Yearly Traffic Safety Analysis

9 CRASHES IN
NEW MARLBOROUGH, MA
2025

All metrics benchmarked against2024

In 2025, New Marlborough recorded 9 total vehicle crashes, a 50% increase from the 6 crashes reported in 2024. While no fatalities were recorded in either period, the number of injuries tripled from 1 in 2024 to 3 in 2025. A notable change was the appearance of crashes involving contributing factors like physical impairment, which were not reported in the prior year's data.

9

50.0%was 6

Total Crash Events

0

Persons Killed

3

200.0%was 1

Persons Injured

2

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 · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend in New Marlborough shows an increase in traffic collisions year-over-year. Total crashes rose by 50%, from 6 in 2024 to 9 in 2025. This increase was accompanied by a rise in injuries, which increased from 1 to 3, while fatalities remained at zero for both years.

2

Hit-and-Run Crashes — 2025

22.2% hit-and-run rate this period vs 0.0% prior. Prior period: 0.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

3

Motorists Injured

Prior: 1200.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes shifted between the two periods. In 2025, the peak day for crashes was Monday with 3 incidents, a change from 2024 when Sunday was the peak day with 2 incidents. Crash distribution throughout the day was sparse in both years with no distinct peak hour emerging. Monthly patterns also changed, with 2025 showing concentrations in March and December, whereas 2024's crashes were more evenly distributed.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity saw a slight increase in 2025 compared to the prior year. While there were no fatal crashes recorded in either period, the number of crashes resulting in minor injuries doubled from 1 in 2024 to 2 in 2025. Consequently, the share of crashes involving minor injuries rose from 16.7% of all collisions in 2024 to 22.2% in 2025.

Outcome by Severity (Crash Events)

Minor Injury2minor injury crashes22.2%
100.0%prior 1
No Injury7no injury crashes77.8%
40.0%prior 5

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factor in both years was 'No improper driving,' with its count increasing from 4 crashes in 2024 to 5 in 2025. A notable change in 2025 was the emergence of 'Physical impairment' as a factor in 2 crashes, a category not reported in the prior year. Additionally, one crash was attributed to 'Operating vehicle in erratic, reckless, careless, negligent or aggressive manner' in 2025, which also did not appear in the 2024 data.

Officer-Reported Primary Contributing Cause

No improper driving5 (55.6%)
Physical impairment2 (22.2%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (11.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes in both periods primarily occurred in daylight on dry roads. However, the proportion of crashes happening in dark, unlighted conditions increased, accounting for 3 of 9 incidents in 2025 compared to 1 of 6 in 2024. While most crashes happened on dry roads in both years, the number of incidents on snowy surfaces decreased from 2 in 2024 to 1 in 2025.

Weather

Clear3 (33.3%)
Clear/Clear2 (22.2%)
Cloudy2 (22.2%)
Clear/Unknown1 (11.1%)
Fog, smog, smoke1 (11.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash

Lighting

Daylight6 (66.7%)
20.0%prior 5
Dark - roadway not lighted3 (33.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field

Road Surface

Dry7 (77.8%)
Snow1 (11.1%)
Wet1 (11.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (13 vehicles)

1
TOYOTA4 (30.8%)
2
CHEVROLET2 (15.4%)
3
LEXUS1 (7.7%)
4
MITSUBISHI1 (7.7%)
5
SUBARU1 (7.7%)
6
FORD1 (7.7%)
7
KIA1 (7.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records

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

Sex Distribution (12 persons with recorded sex)

Female6 (50.0%)
200.0%prior 2
Male6 (50.0%)
50.0%prior 4

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Person-level records linked to crash events

Speed Limit Zones

The distribution of crashes across speed zones shifted notably year-over-year. In 2025, there was a significant increase in crashes within 40 mph zones, rising from 1 incident in 2024 to 5 incidents. Conversely, crashes in 30 mph zones decreased from 3 to 1 over the same period. One crash was recorded in a 45 mph zone in 2025, a speed limit that had no reported crashes in the prior year.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · 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: 2025-01-01 through 2025-12-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2025-01-01 through 2025-12-31 (365 days)
  • Geographic scope: NEW MARLBOROUGH, MA
  • Total crash records analyzed: 9
  • Total persons involved: 15
  • Total vehicles involved: 13

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). "NEW MARLBOROUGH, MA Crash Intelligence Report: 2025." Published June 21, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/new-marlborough/2025-annual-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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New Marlborough, MA Crash Report — 2025 | ThatCarHitMe.com