Monthly Traffic Safety Analysis

7,607 CRASHES IN
MASSACHUSETTS, MA
APRIL 2026

All metrics benchmarked againstApril 2025

In April 2026, there were 7,607 total crashes, a 22.4% decrease from the 9,808 crashes recorded in April 2025. This year-over-year decline was also reflected in key safety metrics, with total fatalities falling from 24 to 15 and total injuries decreasing from 3,156 to 2,348. The most significant shift was the overall reduction in crash volume across all major categories.

7,607

-22.4%was 9,808

Total Crash Events

15

-37.5%was 24

Persons Killed

2,348

-25.6%was 3,156

Persons Injured

754

-17.5%was 914

Hit-and-Run Crashes

Note: "Persons Killed" (15) counts individual fatalities across all crash events. "Fatal" in the severity table below (14) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 275 crashes with unreported severity are not shown in the severity breakdown.

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

Traffic safety metrics showed a significant downward trend in April 2026 compared to the same month in the previous year. Total crashes fell by 2,201 incidents, representing a 22.4% decrease. Similarly, fatalities dropped by 37.5% (from 24 to 15), and injuries decreased by 25.6% (from 3,156 to 2,348).

754

Hit-and-Run Crashes — April 2026

-17.5% vs prior (914)

While the absolute number of hit-and-run incidents decreased from 914 in April 2025 to 754 in April 2026, the hit-and-run rate as a proportion of all crashes saw a slight increase. The rate rose from 9.3% in the prior year to 9.9% in the current year, indicating that hit-and-runs constituted a slightly larger share of the total incidents.

Vulnerable Road User Casualties

3

Pedestrians Killed

Prior: 250.0%

0

Cyclists Killed

Prior: 00.0%

12

Motorists Killed

Prior: 22-45.5%

0

Other Killed

Prior: 00.0%

82

Pedestrians Injured

Prior: 103-20.4%

75

Cyclists Injured

Prior: 107-29.9%

2,165

Motorists Injured

Prior: 2,915-25.7%

26

Other Injured

Prior: 31-16.1%

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 temporal patterns of crashes showed some consistency year-over-year, though the peak day for crashes shifted from Wednesday (1,720 incidents) in April 2025 to Thursday (1,346 incidents) in April 2026. The peak hour for collisions remained the 3 p.m. hour in both periods. However, the number of crashes during this peak hour decreased from 812 to 677.

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

The severity of crashes decreased from April 2025 to April 2026. The number of fatal crashes fell from 24 to 14, and the fatal crash rate as a percentage of total crashes declined from 0.24% to 0.18%. The proportion of crashes resulting in serious injuries also saw a slight decrease, from 1.9% to 1.7% of all incidents. Correspondingly, the share of crashes with no reported injuries increased from 72.0% to 73.0%.

Severity is per crash event (most severe injury). 14 fatal crash events resulted in 15 persons killed.

Outcome by Severity (Crash Events)

Fatal14fatal crashes0.2%
-41.7%prior 24
Serious Injury129serious injury crashes1.7%
-29.9%prior 184
Minor Injury1,128minor injury crashes14.8%
-24.0%prior 1,484
Possible Injury506possible injury crashes6.7%
-24.0%prior 666
No Injury5,555no injury crashes73%
-21.3%prior 7,060

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

The ranking of the top five contributing factors remained unchanged year-over-year, with 'No improper driving' listed as the most common circumstance, followed by 'Inattention' and 'Failed to yield right of way'. However, the raw counts for these factors decreased significantly. Crashes attributed to 'Inattention' fell by 27.0% in count, from 1,395 in April 2025 to 1,019 in April 2026. Similarly, crashes involving 'Failed to yield right of way' decreased in count from 1,108 to 898.

Officer-Reported Primary Contributing Cause

No improper driving1,743 (22.9%)-23.7%prior 2,283
Inattention1,019 (13.4%)-27.0%prior 1,395
Failed to yield right of way898 (11.8%)-19.0%prior 1,108
Followed too closely779 (10.2%)-15.0%prior 916
Failure to keep in proper lane or running off road499 (6.6%)-3.9%prior 519
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner280 (3.7%)-10.0%prior 311
Other improper action259 (3.4%)-10.4%prior 289
Disregarded traffic signs, signals, road markings244 (3.2%)-10.0%prior 271
Distracted169 (2.2%)-19.5%prior 210
Made an improper turn103 (1.4%)-29.0%prior 145

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

Crashes in both periods predominantly occurred in daylight on dry roads, with the share of crashes under these conditions remaining stable. There was a notable shift in road surface conditions, with crashes on wet surfaces decreasing from 19.7% of the total in April 2025 to 14.8% in April 2026. This corresponds with a lower proportion of crashes occurring during rain, which fell from 8.0% to 5.5% of all incidents year-over-year.

