Monthly Traffic Safety Analysis

26 CRASHES IN
LEE, MA
DECEMBER 2025

All metrics benchmarked againstDecember 2024

Total crashes in Lee, MA increased by 8.3% from 24 in December 2024 to 26 in December 2025. The number of injured persons also rose from 3 to 4 year-over-year. A notable shift was the increase in crashes attributed to 'Driving too fast for conditions,' which rose from 0 to 4.

26

8.3%was 24

Total Crash Events

0

Persons Killed

4

33.3%was 3

Persons Injured

4

100.0%was 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. 3 crashes with unreported severity are not shown in the severity breakdown.

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

Overall, crash activity in Lee, MA showed an upward trend, with total crashes increasing by 8.3% from 24 to 26 year-over-year. The number of individuals injured in crashes also rose by 33.3%, from 3 in December 2024 to 4 in December 2025. Fatalities remained at zero in both comparative periods.

4

Hit-and-Run Crashes — December 2025

100.0% vs prior (2)

Hit-and-run crashes increased by 100% year-over-year, rising from 2 incidents in December 2024 to 4 in December 2025. This increase led to the hit-and-run rate rising from 8.3% to 15.4% of all crashes.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

4

Motorists Injured

Prior: 2100.0%

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 peak crash days shifted, with December 2024 having peaks on Tuesday and Saturday (7 crashes each), while December 2025 saw peaks on Wednesday and Saturday (6 crashes each). The peak crash hour moved from 5 PM (4 crashes) in the prior period to 4 PM (3 crashes) in the current period. Crashes on Wednesday significantly increased from 1 to 6, and on Friday from 2 to 4, while crashes on Tuesday decreased from 7 to 4.

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

The number of minor injury crashes remained stable at 1 in both December 2024 and December 2025. Possible injury crashes decreased from 2 in the prior period to 1 in the current period. Crashes resulting in no injuries saw a slight increase from 20 to 21 year-over-year. No fatal crashes or fatalities were recorded in either period.

Outcome by Severity (Crash Events)

Minor Injury1minor injury crashes3.8%
0.0%prior 1
Possible Injury1possible injury crashes3.8%
-50.0%prior 2
No Injury21no injury crashes80.8%
5.0%prior 20

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

Crashes attributed to 'Driving too fast for conditions' increased significantly from 0 in December 2024 to 4 in December 2025. 'Failed to yield right of way' crashes doubled from 2 to 4, while 'Inattention' crashes decreased from 4 to 2. 'Followed too closely' also saw a notable decrease in count, falling from 4 to 1 crash.

Officer-Reported Primary Contributing Cause

No improper driving6 (23.1%)-14.3%prior 7
Driving too fast for conditions4 (15.4%)
Failed to yield right of way4 (15.4%)
Failure to keep in proper lane or running off road3 (11.5%)
Inattention2 (7.7%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway1 (3.8%)
Disregarded traffic signs, signals, road markings1 (3.8%)
Fatigued/asleep1 (3.8%)
Followed too closely1 (3.8%)
Other improper action1 (3.8%)

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 in snow conditions significantly increased from 2 in December 2024 to 9 in December 2025, while dry road surface crashes decreased from 14 to 9. Clear weather conditions saw a decrease in associated crashes, from 10 to 7. Crashes occurring in 'Dark - roadway not lighted' conditions doubled from 4 to 8 year-over-year.

Weather

Snow9 (34.6%)
Clear7 (26.9%)
-30.0%prior 10
Cloudy4 (15.4%)
Rain/Cloudy1 (3.8%)
Snow/Rain1 (3.8%)
Blowing sand, snow1 (3.8%)
Snow/Snow1 (3.8%)
Cloudy/Blowing sand, snow1 (3.8%)
Cloudy/Rain1 (3.8%)

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

Lighting

Daylight14 (53.8%)
27.3%prior 11
Dark - roadway not lighted8 (30.8%)
Dark - lighted roadway4 (15.4%)
-50.0%prior 8

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

Road Surface

Snow11 (42.3%)
Dry9 (34.6%)
-35.7%prior 14
Wet3 (11.5%)
Slush2 (7.7%)
Ice1 (3.8%)

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 (37 vehicles)

1
TOYOTA7 (18.9%)
40.0%prior 5
2
NISSAN4 (10.8%)
3
FORD4 (10.8%)
4
KIA3 (8.1%)
5
VOLVO2 (5.4%)
6
HONDA2 (5.4%)
7
DODGE2 (5.4%)
8
KENWORTH MOTOR1 (2.7%)
9
FREIGHTLINER CO1 (2.7%)
10
FRT1 (2.7%)

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

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

Sex Distribution (39 persons with recorded sex)

Male20 (51.3%)
-23.1%prior 26
Female19 (48.7%)
-5.0%prior 20

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 the 25 mph speed zone decreased from 6 to 3, while those in the 30 mph zone increased from 5 to 7. The 35 mph speed zone maintained 6 crashes in both periods. The 65 mph speed zone experienced a decrease from 3 crashes to 2. No fatal crashes were reported in any speed zone during either period.

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: LEE, MA
  • Total crash records analyzed: 26
  • Total persons involved: 46
  • Total vehicles involved: 37

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). "LEE, 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/lee/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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Lee, MA Crash Report — December 2025 | ThatCarHitMe.com