Monthly Traffic Safety Analysis

28 CRASHES IN
PEMBROKE, MA
FEBRUARY 2026

All metrics benchmarked againstFebruary 2025

Total crashes in Pembroke increased from 21 in February 2025 to 28 in February 2026, representing a 33.3% rise year-over-year. This period also saw an 80% increase in total injuries, climbing from 5 to 9. A notable shift was the significant increase in crashes occurring on snowy road surfaces, which rose from 3 to 14.

28

33.3%was 21

Total Crash Events

0

Persons Killed

9

80.0%was 5

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 · 2026-02-01 to 2026-02-28 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash data for Pembroke in February 2026 indicates an upward trend compared to February 2025. Total crashes increased by 33.3%, from 21 to 28, while total injuries rose by 80%, from 5 to 9. Fatalities remained at zero for both periods.

1

Hit-and-Run Crashes — February 2026

0.0% vs prior (1)

The number of hit-and-run crashes remained constant at 1 for both February 2025 and February 2026. However, due to the overall increase in total crashes, the hit-and-run rate decreased from 4.8% in February 2025 to 3.6% in February 2026.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

9

Motorists Injured

Prior: 580.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · 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. In February 2026, the peak day for crashes was Saturday with 13 incidents, a significant increase from just 1 crash on Saturdays in February 2025. The peak hour for crashes also shifted, with 4 crashes occurring at 7 AM in February 2026, compared to 4 crashes at 11 AM in February 2025.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While no fatalities were reported in either period, the number of injuries increased from 5 in February 2025 to 9 in February 2026. Minor injury crashes (severity 'B') increased from 2 (9.5% of crashes) to 3 (10.7% of crashes), and possible injury crashes (severity 'C') increased from 1 (4.8%) to 3 (10.7%). Consequently, crashes with no injuries decreased from 18 (85.7%) to 22 (78.6%) of the total crashes.

Outcome by Severity (Crash Events)

Minor Injury3minor injury crashes10.7%
50.0%prior 2
Possible Injury3possible injury crashes10.7%
200.0%prior 1
No Injury22no injury crashes78.6%
22.2%prior 18

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Most severe injury per crash record

Top Contributing Factors

Several contributing factors saw notable increases in crash counts year-over-year. Crashes attributed to "Driving too fast for conditions" increased by 300%, from 1 in February 2025 to 4 in February 2026. "Inattention" also saw a 100% increase, rising from 2 to 4 crashes, while "Failed to yield right of way" increased by 33.3%, from 3 to 4 crashes. Conversely, crashes with "No improper driving" decreased by 40%, from 5 to 3.

Officer-Reported Primary Contributing Cause

Driving too fast for conditions4 (14.3%)
Failed to yield right of way4 (14.3%)
Inattention4 (14.3%)
No improper driving3 (10.7%)-40.0%prior 5
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner2 (7.1%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway2 (7.1%)
Failure to keep in proper lane or running off road2 (7.1%)
Followed too closely2 (7.1%)
Wrong side or wrong way1 (3.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Adverse conditions played a more significant role in February 2026 crashes compared to the prior year. Crashes occurring on "Snow" road surfaces increased by 366.7%, from 3 to 14, and those in "Dark - roadway not lighted" conditions doubled from 2 to 4. While "Clear" weather crashes decreased from 15 to 13, snow-related weather conditions like "Snow" and "Snow/Blowing sand, snow" saw increased representation.

Weather

Clear13 (46.4%)
-13.3%prior 15
Snow6 (21.4%)
Snow/Blowing sand, snow3 (10.7%)
Cloudy2 (7.1%)
Rain1 (3.6%)
Sleet, hail (freezing rain or drizzle)1 (3.6%)
Snow/Other1 (3.6%)
Snow/Sleet, hail (freezing rain or drizzle)1 (3.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Weather condition at time of crash

Lighting

Daylight17 (60.7%)
21.4%prior 14
Dark - lighted roadway5 (17.9%)
Dark - roadway not lighted4 (14.3%)
Dusk2 (7.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Lighting condition field

Road Surface

Snow14 (50.0%)
Dry12 (42.9%)
-25.0%prior 16
Slush1 (3.6%)
Wet1 (3.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (53 vehicles)

1
FORD10 (18.9%)
2
TOYOTA8 (15.1%)
33.3%prior 6
3
CHEVROLET4 (7.5%)
4
JEEP3 (5.7%)
5
NISSAN3 (5.7%)
-50.0%prior 6
6
HYUNDAI3 (5.7%)
7
VOLKSWAGEN2 (3.8%)
8
MAZDA2 (3.8%)
9
MERCEDES-BENZ2 (3.8%)
10
SUBARU2 (3.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Vehicle unit records

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

Sex Distribution (61 persons with recorded sex)

Male37 (60.7%)
42.3%prior 26
Female24 (39.3%)
20.0%prior 20

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · Person-level records linked to crash events

Speed Limit Zones

The distribution of crashes across speed zones shifted in February 2026. Crashes in 35 mph zones increased by 225%, rising from 4 in February 2025 to 13 in February 2026. Conversely, crashes in 25 mph zones decreased from 5 to 3, and those in 40 mph zones decreased from 7 to 4. No fatalities were recorded in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-02-01 to 2026-02-28 · 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: 2026-02-01 through 2026-02-28
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2026-02-01 through 2026-02-28 (28 days)
  • Geographic scope: PEMBROKE, MA
  • Total crash records analyzed: 28
  • Total persons involved: 62
  • Total vehicles involved: 53

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). "PEMBROKE, MA Crash Intelligence Report: February 2026." Published June 21, 2026. Reporting period: 2026-02-01 to 2026-02-28. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/pembroke/february-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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Pembroke, MA Crash Report — February 2026 | ThatCarHitMe.com