Monthly Traffic Safety Analysis

23 CRASHES IN
SUTTON, MA
DECEMBER 2024

All metrics benchmarked againstDecember 2023

In December 2024, Sutton experienced 23 crashes, marking a 9.52% increase from the 21 crashes reported in December 2023. A notable shift includes the rise in speeding-related crashes, which increased from 0 in the prior period to 5 in the current period. Additionally, serious injury crashes, which were absent in December 2023, accounted for 1 crash in December 2024.

23

9.5%was 21

Total Crash Events

0

Persons Killed

5

Persons Injured

0

Fatal Crash Events

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. 1 crash with unreported severity is 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

Overall, crash incidents in Sutton saw a slight increase year-over-year, rising by 9.52% from 21 crashes in December 2023 to 23 crashes in December 2024. Despite this rise in total crashes, the number of total injuries remained stable at 5 in both periods. Fatalities remained at 0 in both December 2023 and December 2024.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

5

Motorists Injured

Prior: 50.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes in Sutton shifted between the two periods. The peak day for crashes moved from Wednesday, with 6 incidents in December 2023, to Thursday, with 7 incidents in December 2024. Furthermore, the peak hour for crashes changed significantly from 7a with 3 crashes in the prior period to 4p with 4 crashes in the current period.

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)

Crash Severity Breakdown

Crash severity distributions saw some changes year-over-year, although total fatalities remained at 0 in both December 2023 and December 2024. While total injuries held steady at 5 for both periods, the current period reported 1 serious injury crash, which was not present in the prior period. Minor injury crashes decreased from 4 to 3, while possible injury crashes remained constant at 1.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes4.3%
Minor Injury3minor injury crashes13%
-25.0%prior 4
Possible Injury1possible injury crashes4.3%
0.0%prior 1
No Injury17no injury crashes73.9%
6.3%prior 16

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-12-01 to 2024-12-31 · Most severe injury per crash record

Top Contributing Factors

Several shifts were observed in contributing factors to crashes. 'Driving too fast for conditions' emerged as a significant factor in the current period, accounting for 4 crashes, compared to 0 in the prior period. 'Failure to keep in proper lane or running off road' also saw an increase, rising from 2 crashes to 3 crashes. Conversely, 'No improper driving' decreased from 7 crashes in December 2023 to 6 crashes in December 2024, and 'Followed too closely' decreased from 3 crashes to 2 crashes.

Officer-Reported Primary Contributing Cause

No improper driving6 (26.1%)-14.3%prior 7
Driving too fast for conditions4 (17.4%)
Failure to keep in proper lane or running off road3 (13%)
Disregarded traffic signs, signals, road markings2 (8.7%)
Distracted2 (8.7%)
Followed too closely2 (8.7%)
Glare1 (4.3%)
Exceeded authorized speed limit1 (4.3%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (4.3%)

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

Adverse weather and road conditions played a more prominent role in the current period's crashes. Snow-related weather conditions (Snow, Cloudy/Snow, Snow/Snow) accounted for 6 crashes in December 2024, up from 0 in December 2023. Correspondingly, crashes on snowy, icy, or slushy road surfaces increased from 0 in the prior period to 7 in the current period. Daylight crashes significantly increased from 8 to 20, while crashes occurring in 'Dark - roadway not lighted' conditions were absent in the current period after accounting for 7 incidents previously.

Weather

Clear13 (56.5%)
-7.1%prior 14
Snow3 (13.0%)
Clear/Clear2 (8.7%)
Cloudy/Snow2 (8.7%)
Rain2 (8.7%)
Snow/Snow1 (4.3%)

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

Lighting

Daylight20 (87.0%)
150.0%prior 8
Dark - lighted roadway3 (13.0%)

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

Road Surface

Dry11 (47.8%)
-15.4%prior 13
Wet5 (21.7%)
-28.6%prior 7
Snow3 (13.0%)
Ice2 (8.7%)
Slush2 (8.7%)

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 (33 vehicles)

1
TOYOTA5 (15.2%)
-16.7%prior 6
2
CHEVROLET5 (15.2%)
3
HONDA3 (9.1%)
4
LEXUS3 (9.1%)
5
KIA3 (9.1%)
6
FORD2 (6.1%)
7
GMC2 (6.1%)
8
TRLR1 (3%)
9
ACURA1 (3%)
10
VOLKSWAGEN1 (3%)

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

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

Sex Distribution (31 persons with recorded sex)

Male18 (58.1%)
-21.7%prior 23
Female13 (41.9%)
8.3%prior 12

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

Crash distribution across speed zones saw some changes year-over-year, with no fatal crashes reported in any zone during either period. Crashes in the 40 mph zone increased notably from 5 incidents in December 2023 to 9 incidents in December 2024, becoming the most frequent speed zone for crashes. Additionally, crashes in the 30 mph and 35 mph zones also saw slight increases, rising from 3 to 5 and 5 to 6 respectively. Crashes in the 25 mph, 50 mph, and 65 mph zones, present in the prior period, were not observed in the current period.

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: SUTTON, MA
  • Total crash records analyzed: 23
  • Total persons involved: 35
  • Total vehicles involved: 33

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). "SUTTON, 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/sutton/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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Sutton, MA Crash Report — December 2024 | ThatCarHitMe.com