Monthly Traffic Safety Analysis

8,905 CRASHES IN
MASSACHUSETTS, MA
MARCH 2025

All metrics benchmarked againstMarch 2024

In March 2025, there were 8,905 total traffic crashes, a 13.5% decrease from the 10,292 crashes recorded in March 2024. While the overall number of crashes and injuries declined, total fatalities increased from 17 to 21. The most significant year-over-year shift was a substantial reduction in crashes related to speeding, with incidents attributed to 'Driving too fast for conditions' falling from 322 to 99.

8,905

-13.5%was 10,292

Total Crash Events

21

23.5%was 17

Persons Killed

2,766

-10.3%was 3,085

Persons Injured

930

-7.3%was 1,003

Hit-and-Run Crashes

Note: "Persons Killed" (21) counts individual fatalities across all crash events. "Fatal" in the severity table below (19) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 414 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic safety data for March shows a downward trend in the total volume of crashes and injuries compared to the same month last year. Total crashes fell by 13.5%, from 10,292 to 8,905, and total injuries decreased by 10.3%, from 3,085 to 2,766. In contrast to this overall improvement, the number of fatalities rose by 23.5%, increasing from 17 in March 2024 to 21 in March 2025.

930

Hit-and-Run Crashes — March 2025

-7.3% vs prior (1,003)

The total number of hit-and-run incidents decreased from 1,003 in March 2024 to 930 in March 2025. However, due to the larger overall decrease in total crashes, the hit-and-run rate as a proportion of all crashes trended upward. The rate increased from 9.7% in the prior year to 10.4% in the current period.

Vulnerable Road User Casualties

7

Pedestrians Killed

Prior: 540.0%

0

Cyclists Killed

Prior: 00.0%

11

Motorists Killed

Prior: 12-8.3%

3

Other Killed

Prior: 0%

119

Pedestrians Injured

Prior: 140-15.0%

72

Cyclists Injured

Prior: 4560.0%

2,560

Motorists Injured

Prior: 2,877-11.0%

15

Other Injured

Prior: 23-34.8%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak hour for crashes remained consistent year-over-year, occurring at 3 p.m. in both March 2024 (836 crashes) and March 2025 (782 crashes). However, the peak day of the week for crashes shifted. In March 2024, Friday was the day with the most incidents (1,773 crashes), whereas in March 2025, Monday became the peak day with 1,536 crashes.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes decreased, the severity of crashes showed a concerning trend. The fatal crash rate increased from 0.17% of all crashes in March 2024 to 0.21% in March 2025, with total fatalities rising from 17 to 21. The proportion of crashes resulting in any level of injury (Serious, Minor, or Possible) remained stable, accounting for 22.4% of crashes in the prior period and 22.5% in the current period.

Severity is per crash event (most severe injury). 19 fatal crash events resulted in 21 persons killed.

Outcome by Severity (Crash Events)

Fatal19fatal crashes0.2%
11.8%prior 17
Serious Injury143serious injury crashes1.6%
-13.9%prior 166
Minor Injury1,263minor injury crashes14.2%
-7.8%prior 1,370
Possible Injury599possible injury crashes6.7%
-22.2%prior 770
No Injury6,467no injury crashes72.6%
-13.1%prior 7,444

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Most severe injury per crash record

Top Contributing Factors

The top five contributing factors cited in crashes remained the same across both periods, led by 'Inattention' and 'Failed to yield right of way'. While the count for nearly all factors decreased in line with the overall drop in crashes, some saw particularly large reductions. The number of crashes attributed to 'Followed too closely' fell by 22.7% (from 989 to 765). Most notably, crashes related to 'Driving too fast for conditions' decreased by 69.2% in count, from 322 to 99 incidents.

Officer-Reported Primary Contributing Cause

No improper driving2,072 (23.3%)-7.3%prior 2,235
Inattention1,230 (13.8%)-16.0%prior 1,464
Failed to yield right of way1,006 (11.3%)-10.2%prior 1,120
Followed too closely765 (8.6%)-22.6%prior 989
Failure to keep in proper lane or running off road499 (5.6%)-6.4%prior 533
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner298 (3.3%)-10.5%prior 333
Other improper action294 (3.3%)-7.0%prior 316
Disregarded traffic signs, signals, road markings277 (3.1%)-4.5%prior 290
Distracted174 (2%)-25.6%prior 234
Made an improper turn117 (1.3%)-17.0%prior 141

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crash data indicates that road conditions were significantly different between the two periods. In March 2025, 82.4% of crashes occurred on dry roads, a notable increase from 70.6% in March 2024. Correspondingly, the share of crashes happening on wet roads dropped from 25.7% to 14.8%. This suggests that March 2025 experienced drier weather, which is reflected in the lower number of crashes attributed to adverse road surface conditions.

