Monthly Traffic Safety Analysis

5 CRASHES IN
TYNGSBOROUGH, MA
MARCH 2026

All metrics benchmarked againstMarch 2025

In March 2026, Tyngsborough experienced a significant decrease in total crashes, with 5 reported incidents compared to 22 in March 2025, representing a 77.27% reduction. This period also saw a notable decline in fatalities, dropping from 1 in the prior year to 0 in the current month.

5

-77.3%was 22

Total Crash Events

0

-100.0%was 1

Persons Killed

0

Persons Injured

0

-100.0%was 1

Fatal Crash Events

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. 5 crashes with unreported severity are not shown in the severity breakdown.

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

The overall trend indicates a substantial decrease in crash activity year-over-year. Total crashes fell from 22 in March 2025 to 5 in March 2026, marking a 77.27% reduction. Fatalities also decreased, with 1 fatality reported in March 2025 and none in March 2026.

When Crashes Happen

The temporal patterns for crashes shifted notably between the two periods. In March 2025, the peak day for crashes was Sunday with 6 incidents, and the peak hour was 8 PM with 2 incidents. In contrast, March 2026 saw Friday as the peak day with 2 crashes, and 12 AM as the peak hour with 2 crashes.

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)

Top Contributing Factors

The contributing factors show a shift in prominent issues. 'Followed too closely' remained consistent with 1 crash in both periods. Factors like 'Driving too fast for conditions' and 'Fatigued/asleep' emerged in March 2026, each contributing to 2 crashes, whereas they were not present in the prior period's data. Conversely, 'Inattention,' which accounted for 10 crashes (45.5% share) in March 2025, was not a contributing factor in March 2026.

Officer-Reported Primary Contributing Cause

Driving too fast for conditions2 (40%)
Fatigued/asleep2 (40%)
Followed too closely1 (20%)

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

Regarding crash conditions, the prior period in March 2025 saw 19 crashes occur in clear weather and 15 in daylight conditions, with 18 on dry roads. In March 2026, clear weather accounted for 3 crashes, daylight for 1 crash, and dry roads for 3 crashes. The current period also reported 2 crashes on snowy roads and 2 crashes during sleet/hail or snow conditions, which were not present in the prior period's weather data.

Weather

Clear/Clear3 (60.0%)
Sleet, hail (freezing rain or drizzle)/Snow1 (20.0%)
Snow/Sleet, hail (freezing rain or drizzle)1 (20.0%)

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

Lighting

Dark - lighted roadway2 (40.0%)
-66.7%prior 6
Dark - roadway not lighted1 (20.0%)
Dawn1 (20.0%)
Daylight1 (20.0%)
-93.3%prior 15

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

Road Surface

Dry3 (60.0%)
-83.3%prior 18
Snow2 (40.0%)

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

Vehicles & Demographics

Top Vehicle Makes (9 vehicles)

1
HONDA2 (22.2%)
2
TOYOTA2 (22.2%)
-71.4%prior 7
3
FREIGHTLINER CO1 (11.1%)
4
MAZDA1 (11.1%)
5
SUBARU1 (11.1%)
6
JEEP1 (11.1%)
7
GMC1 (11.1%)

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

Sex Distribution (10 persons with recorded sex)

Male9 (90.0%)
-65.4%prior 26
Female1 (10.0%)
-94.7%prior 19

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

In March 2026, all 5 crashes occurred in the 65 mph speed zone, representing an increase from the 2 crashes recorded in this zone during March 2025. The prior period showed a broader distribution of crashes across various speed limits, ranging from 5 mph to 65 mph, including 1 fatal crash in a 40 mph zone, which is absent in the current data.

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: TYNGSBOROUGH, MA
  • Total crash records analyzed: 5
  • Total persons involved: 10
  • Total vehicles involved: 9

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). "TYNGSBOROUGH, 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/tyngsborough/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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Tyngsborough, MA Crash Report — March 2026 | ThatCarHitMe.com