Monthly Traffic Safety Analysis

90 CRASHES IN
FRAMINGHAM, MA
APRIL 2025

All metrics benchmarked againstApril 2024

FRAMINGHAM experienced a decrease in total crashes, from 102 in April 2024 to 90 in April 2025, marking an 11.8% reduction. This period also saw a significant decrease in hit-and-run incidents, which fell from 23 to 15 crashes. Additionally, pedestrian crashes reduced from 4 to 1 year-over-year.

90

-11.8%was 102

Total Crash Events

0

Persons Killed

22

-18.5%was 27

Persons Injured

15

-34.8%was 23

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates a decrease in crash activity in FRAMINGHAM, with total crashes falling from 102 to 90. This represents an 11.8% reduction year-over-year. Total injuries also decreased by 18.5%, from 27 in the prior period to 22 in the current period.

15

Hit-and-Run Crashes — April 2025

-34.8% vs prior (23)

The number of hit-and-run crashes decreased from 23 in April 2024 to 15 in April 2025, representing a 34.8% reduction. Consequently, the hit-and-run crash rate also decreased from 22.5% to 16.7% of all crashes. This indicates a downward trend in hit-and-run incidents.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

1

Pedestrians Injured

Prior: 4-75.0%

2

Cyclists Injured

Prior: 0%

19

Motorists Injured

Prior: 22-13.6%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · 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 Thursday (19 crashes) in the prior period to Saturday (23 crashes) in the current period. Similarly, the peak hour for crashes changed from 9 AM (11 crashes) in the prior period to 3 PM (9 crashes) in the current period. This suggests a shift in crash concentration towards weekend afternoons.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatalities remained at zero for both periods, indicating no change in the most severe outcome. While serious injury crashes remained constant at 1 in both periods, minor injury crashes increased from 12 to 15, a 25% rise in count. Conversely, possible injury crashes decreased from 7 to 4, representing a 42.9% reduction in count.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes1.1%
0.0%prior 1
Minor Injury15minor injury crashes16.7%
25.0%prior 12
Possible Injury4possible injury crashes4.4%
-42.9%prior 7
No Injury67no injury crashes74.4%
-11.8%prior 76

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · Most severe injury per crash record

Top Contributing Factors

Crashes attributed to 'No improper driving' decreased from 32 in the prior period to 25 in the current period, a 21.9% reduction in count. 'Failed to yield right of way' crashes also saw a substantial decrease from 17 to 9, a 47.1% reduction in count. 'Followed too closely' incidents decreased from 12 to 9, a 25% reduction in count.

Officer-Reported Primary Contributing Cause

No improper driving25 (27.8%)-21.9%prior 32
Followed too closely9 (10%)-25.0%prior 12
Failed to yield right of way9 (10%)-47.1%prior 17
Failure to keep in proper lane or running off road7 (7.8%)0.0%prior 7
Inattention6 (6.7%)
Disregarded traffic signs, signals, road markings4 (4.4%)
Driving too fast for conditions4 (4.4%)-20.0%prior 5
Other improper action3 (3.3%)
Fatigued/asleep2 (2.2%)
Exceeded authorized speed limit2 (2.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in daylight decreased from 72 in the prior period to 57 in the current period, while those in dark-lighted conditions increased from 17 to 23. Crashes on dry road surfaces increased from 64 to 69, whereas crashes on wet surfaces decreased from 23 to 16. Clear weather conditions remained the most common factor, accounting for 63 crashes in the current period compared to 57 in the prior period.

Weather

Clear/Clear45 (50.6%)
15.4%prior 39
Clear18 (20.2%)
0.0%prior 18
Rain/Rain5 (5.6%)
-16.7%prior 6
Rain4 (4.5%)
-20.0%prior 5
Rain/Cloudy3 (3.4%)
Cloudy/Cloudy3 (3.4%)
Snow/Snow2 (2.2%)
Cloudy/Clear2 (2.2%)
Cloudy2 (2.2%)
-66.7%prior 6
Snow/Sleet, hail (freezing rain or drizzle)2 (2.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · Weather condition at time of crash

Lighting

Daylight57 (64.0%)
-20.8%prior 72
Dark - lighted roadway23 (25.8%)
35.3%prior 17
Dawn4 (4.5%)
Dark - roadway not lighted3 (3.4%)
Dusk2 (2.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · Lighting condition field

Road Surface

Dry69 (77.5%)
7.8%prior 64
Wet16 (18.0%)
-30.4%prior 23
Slush2 (2.2%)
Snow2 (2.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · Road surface condition field

Vehicles & Demographics

The total number of persons involved in crashes decreased from 230 in the prior period to 182 in the current period. The 26-34 age group saw a notable reduction in involvement, from 43 persons to 26 persons. In contrast, the 45-54 age group experienced an increase in involvement, rising from 24 persons to 36 persons.

Top Vehicle Makes (159 vehicles)

1
TOYOTA25 (15.7%)
-34.2%prior 38
2
HONDA24 (15.1%)
26.3%prior 19
3
FORD22 (13.8%)
-8.3%prior 24
4
JEEP9 (5.7%)
28.6%prior 7
5
CHEVROLET7 (4.4%)
-30.0%prior 10
6
SUBARU6 (3.8%)
20.0%prior 5
7
NISSAN6 (3.8%)
-45.5%prior 11
8
BMW5 (3.1%)
9
HYUNDAI5 (3.1%)
-37.5%prior 8
10
ACURA3 (1.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · Vehicle unit records

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

Sex Distribution (158 persons with recorded sex)

Male100 (63.3%)
-7.4%prior 108
Female57 (36.1%)
-28.7%prior 80
X / Unspecified1 (0.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes occurring in the 65 MPH speed zone increased from 10 in the prior period to 12 in the current period. Conversely, crashes in the 25 MPH speed zone decreased from 4 to 2, and in the 30 MPH zone from 3 to 1. No fatalities were recorded in any speed zone for either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-04-01 to 2025-04-30 · 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: 2025-04-01 through 2025-04-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2025-04-01 through 2025-04-30 (30 days)
  • Geographic scope: FRAMINGHAM, MA
  • Total crash records analyzed: 90
  • Total persons involved: 182
  • Total vehicles involved: 159

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). "FRAMINGHAM, MA Crash Intelligence Report: April 2025." Published June 21, 2026. Reporting period: 2025-04-01 to 2025-04-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/framingham/april-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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Framingham, MA Crash Report — April 2025 | ThatCarHitMe.com