Weather

Clear4,333 (57.9%)
-20.6%prior 5,459
Clear/Clear1,250 (16.7%)
1.8%prior 1,228
Cloudy626 (8.4%)
-17.1%prior 755
Rain422 (5.6%)
-46.4%prior 788
Cloudy/Rain162 (2.2%)
-48.4%prior 314
Clear/Cloudy135 (1.8%)
-9.4%prior 149
Cloudy/Cloudy129 (1.7%)
14.2%prior 113
Rain/Cloudy119 (1.6%)
-46.9%prior 224
Rain/Rain78 (1.0%)
-42.6%prior 136
Clear/Other38 (0.5%)
-39.7%prior 63

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Weather condition at time of crash

Lighting

Daylight5,767 (77.0%)
-22.1%prior 7,402
Dark - lighted roadway1,093 (14.6%)
-20.3%prior 1,371
Dark - roadway not lighted304 (4.1%)
-24.2%prior 401
Dusk156 (2.1%)
-32.2%prior 230
Dawn118 (1.6%)
-41.3%prior 201
Dark - unknown roadway lighting36 (0.5%)
-45.5%prior 66
Other11 (0.1%)
-26.7%prior 15

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

Road Surface

Dry6,264 (84.5%)
-16.0%prior 7,461
Wet1,127 (15.2%)
-41.8%prior 1,936
Sand, mud, dirt, oil, gravel8 (0.1%)
-42.9%prior 14
Snow8 (0.1%)
-94.4%prior 142
Ice3 (0.0%)
-87.0%prior 23
Water (standing, moving)2 (0.0%)
-66.7%prior 6
Other2 (0.0%)
-71.4%prior 7

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

Vehicles & Demographics

The demographic profile of vehicles and persons involved in crashes remained stable year-over-year. Toyota, Honda, and Ford were the top three vehicle makes involved in collisions in both April 2025 and April 2026, with their rank order unchanged. The age distribution of all persons involved also showed little change; for example, the 26-34 age group represented 15.9% of individuals in the prior year and 16.1% in the current year, with other age groups showing similar consistency.

Top Vehicle Makes (14,495 vehicles)

1
TOYOTA2,453 (16.9%)
-21.7%prior 3,131
2
HONDA1,832 (12.6%)
-23.2%prior 2,386
3
FORD1,496 (10.3%)
-18.3%prior 1,831
4
CHEVROLET970 (6.7%)
-23.4%prior 1,266
5
NISSAN822 (5.7%)
-24.8%prior 1,093
6
JEEP626 (4.3%)
-23.6%prior 819
7
HYUNDAI588 (4.1%)
-17.9%prior 716
8
SUBARU563 (3.9%)
-24.8%prior 749
9
KIA367 (2.5%)
-10.3%prior 409
10
MAZDA296 (2%)
-6.6%prior 317

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

1,971 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (15,788 persons with recorded sex)

Male9,129 (57.8%)
-20.4%prior 11,466
Female6,650 (42.1%)
-23.1%prior 8,642
X / Unspecified9 (0.1%)
12.5%prior 8

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

Crashes in both years were most frequent in zones with posted speed limits of 25 mph and 30 mph, with counts decreasing in line with the overall trend. A notable shift occurred in the location of fatal crashes; in April 2025, the highest number of fatal crashes (5) occurred in 65 mph zones. In contrast, April 2026 saw the highest concentration of fatal crashes (7) in 30 mph zones, while only one fatal crash was recorded in a 65 mph zone.

Fatal crashes by zone: 30 mph: 7 of 1,841 (0.38%) · 35 mph: 1 of 824 (0.121%) · 40 mph: 4 of 570 (0.702%) · 50 mph: 1 of 170 (0.588%) · 65 mph: 1 of 496 (0.202%)

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: massachusetts, MA
  • Total crash records analyzed: 7,607
  • Total persons involved: 17,884
  • Total vehicles involved: 14,495

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). "massachusetts, 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/statewide/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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