Monthly Traffic Safety Analysis

42 CRASHES IN
PALMER, MA
OCTOBER 2025

All metrics benchmarked againstOctober 2024

In October 2025, Palmer experienced 42 crashes, marking a significant 90.9% increase from the 22 crashes reported in October 2024. The total number of injuries also rose from 10 to 12. A notable year-over-year shift is the increase in hit-and-run crashes, which grew from 1 to 5.

42

▲ 90.9%was 22

Total Crash Events

0

Persons Killed

12

▲ 20.0%was 10

Persons Injured

5

▲ 400.0%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. 2 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates a substantial increase in crashes year-over-year, with total crashes rising by 90.9% from 22 in October 2024 to 42 in October 2025. Total injuries also increased by 20%, from 10 to 12. Fatalities remained at 0 in both periods.

5

Hit-and-Run Crashes — October 2025

▲ 400.0% vs prior (1)

Hit-and-run crashes increased significantly year-over-year, rising from 1 in October 2024 to 5 in October 2025. This resulted in the hit-and-run rate more than doubling, increasing from 4.5% to 11.9% of all crashes.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 0→0.0%

1

Cyclists Injured

Prior: 0→%

11

Motorists Injured

Prior: 10↑10.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns shifted significantly, with the peak day for crashes moving from Thursday in October 2024 (5 crashes) to Sunday in October 2025 (9 crashes). The peak crash hour also changed from 10 AM (3 crashes) in the prior period to 2 PM (6 crashes) in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While no fatalities occurred in either period, there was a shift in injury severity distribution. Serious injury crashes increased from 0 in October 2024 to 1 in October 2025, while minor injury crashes decreased from 5 to 2. Possible injury crashes increased from 2 to 4, and the proportion of no injury crashes rose from 68.2% to 78.6% of all crashes.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes2.4%
Minor Injury2minor injury crashes4.8%
↓-60.0%prior 5
Possible Injury4possible injury crashes9.5%
↑100.0%prior 2
No Injury33no injury crashes78.6%
↑120.0%prior 15

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Most severe injury per crash record

Top Contributing Factors

Several contributing factors saw notable changes year-over-year. Crashes attributed to 'Inattention' increased from 2 to 9, while 'No improper driving' crashes rose from 3 to 7. Conversely, crashes where 'Followed too closely' was a factor decreased from 4 to 0, and 'Failure to keep in proper lane or running off road' decreased from 4 to 2 crashes.

Officer-Reported Primary Contributing Cause

Inattention9 (21.4%)
No improper driving7 (16.7%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner4 (9.5%)
Exceeded authorized speed limit3 (7.1%)
Visibility obstructed3 (7.1%)
Failed to yield right of way2 (4.8%)
Failure to keep in proper lane or running off road2 (4.8%)
Distracted2 (4.8%)
Other improper action2 (4.8%)
Disregarded traffic signs, signals, road markings1 (2.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

While 'Clear' weather and 'Daylight' conditions remained dominant in both periods, crashes occurring in 'Wet' road surface conditions increased substantially from 1 in October 2024 to 9 in October 2025. Crashes during 'Rain' conditions also increased from 0 to 5. There was an increase in crashes occurring in 'Dark - lighted roadway' conditions from 2 to 4.

Weather

Clear24 (57.1%)
↑41.2%prior 17
Rain5 (11.9%)
Clear/Clear4 (9.5%)
Cloudy3 (7.1%)
Cloudy/Rain3 (7.1%)
Clear/Cloudy2 (4.8%)
Rain/Cloudy1 (2.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Weather condition at time of crash

Lighting

Daylight30 (71.4%)
↑114.3%prior 14
Dark - roadway not lighted5 (11.9%)
→0.0%prior 5
Dark - lighted roadway4 (9.5%)
Dawn2 (4.8%)
Dusk1 (2.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Lighting condition field

Road Surface

Dry33 (78.6%)
↑57.1%prior 21
Wet9 (21.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (73 vehicles)

1
FORD8 (11%)
2
TOYOTA7 (9.6%)
3
HYUNDAI6 (8.2%)
4
SUBARU6 (8.2%)
5
HONDA6 (8.2%)
↓-33.3%prior 9
6
CHEVROLET4 (5.5%)
↓-50.0%prior 8
7
MERCEDES-BENZ4 (5.5%)
8
DODGE3 (4.1%)
9
NISSAN3 (4.1%)
↓-57.1%prior 7
10
VOLVO2 (2.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Vehicle unit records

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

Sex Distribution (81 persons with recorded sex)

Male48 (59.3%)
↑37.1%prior 35
Female33 (40.7%)
↑65.0%prior 20

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 30 mph speed zones saw a significant increase, rising from 8 in October 2024 to 19 in October 2025. Conversely, crashes in 65 mph zones decreased from 6 to 4. Crashes in 35 mph zones increased from 1 to 2.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · 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: 2025-10-01 through 2025-10-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2025-10-01 through 2025-10-31 (31 days)
  • Geographic scope: PALMER, MA
  • Total crash records analyzed: 42
  • Total persons involved: 92
  • Total vehicles involved: 73

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: October 2025." Published June 21, 2026. Reporting period: 2025-10-01 to 2025-10-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/palmer/october-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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Palmer, MA Crash Report — October 2025 | ThatCarHitMe.com