Monthly Traffic Safety Analysis

21 CRASHES IN
FALMOUTH, MA
NOVEMBER 2025

All metrics benchmarked againstNovember 2024

In November 2025, Falmouth experienced 21 total crashes, a significant decrease of 60.38% compared to the 53 crashes recorded in November 2024. Total injuries also saw a 50% reduction, falling from 14 to 7 year-over-year. The most notable shift was the 80% decrease in hit-and-run crashes, from 5 in the prior year to 1 in the current period.

21

▼ -60.4%was 53

Total Crash Events

0

Persons Killed

7

▼ -50.0%was 14

Persons Injured

1

▼ -80.0%was 5

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 · 2025-11-01 to 2025-11-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crashes in Falmouth showed a substantial downward trend year-over-year, with total crashes decreasing from 53 in November 2024 to 21 in November 2025. This represents a 60.38% reduction in total crash incidents. The number of injured persons also decreased by 50%, from 14 to 7.

1

Hit-and-Run Crashes — November 2025

▼ -80.0% vs prior (5)

Hit-and-run crashes saw a significant decrease, falling from 5 incidents in November 2024 to 1 incident in November 2025. This reduction of 4 crashes also led to a decrease in the hit-and-run rate, which dropped from 9.4% to 4.8% year-over-year.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 0→0.0%

7

Motorists Injured

Prior: 11↓-36.4%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes shifted significantly year-over-year. In November 2024, the peak day for crashes was Saturday with 10 incidents, and the peak hour was 4 PM with 8 crashes. In November 2025, the peak day shifted to Thursday with 4 crashes, and the peak hour moved to 11 AM, also with 4 crashes.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There were no fatal crashes in either November 2024 or November 2025. Total injuries decreased from 14 to 7, a 50% reduction. Crashes with serious injuries (severity 'A') decreased from 2 (3.8% of crashes) in November 2024 to 0 in November 2025. The proportion of crashes resulting in no injury increased from 75.5% to 81% year-over-year.

Outcome by Severity (Crash Events)

Minor Injury2minor injury crashes9.5%
↓-80.0%prior 10
Possible Injury2possible injury crashes9.5%
No Injury17no injury crashes81%
↓-57.5%prior 40

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Most severe injury per crash record

Top Contributing Factors

The top contributing factor, 'No improper driving', decreased in count from 17 crashes in November 2024 to 7 crashes in November 2025. 'Inattention', which was the second-highest factor in the prior period with 14 crashes, was not listed among the current period's factors. Crashes attributed to 'Failed to yield right of way' decreased from 8 to 5, while 'Exceeded authorized speed limit' crashes increased from 0 to 2.

Officer-Reported Primary Contributing Cause

No improper driving7 (33.3%)↓-58.8%prior 17
Failed to yield right of way5 (23.8%)↓-37.5%prior 8
Failure to keep in proper lane or running off road2 (9.5%)
Exceeded authorized speed limit2 (9.5%)
Disregarded traffic signs, signals, road markings1 (4.8%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (4.8%)
Distracted1 (4.8%)
Driving too fast for conditions1 (4.8%)
Followed too closely1 (4.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in clear weather conditions decreased from 43 to 13 year-over-year. The proportion of crashes occurring in daylight decreased from 54.7% (29 of 53) in November 2024 to 38.1% (8 of 21) in November 2025. The proportion of crashes on wet roads slightly increased from 13.2% (7 of 53) to 19.0% (4 of 21), despite a decrease in the absolute count of wet road crashes.

Weather

Clear13 (65.0%)
↓-69.8%prior 43
Rain2 (10.0%)
Clear/Clear2 (10.0%)
Rain/Cloudy1 (5.0%)
Cloudy/Clear1 (5.0%)
Rain/Clear1 (5.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Weather condition at time of crash

Lighting

Daylight8 (40.0%)
↓-72.4%prior 29
Dark - lighted roadway5 (25.0%)
↓-37.5%prior 8
Dark - roadway not lighted5 (25.0%)
↓-16.7%prior 6
Dark - unknown roadway lighting1 (5.0%)
Dawn1 (5.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Lighting condition field

Road Surface

Dry17 (81.0%)
↓-62.2%prior 45
Wet4 (19.0%)
↓-42.9%prior 7

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (33 vehicles)

1
TOYOTA6 (18.2%)
↓-73.9%prior 23
2
HYUNDAI4 (12.1%)
3
FORD4 (12.1%)
↓-33.3%prior 6
4
NISSAN4 (12.1%)
↓-33.3%prior 6
5
BMW3 (9.1%)
6
VOLKSWAGEN2 (6.1%)
7
JEEP2 (6.1%)
8
KIA1 (3%)
9
LEXUS1 (3%)
10
MERCEDESBENZ1 (3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Vehicle unit records

Sex Distribution (36 persons with recorded sex)

Male24 (66.7%)
↓-57.1%prior 56
Female12 (33.3%)
↓-73.9%prior 46

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes in the 25 MPH speed zone decreased from 5 to 1, and in the 30 MPH zone from 9 to 6. While crashes across most speed zones decreased, incidents in the 55 MPH zone increased from 1 in November 2024 to 3 in November 2025. The prior period recorded crashes in lower speed zones (5, 10, 15 MPH) and the 40 MPH zone, which were not present in the current period's data.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · 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: 2025-11-01 through 2025-11-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2025-11-01 through 2025-11-30 (30 days)
  • Geographic scope: FALMOUTH, MA
  • Total crash records analyzed: 21
  • Total persons involved: 37
  • Total vehicles involved: 33

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). "FALMOUTH, MA Crash Intelligence Report: November 2025." Published June 21, 2026. Reporting period: 2025-11-01 to 2025-11-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/falmouth/november-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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Falmouth, MA Crash Report — November 2025 | ThatCarHitMe.com