Monthly Traffic Safety Analysis

85 CRASHES IN
BARNSTABLE, MA
JANUARY 2026

All metrics benchmarked againstJanuary 2025

Total crashes in BARNSTABLE increased by 14.86% year-over-year, rising from 74 crashes in January 2025 to 85 crashes in January 2026. This period also saw a notable shift from zero fatalities in the prior year to one fatality in the current year.

85

14.9%was 74

Total Crash Events

1

Persons Killed

16

-20.0%was 20

Persons Injured

3

-25.0%was 4

Hit-and-Run Crashes

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 2 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crashes in BARNSTABLE show an upward trend, with total crashes increasing by 11 incidents, from 74 to 85. While total fatalities increased from 0 to 1, total injuries decreased by 20%, falling from 20 to 16.

3

Hit-and-Run Crashes — January 2026

-25.0% vs prior (4)

Hit-and-run crashes decreased by 1 incident, from 4 in January 2025 to 3 in January 2026. The hit-and-run rate also saw a decrease, falling from 5.4% of all crashes in the prior period to 3.5% in the current period.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 0%

16

Motorists Injured

Prior: 17-5.9%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · 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 Wednesday (17 crashes) in January 2025 to Thursday (17 crashes) in January 2026. The peak hour also changed, with 5 PM recording 8 crashes in the prior period, while 2 PM recorded 9 crashes in the current period. Crashes on Tuesdays saw a notable increase from 7 to 15.

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

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

Crash Severity Breakdown

The most significant change in severity was the increase in fatal crashes from 0 in January 2025 to 1 in January 2026. While serious injury crashes remained stable at 1, minor injury crashes decreased from 9 to 8, and possible injury crashes dropped from 7 to 3. The proportion of no-injury crashes increased from 75.7% to 82.4% of all incidents.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.2%
Serious Injury1serious injury crashes1.2%
0.0%prior 1
Minor Injury8minor injury crashes9.4%
-11.1%prior 9
Possible Injury3possible injury crashes3.5%
-57.1%prior 7
No Injury70no injury crashes82.4%
25.0%prior 56

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

Severity Distribution (Crash Events)

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

Top Contributing Factors

The count of crashes attributed to 'No improper driving' increased by 6, from 15 to 21, representing a 40% rise. Conversely, 'Inattention' related crashes decreased by 6, from 17 to 11, a 35.3% reduction. Crashes due to 'Failed to yield right of way' doubled, increasing by 5 incidents from 5 to 10.

Officer-Reported Primary Contributing Cause

No improper driving21 (24.7%)40.0%prior 15
Inattention11 (12.9%)-35.3%prior 17
Failed to yield right of way10 (11.8%)100.0%prior 5
Failure to keep in proper lane or running off road5 (5.9%)
Visibility obstructed4 (4.7%)
Driving too fast for conditions4 (4.7%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner3 (3.5%)
Followed too closely2 (2.4%)
Glare2 (2.4%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway2 (2.4%)

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

Road & Environmental Conditions

Crashes occurring on snowy road surfaces saw a substantial increase, rising from 1 in January 2025 to 17 in January 2026, a 1600% increase. Similarly, crashes on icy road surfaces increased by 8 incidents, from 4 to 12. While crashes under clear weather conditions remained stable at 57, crashes under dry road conditions decreased by 22, from 64 to 42.

Weather

Clear57 (68.7%)
0.0%prior 57
Snow9 (10.8%)
Cloudy4 (4.8%)
Rain2 (2.4%)
Clear/Clear2 (2.4%)
Clear/Cloudy2 (2.4%)
Snow/Cloudy2 (2.4%)
Snow/Sleet, hail (freezing rain or drizzle)2 (2.4%)
Clear/Unknown1 (1.2%)
Cloudy/Snow1 (1.2%)

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

Lighting

Daylight58 (69.9%)
38.1%prior 42
Dark - lighted roadway14 (16.9%)
-17.6%prior 17
Dark - roadway not lighted8 (9.6%)
14.3%prior 7
Dawn1 (1.2%)
Dark - unknown roadway lighting1 (1.2%)
Dusk1 (1.2%)

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

Road Surface

Dry42 (50.6%)
-34.4%prior 64
Snow17 (20.5%)
Ice12 (14.5%)
Wet10 (12.0%)
100.0%prior 5
Slush2 (2.4%)

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

Vehicles & Demographics

The total number of vehicles involved in crashes increased by 17, from 139 to 156. Toyota vehicles involved in crashes more than doubled, rising from 18 to 39, making it the top make in January 2026. Conversely, Jeep vehicles involved decreased significantly from 16 to 4.

Top Vehicle Makes (156 vehicles)

1
TOYOTA39 (25%)
116.7%prior 18
2
FORD17 (10.9%)
-29.2%prior 24
3
CHEVROLET17 (10.9%)
70.0%prior 10
4
HONDA15 (9.6%)
66.7%prior 9
5
NISSAN6 (3.8%)
-33.3%prior 9
6
VOLKSWAGEN5 (3.2%)
7
GMC5 (3.2%)
0.0%prior 5
8
HYUNDAI5 (3.2%)
9
KIA5 (3.2%)
10
BMW4 (2.6%)
-20.0%prior 5

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

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

Sex Distribution (165 persons with recorded sex)

Male94 (57.0%)
8.0%prior 87
Female71 (43.0%)
9.2%prior 65

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

Speed Limit Zones

Crashes occurring in 30 mph zones more than doubled, increasing by 19 incidents from 16 in January 2025 to 35 in January 2026. In contrast, crashes in 35 mph zones decreased by 14 incidents, from 32 to 18. The only fatal crash in January 2026 occurred in a 55 mph zone, where no fatalities were recorded in the prior period.

Fatal crashes by zone: 55 mph: 1 of 5 (20%)

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

Data Coverage

  • Reporting period: 2026-01-01 through 2026-01-31 (31 days)
  • Geographic scope: BARNSTABLE, MA
  • Total crash records analyzed: 85
  • Total persons involved: 173
  • Total vehicles involved: 156

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). "BARNSTABLE, MA Crash Intelligence Report: January 2026." Published June 21, 2026. Reporting period: 2026-01-01 to 2026-01-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/barnstable/january-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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Barnstable, MA Crash Report — January 2026 | ThatCarHitMe.com