Monthly Traffic Safety Analysis

3 CRASHES IN
PRINCETON, MA
APRIL 2026

All metrics benchmarked againstApril 2025

In April 2026, Princeton experienced 3 crashes, a 40% decrease compared to the 5 crashes reported in April 2025. Despite the reduction in total crashes, injuries increased by 50%, rising from 2 to 3 over the same period. The most notable shift was the absence of "Driving too fast for conditions" as a contributing factor in April 2026, which was present in 2 crashes in April 2025.

3

-40.0%was 5

Total Crash Events

0

Persons Killed

3

50.0%was 2

Persons Injured

0

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.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Total crashes decreased from 5 in April 2025 to 3 in April 2026, representing a 40% reduction year-over-year. Fatalities remained stable at 0 in both periods. However, total injuries increased by 50%, from 2 in April 2025 to 3 in April 2026.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

3

Motorists Injured

Prior: 250.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · 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 Saturday, which had 2 crashes in April 2025, to Friday, which also recorded 2 crashes in April 2026. The peak hour for crashes changed from 12 p.m. with 2 crashes in April 2025 to 5 p.m. with 1 crash in April 2026. Crashes on Saturday decreased from 2 to 0, while Monday crashes remained consistent at 1 for both periods.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatalities remained at 0 in both April 2025 and April 2026. Total injuries increased by 50%, from 2 in April 2025 to 3 in April 2026. In April 2025, 2 crashes resulted in minor injuries, accounting for 40% of all crashes, while in April 2026, 1 crash resulted in serious injury, representing 33.3% of crashes.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes33.3%
No Injury2no injury crashes66.7%
-33.3%prior 3

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Most severe injury per crash record

Top Contributing Factors

Crashes attributed to "No improper driving" decreased by 50%, from 2 in April 2025 to 1 in April 2026. "Driving too fast for conditions" and "Fatigued/asleep" factors, which accounted for 2 and 1 crash respectively in April 2025, were not reported in April 2026. Conversely, "Followed too closely" and "Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway" each emerged as a factor in 1 crash in April 2026, having not been present in April 2025.

Officer-Reported Primary Contributing Cause

Followed too closely1 (33.3%)
No improper driving1 (33.3%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway1 (33.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Lighting

Daylight2 (66.7%)
Dark - lighted roadway1 (33.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Lighting condition field

Vehicles & Demographics

Top Vehicle Makes (4 vehicles)

1
BUIC1 (25%)
2
DODGE1 (25%)
3
FORD1 (25%)
4
NISSAN1 (25%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Vehicle unit records

Sex Distribution (6 persons with recorded sex)

Male5 (83.3%)
66.7%prior 3
Female1 (16.7%)
-66.7%prior 3

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Person-level records linked to crash events

Speed Limit Zones

The total number of crashes with reported speed limits decreased from 5 in April 2025 to 3 in April 2026. Crashes in the 40 mph zone decreased by 66.7%, from 3 crashes in April 2025 to 1 crash in April 2026. A crash occurred in a 25 mph zone in April 2026, where none were reported in the prior period, while a crash in a 30 mph zone in April 2025 had no counterpart in April 2026. No fatal crashes were reported in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · 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: 2026-04-01 through 2026-04-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2026-04-01 through 2026-04-30 (30 days)
  • Geographic scope: PRINCETON, MA
  • Total crash records analyzed: 3
  • Total persons involved: 6
  • Total vehicles involved: 4

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). "PRINCETON, MA Crash Intelligence Report: April 2026." Published June 21, 2026. Reporting period: 2026-04-01 to 2026-04-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/princeton/april-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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Princeton, MA Crash Report — April 2026 | ThatCarHitMe.com