Yearly Traffic Safety Analysis

129,276 CRASHES IN
MASSACHUSETTS, MA
2025

All metrics benchmarked against2024

In the current period, there were 129,276 total traffic crashes, a 4.6% decrease from the 135,445 crashes recorded in the prior year. This downward trend was also observed in total injuries and fatalities. One of the most notable shifts was in crash location by speed limit, where collisions increased in 25 mph zones while decreasing in 30 mph zones, running counter to the overall downward trend.

129,276

▼ -4.6%was 135,445

Total Crash Events

358

▼ -1.4%was 363

Persons Killed

40,088

▼ -4.3%was 41,887

Persons Injured

12,365

▼ -1.6%was 12,560

Hit-and-Run Crashes

Note: "Persons Killed" (358) counts individual fatalities across all crash events. "Fatal" in the severity table below (340) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 5,117 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend in traffic incidents is downward year-over-year. Total crashes decreased by 4.6%, from 135,445 to 129,276. Similarly, total injuries fell by 4.3% (from 41,887 to 40,088) and total fatalities decreased by 1.4% (from 363 to 358).

12,365

Hit-and-Run Crashes — 2025

▼ -1.6% vs prior (12,560)

The number of hit-and-run crashes saw a slight decrease from 12,560 to 12,365. However, as a proportion of all crashes, the hit-and-run rate trended upward, increasing from 9.3% in the prior period to 9.6% in the current period.

Vulnerable Road User Casualties

73

Pedestrians Killed

Prior: 74↓-1.4%

9

Cyclists Killed

Prior: 10↓-10.0%

273

Motorists Killed

Prior: 271↑0.7%

3

Other Killed

Prior: 8↓-62.5%

1,461

Pedestrians Injured

Prior: 1,635↓-10.6%

1,208

Cyclists Injured

Prior: 1,244↓-2.9%

37,010

Motorists Injured

Prior: 38,618↓-4.2%

409

Other Injured

Prior: 390↑4.9%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

Temporal crash patterns remained largely consistent between the two periods. Friday was the peak day for crashes in both years, though the total count on Fridays decreased from 21,529 to 19,909. The peak hour for crashes shifted slightly earlier, from the 4 p.m. hour in the prior year (10,925 crashes) to the 3 p.m. hour in the current year (10,389 crashes).

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes showed minor changes year-over-year. The proportion of fatal crashes remained stable at 0.3% of all incidents in both periods. The share of crashes involving serious injuries decreased slightly from 1.8% to 1.7%, while the share of minor injury crashes increased from 14.1% to 14.6%.

Severity is per crash event (most severe injury). 340 fatal crash events resulted in 358 persons killed.

Outcome by Severity (Crash Events)

Fatal340fatal crashes0.3%
↓-2.6%prior 349
Serious Injury2,243serious injury crashes1.7%
↓-5.5%prior 2,374
Minor Injury18,895minor injury crashes14.6%
↓-1.0%prior 19,083
Possible Injury8,569possible injury crashes6.6%
↓-11.3%prior 9,656
No Injury94,112no injury crashes72.8%
↓-3.8%prior 97,827

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record

Top Contributing Factors

The top contributing factors remained consistent across both periods, with 'No improper driving,' 'Inattention,' and 'Failed to yield right of way' as the leading three. The counts for most factors decreased, in line with the overall reduction in crashes. For instance, crashes attributed to 'Inattention' decreased in count by 6.3% (from 18,473 to 17,310), and those involving 'Driving too fast for conditions' decreased in count by 11.3% (from 3,569 to 3,165).

Officer-Reported Primary Contributing Cause

No improper driving32,364 (25%)↓-1.8%prior 32,971
Inattention17,310 (13.4%)↓-6.3%prior 18,473
Failed to yield right of way14,170 (11%)↓-2.2%prior 14,484
Followed too closely11,646 (9%)↓-7.6%prior 12,609
Failure to keep in proper lane or running off road6,500 (5%)↑1.5%prior 6,402
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner4,003 (3.1%)↓-3.2%prior 4,136
Disregarded traffic signs, signals, road markings3,997 (3.1%)↓-0.2%prior 4,004
Other improper action3,675 (2.8%)↓-11.5%prior 4,152
Driving too fast for conditions3,165 (2.4%)↓-11.3%prior 3,569
Distracted2,423 (1.9%)↓-12.3%prior 2,762

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The distribution of crashes across different environmental conditions was largely stable year-over-year. The proportion of crashes occurring in daylight was consistent, at 68.3% in the current period versus 67.7% in the prior period. Similarly, crashes on dry road surfaces accounted for 78.4% of incidents compared to 78.8% previously, indicating no significant shift in the role of lighting or road surface conditions.

