Monthly Traffic Safety Analysis

38 CRASHES IN
LUNENBURG, MA
DECEMBER 2025

All metrics benchmarked againstDecember 2024

In December 2025, LUNENBURG, MA experienced 38 total crashes, a significant increase of 123.5% compared to the 17 crashes reported in December 2024. The most notable year-over-year shift was the more than doubling of total crashes, accompanied by an increase in total injuries from 4 to 6, while fatalities remained at zero in both periods.

38

123.5%was 17

Total Crash Events

0

Persons Killed

6

50.0%was 4

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

Trend Summary

The overall trend indicates a substantial increase in crash activity year-over-year, with total crashes rising from 17 in December 2024 to 38 in December 2025. This represents a 123.5% increase in crash volume for the month.

2

Hit-and-Run Crashes — December 2025

5.3% hit-and-run rate this period vs 0.0% prior. Prior period: 0.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

6

Motorists Injured

Prior: 450.0%

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

When Crashes Happen

The temporal distribution of crashes shifted year-over-year. The peak day for crashes moved from Wednesday with 4 incidents in December 2024 to Tuesday with 11 incidents in December 2025, while the peak hour shifted from 9 AM with 4 crashes in the prior period to 3 PM with 4 crashes in the current period.

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

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

Crash Severity Breakdown

Fatalities remained at 0 in both December 2024 and December 2025. Total injuries increased from 4 in the prior period to 6 in the current period, while serious injuries decreased from 2 to 0. The proportion of crashes resulting in no injury increased from 82.4% in December 2024 to 92.1% in December 2025.

Outcome by Severity (Crash Events)

Minor Injury3minor injury crashes7.9%
No Injury35no injury crashes92.1%
150.0%prior 14

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

Severity Distribution (Crash Events)

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

Top Contributing Factors

The top contributing factor, 'No improper driving,' increased in count from 9 to 14 crashes, though its share of total crashes decreased from 52.9% to 36.8%. 'Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway' saw a substantial count increase from 1 to 6 crashes, and 'Failed to yield right of way' also increased from 1 to 5 crashes. New factors appearing in December 2025, such as 'Glare' and 'Driving too fast for conditions,' each accounted for 1 crash.

Officer-Reported Primary Contributing Cause

No improper driving14 (36.8%)55.6%prior 9
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway6 (15.8%)
Failed to yield right of way5 (13.2%)
Inattention2 (5.3%)
Other improper action2 (5.3%)
Glare1 (2.6%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (2.6%)
Driving too fast for conditions1 (2.6%)
Followed too closely1 (2.6%)
Distracted1 (2.6%)

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

Road & Environmental Conditions

Crashes occurring under 'Clear' weather conditions increased from 9 to 19, while those in 'Snow' conditions rose from 2 to 6. Crashes on 'Dry' road surfaces increased from 8 to 17, and those on 'Snow' surfaces increased from 3 to 9. For lighting conditions, crashes in 'Daylight' increased from 9 to 19, and crashes in 'Dark - roadway not lighted' increased from 6 to 9, indicating a general increase across various conditions consistent with the overall rise in crash volume.

Weather

Clear19 (50.0%)
111.1%prior 9
Snow6 (15.8%)
Snow/Sleet, hail (freezing rain or drizzle)5 (13.2%)
Rain2 (5.3%)
Clear/Cloudy2 (5.3%)
Cloudy1 (2.6%)
Cloudy/Other1 (2.6%)
Other1 (2.6%)
Clear/Snow1 (2.6%)

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

Lighting

Daylight19 (50.0%)
111.1%prior 9
Dark - roadway not lighted9 (23.7%)
50.0%prior 6
Dark - lighted roadway6 (15.8%)
Dawn2 (5.3%)
Dusk2 (5.3%)

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

Road Surface

Dry17 (44.7%)
112.5%prior 8
Snow9 (23.7%)
Ice6 (15.8%)
Wet5 (13.2%)
Other1 (2.6%)

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

Vehicles & Demographics

Top Vehicle Makes (61 vehicles)

1
TOYOTA11 (18%)
2
HONDA9 (14.8%)
3
FORD7 (11.5%)
4
NISSAN4 (6.6%)
5
CHEVROLET4 (6.6%)
6
VOLVO3 (4.9%)
7
BUIC3 (4.9%)
8
ACURA2 (3.3%)
9
GMC2 (3.3%)
10
HYUNDAI2 (3.3%)

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

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

Sex Distribution (69 persons with recorded sex)

Male41 (59.4%)
141.2%prior 17
Female28 (40.6%)
180.0%prior 10

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

Speed Limit Zones

Crashes in 30 mph zones increased from 8 to 17, and those in 40 mph zones saw a significant rise from 1 to 11 crashes. Conversely, crashes in 10 mph zones decreased from 2 to 1, and in 45 mph zones from 4 to 2. Fatal crash rates remained at 0 for all reported speed zones in both periods.

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

Data Coverage

  • Reporting period: 2025-12-01 through 2025-12-31 (31 days)
  • Geographic scope: LUNENBURG, MA
  • Total crash records analyzed: 38
  • Total persons involved: 77
  • Total vehicles involved: 61

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). "LUNENBURG, MA Crash Intelligence Report: December 2025." Published June 21, 2026. Reporting period: 2025-12-01 to 2025-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/lunenburg/december-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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Lunenburg, MA Crash Report — December 2025 | ThatCarHitMe.com