Monthly Traffic Safety Analysis

50 CRASHES IN
PALMER, MA
JANUARY 2026

All metrics benchmarked againstJanuary 2025

Total crashes in PALMER increased from 32 in January 2025 to 50 in January 2026, representing a 56.25% rise. The most notable shift was a significant increase in crashes occurring on snow-covered road surfaces, which rose from 3 to 18 crashes year-over-year.

50

56.3%was 32

Total Crash Events

0

Persons Killed

9

-10.0%was 10

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 · 2026-01-01 to 2026-01-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash incidents in PALMER showed a significant upward trend year-over-year. Total crashes increased by 18, rising from 32 in January 2025 to 50 in January 2026, marking a 56.25% increase.

1

Hit-and-Run Crashes — January 2026

0.0% vs prior (1)

The number of hit-and-run crashes remained constant at 1 in both January 2025 and January 2026. Due to the overall increase in total crashes, the hit-and-run rate decreased from 3.1% in the prior period to 2% in the current period.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

9

Motorists Injured

Prior: 90.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · 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 Thursday in January 2025 (9 crashes) to Saturday in January 2026 (15 crashes). Crashes on Saturday saw a substantial increase from 4 to 15, while Wednesday crashes also rose significantly from 2 to 9. The peak hour for crashes remained consistently at 2 PM, with 6 crashes reported during this hour in both periods.

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)

Crash Severity Breakdown

Fatalities remained at zero for both January 2025 and January 2026. The total number of injured persons slightly decreased from 10 to 9. The proportion of crashes resulting in any injury (serious, minor, or possible) decreased from 21.875% (7 out of 32 crashes) in the prior year to 16% (8 out of 50 crashes) in the current year.

Outcome by Severity (Crash Events)

Minor Injury6minor injury crashes12%
20.0%prior 5
Possible Injury2possible injury crashes4%
100.0%prior 1
No Injury42no injury crashes84%
75.0%prior 24

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Most severe injury per crash record

Top Contributing Factors

The contributing factor 'No improper driving' increased by 4 crashes, from 9 in January 2025 to 13 in January 2026. 'Followed too closely' saw a substantial increase of 6 crashes, rising from 1 to 7. Similarly, 'Driving too fast for conditions' increased by 5 crashes, from 1 to 6. The factor 'Inattention' remained consistent with 6 crashes in both periods.

Officer-Reported Primary Contributing Cause

No improper driving13 (26%)44.4%prior 9
Followed too closely7 (14%)
Driving too fast for conditions6 (12%)
Inattention6 (12%)0.0%prior 6
Failure to keep in proper lane or running off road4 (8%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner3 (6%)
Failed to yield right of way2 (4%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway2 (4%)
Over-correcting/over-steering2 (4%)
Exceeded authorized speed limit1 (2%)

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 on snow-covered road surfaces significantly increased from 3 in January 2025 to 18 in January 2026. Conversely, crashes on dry road surfaces decreased from 20 to 17. The proportion of crashes occurring on adverse road conditions (snow, wet, ice, slush) rose from 37.5% (12 out of 32 crashes) in the prior period to 66% (33 out of 50 crashes) in the current period.

Weather

Clear21 (42.0%)
-8.7%prior 23
Snow6 (12.0%)
Snow/Snow5 (10.0%)
Cloudy/Snow5 (10.0%)
Cloudy4 (8.0%)
Clear/Clear3 (6.0%)
Rain/Fog, smog, smoke1 (2.0%)
Sleet, hail (freezing rain or drizzle)1 (2.0%)
Clear/Rain1 (2.0%)
Snow/Blowing sand, snow1 (2.0%)

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

Lighting

Daylight31 (62.0%)
72.2%prior 18
Dark - roadway not lighted8 (16.0%)
Dark - lighted roadway5 (10.0%)
-37.5%prior 8
Dawn3 (6.0%)
Dusk3 (6.0%)

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

Road Surface

Snow18 (36.0%)
Dry17 (34.0%)
-15.0%prior 20
Wet11 (22.0%)
120.0%prior 5
Ice3 (6.0%)
Slush1 (2.0%)

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

Vehicles & Demographics

The total number of vehicles involved in crashes increased from 50 to 79 year-over-year. Among top makes, TOYOTA saw a notable increase in involvement from 6 vehicles to 16 vehicles, and HYUNDAI involvement rose from 1 vehicle to 8 vehicles. The 26-34 age group experienced the largest increase in persons involved, rising from 9 to 26.

Top Vehicle Makes (79 vehicles)

1
TOYOTA16 (20.3%)
166.7%prior 6
2
CHEVROLET8 (10.1%)
33.3%prior 6
3
HYUNDAI8 (10.1%)
4
JEEP8 (10.1%)
5
NISSAN5 (6.3%)
0.0%prior 5
6
FORD5 (6.3%)
7
HONDA5 (6.3%)
8
SUBARU3 (3.8%)
9
ACURA2 (2.5%)
10
INFI2 (2.5%)

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

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

Sex Distribution (97 persons with recorded sex)

Male58 (59.8%)
75.8%prior 33
Female39 (40.2%)
69.6%prior 23

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

There were no fatal crashes in any speed zone during either period. Crashes in 30 MPH zones increased from 7 in January 2025 to 18 in January 2026, and crashes in 65 MPH zones increased from 4 to 10. Conversely, crashes in 10 MPH zones decreased from 3 to 2, and crashes in 40 MPH zones decreased from 6 to 5.

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: PALMER, MA
  • Total crash records analyzed: 50
  • Total persons involved: 100
  • Total vehicles involved: 79

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). "PALMER, 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/palmer/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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Palmer, MA Crash Report — January 2026 | ThatCarHitMe.com