Yearly Traffic Safety Analysis

70 CRASHES IN
HAMILTON, MA
2025

All metrics benchmarked against2024

In Hamilton, total vehicle crashes remained stable year-over-year, with 70 crashes in 2025 compared to 71 in 2024, a decrease of 1.4%. While overall injuries fell from 15 to 11, the most notable shift was the occurrence of one fatal crash in 2025, whereas none were recorded in the prior year.

70

-1.4%was 71

Total Crash Events

1

Persons Killed

11

-26.7%was 15

Persons Injured

1

-66.7%was 3

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. 1 crash with unreported severity is 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

Overall crash volume in Hamilton saw a slight decrease of one incident, from 71 in 2024 to 70 in 2025. This period also saw a 26.7% reduction in total injuries, which fell from 15 to 11. However, the city recorded one fatality in 2025, a change from zero fatalities in the previous year.

1

Hit-and-Run Crashes — 2025

-66.7% vs prior (3)

Hit-and-run incidents decreased significantly year-over-year. The number of hit-and-run crashes fell from 3 in 2024 to 1 in 2025. Consequently, the hit-and-run rate dropped from 4.2% of all crashes in the prior year to 1.4% in the current year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

1

Motorists Killed

Prior: 0%

1

Pedestrians Injured

Prior: 0%

3

Cyclists Injured

Prior: 0%

7

Motorists Injured

Prior: 15-53.3%

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

The timing of crashes shifted slightly between the two periods. In 2025, the peak day for crashes was Saturday with 15 incidents, a shift from Friday, which had 15 crashes in 2024. The afternoon commute hour remained the most frequent time for collisions, with the 3 p.m. hour being the peak in both years, though the number of crashes in that hour increased from 7 to 9 year-over-year.

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 worsened in 2025, with one fatal crash accounting for 1.4% of all incidents, compared to zero fatal crashes in 2024. While the prior year included two serious injury crashes, none were recorded in 2025. The proportion of crashes resulting in minor or possible injuries decreased from 11.2% (8 crashes) in 2024 to 11.5% (8 crashes) in 2025, showing a stable rate despite the lower total crash count.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.4%
Minor Injury6minor injury crashes8.6%
50.0%prior 4
Possible Injury2possible injury crashes2.9%
-50.0%prior 4
No Injury60no injury crashes85.7%
3.4%prior 58

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 leading contributing factors cited in crashes saw some changes year-over-year. The count of crashes attributed to 'Inattention' decreased from 10 in 2024 to 6 in 2025. Conversely, the number of incidents where 'No improper driving' was noted as a factor increased from 30 to 39. The counts for 'Distracted' driving and 'Failed to yield right of way' each increased by one, from 3 to 4 incidents respectively.

Officer-Reported Primary Contributing Cause

No improper driving39 (55.7%)30.0%prior 30
Inattention6 (8.6%)-40.0%prior 10
Failed to yield right of way4 (5.7%)
Distracted4 (5.7%)
Failure to keep in proper lane or running off road3 (4.3%)
Other improper action2 (2.9%)
Exceeded authorized speed limit1 (1.4%)
Emotional1 (1.4%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (1.4%)-83.3%prior 6
Physical impairment1 (1.4%)

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

Crashes in 2025 occurred more frequently in clear conditions compared to the prior year. Collisions on dry road surfaces increased from 53 to 61, while crashes on wet or snow-covered roads decreased from 18 to 8. Similarly, crashes during daylight hours rose from 46 to 51, while those in dark but lighted conditions fell from 19 to 13.

Weather

Clear49 (71.0%)
-3.9%prior 51
Cloudy10 (14.5%)
Clear/Cloudy3 (4.3%)
Rain2 (2.9%)
-60.0%prior 5
Rain/Cloudy1 (1.4%)
Snow1 (1.4%)
Clear/Other1 (1.4%)
Cloudy/Rain1 (1.4%)
Other1 (1.4%)

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

Lighting

Daylight51 (73.9%)
10.9%prior 46
Dark - lighted roadway13 (18.8%)
-31.6%prior 19
Dark - roadway not lighted4 (5.8%)
Dusk1 (1.4%)

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

Road Surface

Dry61 (88.4%)
15.1%prior 53
Wet6 (8.7%)
-50.0%prior 12
Snow2 (2.9%)
-66.7%prior 6

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

Vehicles & Demographics

The top vehicle makes involved in crashes remained largely consistent, with Toyota, Ford, and Honda being the top three in both years. Analysis of persons involved shows a significant demographic shift, with the number of individuals aged 65 and older increasing from 23 in 2024 to 35 in 2025. In contrast, involvement for the 35-44 age group decreased from 32 to 15 persons.

Top Vehicle Makes (108 vehicles)

1
TOYOTA17 (15.7%)
0.0%prior 17
2
FORD12 (11.1%)
-20.0%prior 15
3
HONDA12 (11.1%)
9.1%prior 11
4
SUBARU9 (8.3%)
12.5%prior 8
5
JEEP8 (7.4%)
-20.0%prior 10
6
CHEVROLET6 (5.6%)
20.0%prior 5
7
MAZDA5 (4.6%)
8
VOLVO4 (3.7%)
9
VOLKSWAGEN4 (3.7%)
-33.3%prior 6
10
HYUNDAI3 (2.8%)

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

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

Sex Distribution (129 persons with recorded sex)

Male72 (55.8%)
-12.2%prior 82
Female57 (44.2%)
-18.6%prior 70

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 shift in the speed zones where crashes occurred. Collisions in 30 mph zones decreased from 27 incidents in 2024 to 18 in 2025. In contrast, crashes in 35 mph zones increased from 14 to 19. The single fatal crash recorded in 2025 occurred in a 35 mph zone.

Fatal crashes by zone: 35 mph: 1 of 19 (5.263%)

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: HAMILTON, MA
  • Total crash records analyzed: 70
  • Total persons involved: 141
  • Total vehicles involved: 108

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). "HAMILTON, 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/hamilton/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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Hamilton, MA Crash Report — 2025 | ThatCarHitMe.com