Monthly Traffic Safety Analysis

36 CRASHES IN
SHARON, MA
FEBRUARY 2025

All metrics benchmarked againstFebruary 2024

In February 2025, SHARON experienced 36 total crashes, an increase of 38.5% compared to the 26 crashes recorded in February 2024. The total number of injuries also rose from 9 to 12 year-over-year. A notable shift was the increase in speeding-related crashes, which jumped from 1 in the prior period to 7 in the current period.

36

38.5%was 26

Total Crash Events

0

Persons Killed

12

33.3%was 9

Persons Injured

1

-66.7%was 3

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. 1 crash with unreported severity is not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates an increase in crash activity year-over-year, with total crashes rising from 26 to 36, representing a 38.5% increase. Similarly, total injuries increased by 33.3%, from 9 to 12. There were no fatalities reported in either period, maintaining a stable fatal crash rate.

1

Hit-and-Run Crashes — February 2025

-66.7% vs prior (3)

Hit-and-run crashes decreased significantly year-over-year, falling from 3 incidents in February 2024 to 1 in February 2025. This reduction also led to a substantial decrease in the hit-and-run rate, which dropped from 11.5% of all crashes in the prior period to 2.8% in the current period.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

12

Motorists Injured

Prior: 933.3%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes shifted year-over-year. In February 2025, the peak crash days were Sunday and Saturday with 7 crashes each, whereas in February 2024, Friday and Thursday were the peak days with 6 crashes each. The peak hour for crashes also changed, moving from 6 PM with 3 crashes in the prior period to 3 PM with 4 crashes in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatal crash rates remained stable at 0 in both February 2025 and February 2024, with no fatalities reported. While serious injury crashes remained at 1 in both periods, their share of total crashes decreased from 3.8% to 2.8%. Possible injury crashes increased in count from 2 to 4, and their share rose from 7.7% to 11.1% of all crashes.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes2.8%
0.0%prior 1
Minor Injury5minor injury crashes13.9%
0.0%prior 5
Possible Injury4possible injury crashes11.1%
100.0%prior 2
No Injury25no injury crashes69.4%
38.9%prior 18

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Most severe injury per crash record

Top Contributing Factors

Contributing factors saw several shifts in crash counts year-over-year. Crashes attributed to 'Driving too fast for conditions' increased significantly from 0 to 6. 'Failure to keep in proper lane or running off road' also rose from 3 to 6 crashes. Conversely, 'Inattention' crashes decreased from 4 to 2, and 'Followed too closely' crashes dropped from 3 to 1.

Officer-Reported Primary Contributing Cause

No improper driving8 (22.2%)0.0%prior 8
Driving too fast for conditions6 (16.7%)
Failure to keep in proper lane or running off road6 (16.7%)
Failed to yield right of way4 (11.1%)
Glare2 (5.6%)
Inattention2 (5.6%)
Fatigued/asleep1 (2.8%)
Followed too closely1 (2.8%)
Other improper action1 (2.8%)
Over-correcting/over-steering1 (2.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Adverse weather and road surface conditions played a more significant role in February 2025 compared to the prior year. Crashes occurring on snow-covered roads increased from 0 to 6, and those on ice-covered roads rose from 0 to 2. In terms of lighting, crashes during daylight hours increased from 17 to 24, while those in unlighted dark conditions rose from 3 to 6.

Weather

Clear18 (50.0%)
-14.3%prior 21
Clear/Clear7 (19.4%)
Snow/Sleet, hail (freezing rain or drizzle)3 (8.3%)
Cloudy2 (5.6%)
Snow2 (5.6%)
Cloudy/Rain1 (2.8%)
Sleet, hail (freezing rain or drizzle)/Snow1 (2.8%)
Snow/Rain1 (2.8%)
Clear/Unknown1 (2.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Weather condition at time of crash

Lighting

Daylight24 (66.7%)
41.2%prior 17
Dark - roadway not lighted6 (16.7%)
Dark - lighted roadway4 (11.1%)
-20.0%prior 5
Dusk2 (5.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Lighting condition field

Road Surface

Dry22 (61.1%)
-12.0%prior 25
Snow6 (16.7%)
Wet4 (11.1%)
Ice2 (5.6%)
Slush2 (5.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (61 vehicles)

1
TOYOTA12 (19.7%)
2
FORD10 (16.4%)
3
HONDA8 (13.1%)
60.0%prior 5
4
CHEVROLET5 (8.2%)
0.0%prior 5
5
NISSAN5 (8.2%)
6
SUBARU4 (6.6%)
7
HYUNDAI3 (4.9%)
8
VOLKSWAGEN2 (3.3%)
9
FIAT AUTO2 (3.3%)
10
JEEP2 (3.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Vehicle unit records

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

Sex Distribution (59 persons with recorded sex)

Male39 (66.1%)
34.5%prior 29
Female20 (33.9%)
-4.8%prior 21

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Person-level records linked to crash events

Speed Limit Zones

Crashes in the 65 mph speed zone increased slightly from 10 to 11 year-over-year. There was also an increase in crashes in the 30 mph zone, rising from 3 to 4, and in the 35 mph zone, from 7 to 8. No fatalities were reported in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · 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: 2025-02-01 through 2025-02-28
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2025-02-01 through 2025-02-28 (28 days)
  • Geographic scope: SHARON, MA
  • Total crash records analyzed: 36
  • Total persons involved: 67
  • Total vehicles involved: 61

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). "SHARON, MA Crash Intelligence Report: February 2025." Published June 21, 2026. Reporting period: 2025-02-01 to 2025-02-28. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/sharon/february-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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Sharon, MA Crash Report — February 2025 | ThatCarHitMe.com