Monthly Traffic Safety Analysis

35 CRASHES IN
FALMOUTH, MA
MARCH 2026

All metrics benchmarked againstMarch 2025

In March 2026, Falmouth experienced 35 crashes, a decrease of 31.4% compared to the 51 crashes reported in March 2025. Notably, there were no fatalities in March 2026, down from one fatality in the prior year. This period also saw a significant reduction in crashes attributed to 'Inattention' and an absence of DUI-related incidents.

35

-31.4%was 51

Total Crash Events

0

-100.0%was 1

Persons Killed

11

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 · 2026-03-01 to 2026-03-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash incidents in Falmouth showed a decreasing trend year-over-year, with total crashes falling by 31.4% from 51 to 35. Fatalities also decreased from one in March 2025 to zero in March 2026. The total number of injuries remained stable at 11 for both periods.

2

Hit-and-Run Crashes — March 2026

0.0% vs prior (2)

The number of hit-and-run crashes remained consistent at 2 incidents for both March 2025 and March 2026. However, the hit-and-run rate increased from 3.9% in the prior period to 5.7% in the current period. This rise in rate is due to the overall decrease in total crashes.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 1-100.0%

11

Motorists Injured

Prior: 110.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes shifted from Friday with 11 incidents in March 2025 to Tuesday with 11 incidents in March 2026. The peak hour for crashes also changed, moving from 5 PM with 6 crashes in the prior period to 3 PM with 5 crashes in the current period. This indicates a shift in the most common times and days for crash occurrences.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatal crashes decreased from 1 (1.96% of crashes) in March 2025 to 0 (0% of crashes) in March 2026. Crashes resulting in serious injury (code 'A') increased from 0 in the prior period to 4 crashes (11.4%) in the current period. Minor injury (code 'B') and possible injury (code 'C') crashes both decreased from 4 to 2 and 5 to 2, respectively.

Outcome by Severity (Crash Events)

Serious Injury4serious injury crashes11.4%
Minor Injury2minor injury crashes5.7%
-50.0%prior 4
Possible Injury2possible injury crashes5.7%
-60.0%prior 5
No Injury27no injury crashes77.1%
-30.8%prior 39

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Most severe injury per crash record

Top Contributing Factors

The top contributing factor, 'Inattention,' decreased significantly from 13 crashes in March 2025 to 5 crashes in March 2026, a 61.5% reduction. Conversely, 'Failed to yield right of way' crashes increased by 50%, from 6 to 9 incidents, becoming the leading factor. Crashes involving 'Other improper action' also rose from 1 to 3 incidents year-over-year.

Officer-Reported Primary Contributing Cause

Failed to yield right of way9 (25.7%)50.0%prior 6
No improper driving8 (22.9%)33.3%prior 6
Followed too closely5 (14.3%)0.0%prior 5
Inattention5 (14.3%)-61.5%prior 13
Other improper action3 (8.6%)
Failure to keep in proper lane or running off road2 (5.7%)
Over-correcting/over-steering1 (2.9%)
Exceeded authorized speed limit1 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear' weather conditions decreased from 32 to 18, while those in 'Rain' conditions increased from 2 to 7. Similarly, crashes on 'Dry' road surfaces decreased from 43 to 21, but crashes on 'Wet' surfaces nearly doubled from 7 to 13. There was also a decrease in crashes occurring during 'Dark - lighted roadway' conditions, from 8 to 3 incidents.

Weather

Clear18 (51.4%)
-43.8%prior 32
Rain7 (20.0%)
Cloudy5 (14.3%)
0.0%prior 5
Cloudy/Rain2 (5.7%)
Fog, smog, smoke1 (2.9%)
Rain/Fog, smog, smoke1 (2.9%)
Rain/Rain1 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Weather condition at time of crash

Lighting

Daylight29 (82.9%)
-23.7%prior 38
Dark - lighted roadway3 (8.6%)
-62.5%prior 8
Dark - roadway not lighted1 (2.9%)
Dark - unknown roadway lighting1 (2.9%)
Dawn1 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Lighting condition field

Road Surface

Dry21 (60.0%)
-51.2%prior 43
Wet13 (37.1%)
85.7%prior 7
Ice1 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Road surface condition field

Vehicles & Demographics

The 16-20 age group saw a notable decrease in involvement, from 16 persons in March 2025 to 6 persons in March 2026. Similarly, the 26-34 age group's involvement dropped from 20 to 12 persons. Toyota vehicles became the most frequently involved make, with 12 incidents, surpassing Ford which dropped from 15 to 7 incidents.

Top Vehicle Makes (63 vehicles)

1
TOYOTA12 (19%)
9.1%prior 11
2
FORD7 (11.1%)
-53.3%prior 15
3
CHEVROLET6 (9.5%)
-14.3%prior 7
4
HONDA5 (7.9%)
-61.5%prior 13
5
JEEP4 (6.3%)
6
VOLKSWAGEN3 (4.8%)
7
VOLVO3 (4.8%)
8
SUBARU3 (4.8%)
9
RAM3 (4.8%)
10
GMC2 (3.2%)
-66.7%prior 6

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Vehicle unit records

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

Sex Distribution (70 persons with recorded sex)

Male38 (54.3%)
-35.6%prior 59
Female32 (45.7%)
-23.8%prior 42

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 30 mph zones decreased from 11 to 4, and in 35 mph zones from 15 to 7. Conversely, crashes in 40 mph zones increased from 5 to 7. The single fatal crash reported in a 55 mph zone in March 2025 was not present in March 2026, where 3 crashes occurred in that speed zone without fatalities.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · 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: 2026-03-01 through 2026-03-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2026-03-01 through 2026-03-31 (31 days)
  • Geographic scope: FALMOUTH, MA
  • Total crash records analyzed: 35
  • Total persons involved: 72
  • Total vehicles involved: 63

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: March 2026." Published June 21, 2026. Reporting period: 2026-03-01 to 2026-03-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/falmouth/march-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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Falmouth, MA Crash Report — March 2026 | ThatCarHitMe.com