Weather

Clear75,771 (59.5%)
↓-12.5%prior 86,613
Clear/Clear17,934 (14.1%)
↑74.7%prior 10,267
Cloudy9,077 (7.1%)
↓-13.7%prior 10,519
Rain6,811 (5.3%)
↓-17.3%prior 8,233
Snow2,760 (2.2%)
↓-0.9%prior 2,786
Cloudy/Rain2,139 (1.7%)
↓-20.7%prior 2,698
Clear/Cloudy1,960 (1.5%)
↓-2.4%prior 2,008
Rain/Cloudy1,619 (1.3%)
↑23.9%prior 1,307
Rain/Rain1,288 (1.0%)
↑76.4%prior 730
Cloudy/Cloudy1,280 (1.0%)
↑71.6%prior 746

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash

Lighting

Daylight88,236 (69.1%)
↓-3.8%prior 91,687
Dark - lighted roadway24,916 (19.5%)
↓-8.1%prior 27,121
Dark - roadway not lighted7,326 (5.7%)
↓-6.1%prior 7,802
Dusk3,736 (2.9%)
↓-2.8%prior 3,842
Dawn2,392 (1.9%)
↓-1.7%prior 2,433
Dark - unknown roadway lighting872 (0.7%)
↓-6.4%prior 932
Other166 (0.1%)
↑8.5%prior 153
Reported but invalid1 (0.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field

Road Surface

Dry101,418 (79.9%)
↓-5.0%prior 106,775
Wet17,745 (14.0%)
↓-8.2%prior 19,323
Snow4,573 (3.6%)
↑6.0%prior 4,316
Ice2,301 (1.8%)
↑31.6%prior 1,748
Slush525 (0.4%)
↓-25.0%prior 700
Sand, mud, dirt, oil, gravel221 (0.2%)
↓-4.3%prior 231
Other131 (0.1%)
↑77.0%prior 74
Water (standing, moving)66 (0.1%)
↓-35.9%prior 103
Reported but invalid1 (0.0%)
↓-85.7%prior 7

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field

Vehicles & Demographics

Vehicle and person demographics involved in crashes saw minimal changes. The top three vehicle makes involved in collisions were Toyota, Honda, and Ford in both years, with their involvement decreasing in line with the overall crash reduction. The age distribution of persons in crashes also remained stable; for example, the 16-20 age group accounted for 9.5% of individuals in both periods.

Top Vehicle Makes (240,739 vehicles)

1
TOYOTA39,850 (16.6%)
↓-5.7%prior 42,242
2
HONDA31,300 (13%)
↓-4.1%prior 32,623
3
FORD24,245 (10.1%)
↓-6.5%prior 25,944
4
CHEVROLET16,188 (6.7%)
↓-6.4%prior 17,299
5
NISSAN14,243 (5.9%)
↓-7.8%prior 15,451
6
JEEP10,801 (4.5%)
↓-6.4%prior 11,542
7
SUBARU10,175 (4.2%)
↓-2.0%prior 10,384
8
HYUNDAI9,748 (4%)
↓-2.1%prior 9,957
9
KIA5,923 (2.5%)
↓-3.1%prior 6,111
10
GMC5,252 (2.2%)
↓-3.8%prior 5,461

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records

32,576 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (264,163 persons with recorded sex)

Male151,427 (57.3%)
↓-4.4%prior 158,320
Female112,630 (42.6%)
↓-5.4%prior 119,016
X / Unspecified106 (0.0%)
↑34.2%prior 79

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Person-level records linked to crash events

Speed Limit Zones

There was a notable shift in where crashes occurred based on posted speed limits. The number of crashes in 25 mph zones increased by 9.1% (from 28,875 to 31,505), while crashes in 30 mph zones decreased by 10.9% (from 35,947 to 32,024). Although the count of crashes in 65 mph zones fell, the fatality rate for crashes in that zone increased from 0.451% to 0.646%.

Fatal crashes by zone: 15 mph: 5 of 2,197 (0.228%) · 20 mph: 1 of 3,708 (0.027%) · 25 mph: 55 of 31,505 (0.175%) · 30 mph: 72 of 32,024 (0.225%) · 35 mph: 49 of 15,642 (0.313%) · 40 mph: 43 of 9,448 (0.455%) · 45 mph: 19 of 4,350 (0.437%) · 50 mph: 8 of 2,870 (0.279%) · 55 mph: 12 of 5,398 (0.222%) · 60 mph: 2 of 506 (0.395%) · 65 mph: 51 of 7,900 (0.646%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · 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: 2025-01-01 through 2025-12-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2025-01-01 through 2025-12-31 (365 days)
  • Geographic scope: massachusetts, MA
  • Total crash records analyzed: 129,276
  • Total persons involved: 298,877
  • Total vehicles involved: 240,739

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: 2025." Published June 21, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/statewide/2025-annual-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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Massachusetts (Statewide) Crash Report — 2025 | ThatCarHitMe.com