Weather

Clear5,250 (59.7%)
-6.9%prior 5,637
Clear/Clear1,155 (13.1%)
125.1%prior 513
Cloudy800 (9.1%)
-23.5%prior 1,046
Rain568 (6.5%)
-60.1%prior 1,425
Cloudy/Rain192 (2.2%)
-59.0%prior 468
Clear/Cloudy145 (1.7%)
-3.3%prior 150
Rain/Cloudy107 (1.2%)
-45.7%prior 197
Cloudy/Cloudy102 (1.2%)
70.0%prior 60
Rain/Rain86 (1.0%)
0.0%prior 86
Clear/Unknown79 (0.9%)
8.2%prior 73

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Weather condition at time of crash

Lighting

Daylight6,260 (71.2%)
-10.4%prior 6,988
Dark - lighted roadway1,662 (18.9%)
-18.6%prior 2,042
Dark - roadway not lighted420 (4.8%)
-29.6%prior 597
Dusk218 (2.5%)
-25.3%prior 292
Dawn158 (1.8%)
-18.1%prior 193
Dark - unknown roadway lighting65 (0.7%)
12.1%prior 58
Other6 (0.1%)
-57.1%prior 14

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Lighting condition field

Road Surface

Dry7,341 (84.1%)
1.1%prior 7,263
Wet1,319 (15.1%)
-50.0%prior 2,640
Ice34 (0.4%)
-62.2%prior 90
Sand, mud, dirt, oil, gravel16 (0.2%)
-15.8%prior 19
Snow12 (0.1%)
-75.5%prior 49
Other6 (0.1%)
Water (standing, moving)4 (0.0%)
-69.2%prior 13

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Road surface condition field

Vehicles & Demographics

The demographic profile of vehicles involved in crashes remained stable year-over-year. The top five vehicle makes involved in collisions were identical in both March 2024 and March 2025: Toyota, Honda, Ford, Chevrolet, and Nissan, with their respective rankings unchanged. Similarly, the age distribution of all persons involved in crashes showed no significant shifts between the two periods.

Top Vehicle Makes (16,576 vehicles)

1
TOYOTA2,703 (16.3%)
-16.9%prior 3,252
2
HONDA2,214 (13.4%)
-11.5%prior 2,502
3
FORD1,660 (10%)
-11.7%prior 1,881
4
CHEVROLET1,111 (6.7%)
-19.1%prior 1,373
5
NISSAN993 (6%)
-17.6%prior 1,205
6
JEEP752 (4.5%)
-15.8%prior 893
7
HYUNDAI682 (4.1%)
-7.5%prior 737
8
SUBARU663 (4%)
-15.0%prior 780
9
KIA412 (2.5%)
-1.7%prior 419
10
GMC336 (2%)
-15.6%prior 398

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Vehicle unit records

2,292 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (18,294 persons with recorded sex)

Male10,439 (57.1%)
-12.5%prior 11,924
Female7,848 (42.9%)
-12.3%prior 8,948
X / Unspecified7 (0.0%)
40.0%prior 5

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Person-level records linked to crash events

Speed Limit Zones

The majority of crashes in both periods occurred in lower speed zones, with the highest volumes in 25 mph and 30 mph zones. In March 2025, crashes in higher speed zones of 55 mph and 65 mph decreased from the prior year. The zone with the highest number of fatalities shifted, moving from the 25 mph zone (4 fatalities) in 2024 to the 35 mph zone (5 fatalities) in 2025.

Fatal crashes by zone: 15 mph: 1 of 143 (0.699%) · 25 mph: 2 of 2,304 (0.087%) · 30 mph: 4 of 2,238 (0.179%) · 35 mph: 5 of 1,037 (0.482%) · 40 mph: 3 of 585 (0.513%) · 45 mph: 1 of 333 (0.3%) · 55 mph: 1 of 358 (0.279%) · 65 mph: 1 of 450 (0.222%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · 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: 2025-03-01 through 2025-03-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2025-03-01 through 2025-03-31 (31 days)
  • Geographic scope: massachusetts, MA
  • Total crash records analyzed: 8,905
  • Total persons involved: 20,727
  • Total vehicles involved: 16,576

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: March 2025." Published June 21, 2026. Reporting period: 2025-03-01 to 2025-03-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/statewide/march-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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