summary

Overall, the trend indicates an increase in total crashes year-over-year, with a 37.5% rise from 8 crashes in January 2025 to 11 crashes in January 2026. Despite this increase in crash volume, both total fatalities and total injuries decreased, with fatalities remaining at 0 and injuries falling from 2 to 0.

When Crashes Happen

The temporal pattern for peak crash day remained consistent, with Monday accounting for 3 crashes in both January 2025 and January 2026. However, the peak hour for crashes shifted from 10p with 1 crash in January 2025 to 4p with 2 crashes in January 2026, indicating a change in the highest frequency time of day for incidents.

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

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

Top Contributing Factors

The most frequent contributing factor shifted from 'Exceeded authorized speed limit' (2 crashes, 25% share) in January 2025 to 'No improper driving' (5 crashes, 45.5% share) in January 2026. Crashes attributed to 'No improper driving' saw a substantial increase from 1 in January 2025 to 5 in January 2026. Factors like 'Driving too fast for conditions', 'Distracted', and 'Failed to yield right of way' each maintained 1 crash count across both periods, while 'Exceeded authorized speed limit' (2 crashes in prior period) was not present in the current period, and 'Inattention' and 'Failure to keep in proper lane or running off road' (1 crash each) appeared as new factors in the current period.

Officer-Reported Primary Contributing Cause

No improper driving5 (45.5%)
Driving too fast for conditions1 (9.1%)
Failed to yield right of way1 (9.1%)
Distracted1 (9.1%)
Inattention1 (9.1%)
Failure to keep in proper lane or running off road1 (9.1%)

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

Road & Environmental Conditions

Crashes occurring in 'Daylight' conditions significantly increased from 3 in January 2025 to 9 in January 2026, while those in 'Dark - lighted roadway' conditions decreased from 3 to 1. The number of crashes in 'Clear' weather conditions rose from 5 to 6, and 'Snow' conditions saw a decrease from 2 crashes to 1. Regarding road surface, 'Dry' conditions accounted for 7 crashes in January 2026, up from 6 in January 2025, and 'Ice' and 'Wet' conditions each appeared with 1 crash in January 2026, neither being present in January 2025.

Weather

Clear6 (54.5%)
20.0%prior 5
Clear/Cloudy1 (9.1%)
Rain/Cloudy1 (9.1%)
Sleet, hail (freezing rain or drizzle)1 (9.1%)
Snow1 (9.1%)
Snow/Sleet, hail (freezing rain or drizzle)1 (9.1%)

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

Lighting

Daylight9 (81.8%)
Dark - lighted roadway1 (9.1%)
Dark - roadway not lighted1 (9.1%)

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

Road Surface

Dry7 (63.6%)
16.7%prior 6
Snow2 (18.2%)
Ice1 (9.1%)
Wet1 (9.1%)

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

Vehicles & Demographics

Top Vehicle Makes (20 vehicles)

1
TOYOTA4 (20%)
2
FORD3 (15%)
3
JEEP2 (10%)
4
MERCEDES-BENZ2 (10%)
5
HONDA2 (10%)
6
MACK1 (5%)
7
RAM1 (5%)
8
KIA1 (5%)
9
BMW1 (5%)
10
HYUNDAI1 (5%)

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

Sex Distribution (22 persons with recorded sex)

Male16 (72.7%)
166.7%prior 6
Female6 (27.3%)
-25.0%prior 8

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

Speed Limit Zones

Crashes occurring in the 30 mph speed zone slightly increased from 6 in January 2025 to 7 in January 2026, while those in the 35 mph zone saw a rise from 1 to 3. The 25 mph speed zone, which had 1 crash in January 2025, did not record any crashes in January 2026. Conversely, the 40 mph speed zone, which had no crashes in January 2025, recorded 1 crash in January 2026, and no fatal crashes were reported in any speed zone during either period.

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

Data Coverage

  • Reporting period: 2026-01-01 through 2026-01-31 (31 days)
  • Geographic scope: MILLIS, MA
  • Total crash records analyzed: 11
  • Total persons involved: 22
  • 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). "MILLIS, MA Crash Intelligence Report: January 2026." Published June 21, 2026. Reporting period: 2026-01-01 to 2026-01-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/millis/january-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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Millis, MA Crash Report — January 2026 | ThatCarHitMe.com