Monthly Traffic Safety Analysis

6 CRASHES IN
ACUSHNET, MA
DECEMBER 2024

All metrics benchmarked againstDecember 2023

In December 2024, ACUSHNET recorded 6 crashes, a substantial decrease compared to the 16 crashes reported in December 2023. This represents a 62.5% reduction in total crashes year-over-year. A notable shift is the absence of any reported injuries or fatalities in December 2024, compared to 6 injuries in the prior year.

6

-62.5%was 16

Total Crash Events

0

Persons Killed

0

-100.0%was 6

Persons Injured

0

-100.0%was 1

Hit-and-Run Crashes

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 6 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates a significant decrease in crash activity in ACUSHNET year-over-year. Total crashes fell by 62.5%, from 16 in December 2023 to 6 in December 2024. Furthermore, total injuries dropped from 6 in December 2023 to 0 in December 2024.

When Crashes Happen

The temporal distribution of crashes shifted significantly year-over-year. In December 2024, the peak day for crashes was Monday with 3 incidents, whereas in December 2023, Tuesday saw the highest count with 4 crashes. The peak hour for crashes also changed, moving from 5 p.m. with 3 crashes in December 2023 to 8 p.m. with 1 crash in December 2024.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)

Top Contributing Factors

The leading contributing factor, 'No improper driving,' decreased from 8 crashes in December 2023 to 3 crashes in December 2024, a 62.5% reduction in count, while maintaining a 50% share of all factors in both periods. Factors like 'Driving too fast for conditions' and 'Inattention' remained at 1 crash each year, but their share of total crashes increased from 6.3% to 16.7% due to the overall decrease in crash volume. Several factors present in the prior year, such as 'Failed to yield right of way' and 'Distracted,' were not reported in December 2024.

Officer-Reported Primary Contributing Cause

No improper driving3 (50%)-62.5%prior 8
Driving too fast for conditions1 (16.7%)
Inattention1 (16.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Regarding weather conditions, crashes in clear weather decreased from 10 in December 2023 to 3 in December 2024, while crashes in rainy conditions remained at 1 for both periods. For road surface conditions, crashes on dry roads significantly decreased from 14 in December 2023 to 2 in December 2024, but crashes on wet roads increased from 1 to 3 year-over-year. In terms of lighting, crashes during daylight hours decreased from 7 to 3, and crashes in dark-lighted roadway conditions decreased from 5 to 2.

Weather

Clear3 (50.0%)
-70.0%prior 10
Cloudy2 (33.3%)
Rain1 (16.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · Weather condition at time of crash

Lighting

Daylight3 (50.0%)
-57.1%prior 7
Dark - lighted roadway2 (33.3%)
-60.0%prior 5
Dark - roadway not lighted1 (16.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · Lighting condition field

Road Surface

Wet3 (50.0%)
Dry2 (33.3%)
-85.7%prior 14
Ice1 (16.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (8 vehicles)

1
CHEVROLET3 (37.5%)
2
BUICK1 (12.5%)
3
CHRYSLER1 (12.5%)
4
GMC1 (12.5%)
5
TOYOTA1 (12.5%)
-83.3%prior 6
6
VOLKSWAGEN1 (12.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · Vehicle unit records

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

Sex Distribution (6 persons with recorded sex)

Female3 (50.0%)
-78.6%prior 14
Male3 (50.0%)
-66.7%prior 9

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · Person-level records linked to crash events

Speed Limit Zones

The distribution of crashes across speed zones changed year-over-year, with the 35 mph zone being the only common category. Crashes in the 35 mph speed zone decreased from 5 in December 2023 to 2 in December 2024, representing a 60% reduction. December 2024 saw 4 crashes in the 25 mph zone, a category not present in the prior year's data, while crashes in the 30 mph and 40 mph zones, which accounted for 3 and 7 crashes respectively in December 2023, were not reported in December 2024.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · 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: 2024-12-01 through 2024-12-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2024-12-01 through 2024-12-31 (31 days)
  • Geographic scope: ACUSHNET, MA
  • Total crash records analyzed: 6
  • Total persons involved: 8
  • Total vehicles involved: 8

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). "ACUSHNET, MA Crash Intelligence Report: December 2024." Published June 21, 2026. Reporting period: 2024-12-01 to 2024-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/acushnet/december-2024-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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Acushnet, MA Crash Report — December 2024 | ThatCarHitMe.com