Monthly Traffic Safety Analysis

62 CRASHES IN
MILTON, MA
MAY 2025

All metrics benchmarked againstMay 2024

In May 2025, Milton experienced 62 crashes, a slight decrease from 63 crashes in May 2024. However, total fatalities increased from 0 to 1 during this period, marking a significant shift in crash outcomes. Cyclist fatalities also increased from 0 to 1 year-over-year.

62

▼ -1.6%was 63

Total Crash Events

1

Persons Killed

26

▼ -27.8%was 36

Persons Injured

8

▲ 14.3%was 7

Hit-and-Run Crashes

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 2 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, total crashes in Milton saw a minor decrease of 1.58% year-over-year, from 63 to 62. Despite this slight reduction in crash volume, total fatalities increased from 0 to 1, while total injuries decreased by 27.78%, from 36 to 26.

8

Hit-and-Run Crashes — May 2025

▲ 14.3% vs prior (7)

Hit-and-run crashes increased from 7 in May 2024 to 8 in May 2025. This resulted in an increase in the hit-and-run rate from 11.1% to 12.9% year-over-year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Cyclists Killed

Prior: 0→%

0

Motorists Killed

Prior: 0→0.0%

1

Pedestrians Injured

Prior: 0→%

0

Cyclists Injured

Prior: 0→0.0%

25

Motorists Injured

Prior: 35↓-28.6%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · 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 Friday in May 2024 (13 crashes) to Thursday in May 2025 (11 crashes). Similarly, the peak crash hour moved from 6 PM in May 2024 (8 crashes) to 5 PM in May 2025 (6 crashes).

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatal crashes increased from 0 in May 2024 to 1 in May 2025, raising the fatal crash rate from 0% to 1.6%. Concurrently, total injuries decreased by 27.78%, from 36 to 26, with possible injury crashes notably dropping from 12 to 6.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.6%
Minor Injury14minor injury crashes22.6%
↑7.7%prior 13
Possible Injury6possible injury crashes9.7%
↓-50.0%prior 12
No Injury39no injury crashes62.9%
↑5.4%prior 37

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Most severe injury per crash record

Top Contributing Factors

The contributing factor 'No improper driving' decreased in count from 18 to 16 crashes year-over-year. Conversely, 'Followed too closely' crashes increased from 8 to 11, and 'Disregarded traffic signs, signals, road markings' increased from 1 to 4 crashes.

Officer-Reported Primary Contributing Cause

No improper driving16 (25.8%)↓-11.1%prior 18
Followed too closely11 (17.7%)↑37.5%prior 8
Failed to yield right of way6 (9.7%)→0.0%prior 6
Disregarded traffic signs, signals, road markings4 (6.5%)
Exceeded authorized speed limit4 (6.5%)
Inattention3 (4.8%)↓-66.7%prior 9
Made an improper turn2 (3.2%)
Failure to keep in proper lane or running off road2 (3.2%)
Operating defective equipment2 (3.2%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (1.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in clear weather conditions decreased from 44 in May 2024 to 39 in May 2025, while crashes in rainy conditions decreased from 11 to 9. Crashes during daylight hours also decreased from 46 to 40 year-over-year, with dry road crashes decreasing from 48 to 43.

Weather

Clear/Clear25 (43.1%)
↑38.9%prior 18
Clear14 (24.1%)
↓-46.2%prior 26
Rain/Rain6 (10.3%)
Cloudy/Cloudy6 (10.3%)
Cloudy/Rain2 (3.4%)
Rain2 (3.4%)
↓-71.4%prior 7
Rain/Fog, smog, smoke1 (1.7%)
Cloudy1 (1.7%)
Unknown/Unknown1 (1.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Weather condition at time of crash

Lighting

Daylight40 (69.0%)
↓-13.0%prior 46
Dark - lighted roadway12 (20.7%)
↓-7.7%prior 13
Dark - roadway not lighted3 (5.2%)
Dusk3 (5.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Lighting condition field

Road Surface

Dry43 (75.4%)
↓-10.4%prior 48
Wet14 (24.6%)
→0.0%prior 14

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Road surface condition field

Vehicles & Demographics

The leading vehicle make involved in crashes shifted, with Ford increasing from 8 to 20 incidents, while Toyota decreased from 22 to 14. In terms of persons involved, the 26-34 age group saw an increase from 28 to 35 individuals, whereas the 35-44 age group decreased from 34 to 24. Male persons involved decreased from 85 to 82, and female persons involved decreased from 61 to 52.

Top Vehicle Makes (130 vehicles)

1
FORD20 (15.4%)
↑150.0%prior 8
2
TOYOTA14 (10.8%)
↓-36.4%prior 22
3
HONDA12 (9.2%)
↑20.0%prior 10
4
HYUNDAI8 (6.2%)
↑14.3%prior 7
5
CHEVROLET8 (6.2%)
↑33.3%prior 6
6
VOLKSWAGEN6 (4.6%)
7
JEEP6 (4.6%)
↓-14.3%prior 7
8
BMW4 (3.1%)
9
AUDI4 (3.1%)
10
MERCEDES-BENZ4 (3.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Vehicle unit records

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

Sex Distribution (134 persons with recorded sex)

Male82 (61.2%)
↓-3.5%prior 85
Female52 (38.8%)
↓-14.8%prior 61

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes occurring in 55 mph speed zones slightly decreased from 19 in May 2024 to 17 in May 2025. Crashes in 30 mph zones saw a decrease from 9 to 3, while crashes in 35 mph zones increased from 4 to 8. No fatal crashes were recorded within any specific speed limit zone in either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · 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: 2025-05-01 through 2025-05-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2025-05-01 through 2025-05-31 (31 days)
  • Geographic scope: MILTON, MA
  • Total crash records analyzed: 62
  • Total persons involved: 159
  • Total vehicles involved: 130

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). "MILTON, MA Crash Intelligence Report: May 2025." Published June 21, 2026. Reporting period: 2025-05-01 to 2025-05-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/milton/may-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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Milton, MA Crash Report — May 2025 | ThatCarHitMe.com