Monthly Traffic Safety Analysis

45 CRASHES IN
SHARON, MA
OCTOBER 2025

All metrics benchmarked againstOctober 2024

In October 2025, SHARON experienced 45 crashes, a 25% increase from the 36 crashes reported in October 2024. Total injuries rose significantly from 7 to 19, marking a substantial increase in injury-involved crashes year-over-year.

45

25.0%was 36

Total Crash Events

0

Persons Killed

19

171.4%was 7

Persons Injured

0

-100.0%was 2

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 · 2025-10-01 to 2025-10-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash activity in SHARON showed an upward trend year-over-year, with total crashes increasing from 36 in October 2024 to 45 in October 2025, a 25% rise. Concurrently, the number of injured persons more than doubled, climbing from 7 to 19.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

1

Cyclists Injured

Prior: 0%

18

Motorists Injured

Prior: 7157.1%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · 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 Tuesday in October 2024, with 8 crashes, to Thursday in October 2025, which recorded 12 crashes. The peak hour also changed, moving from 5 PM with 4 crashes in the prior period to 4 PM with 6 crashes in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While both periods reported no fatal crashes, the proportion of crashes resulting in injuries significantly increased. In October 2025, 19 out of 45 crashes (42.2%) involved injuries, compared to 7 out of 36 crashes (19.4%) in October 2024. Minor injuries (Severity B) rose from 4 to 10, and possible injuries (Severity C) increased from 3 to 6.

Outcome by Severity (Crash Events)

Minor Injury10minor injury crashes22.2%
150.0%prior 4
Possible Injury6possible injury crashes13.3%
100.0%prior 3
No Injury28no injury crashes62.2%
-3.4%prior 29

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Most severe injury per crash record

Top Contributing Factors

Followed too closely became the leading contributing factor in October 2025, with 15 occurrences, up from 7 in October 2024, representing a 114.3% increase in count. Conversely, No improper driving decreased from 10 occurrences to 8, while Failed to yield right of way also saw a decrease in count from 6 to 3. Inattention appeared as a significant factor in the current period with 5 crashes, not being among the top factors in the prior period.

Officer-Reported Primary Contributing Cause

Followed too closely15 (33.3%)114.3%prior 7
No improper driving8 (17.8%)-20.0%prior 10
Inattention5 (11.1%)
Failure to keep in proper lane or running off road4 (8.9%)
Driving too fast for conditions3 (6.7%)
Failed to yield right of way3 (6.7%)-50.0%prior 6
Operating defective equipment1 (2.2%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (2.2%)
Wrong side or wrong way1 (2.2%)
Disregarded traffic signs, signals, road markings1 (2.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in rainy conditions increased notably, from 1 in October 2024 to 10 in October 2025. Similarly, crashes on wet road surfaces rose from 4 to 12 year-over-year. The number of crashes occurring in dark conditions (not lighted, lighted, or unknown roadway lighting) also increased from 5 to 11.

Weather

Clear14 (31.1%)
-44.0%prior 25
Clear/Clear12 (26.7%)
140.0%prior 5
Clear/Unknown6 (13.3%)
Rain5 (11.1%)
Rain/Cloudy3 (6.7%)
Cloudy2 (4.4%)
-60.0%prior 5
Rain/Severe crosswinds1 (2.2%)
Cloudy/Rain1 (2.2%)
Rain/Rain1 (2.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Weather condition at time of crash

Lighting

Daylight33 (73.3%)
17.9%prior 28
Dark - roadway not lighted7 (15.6%)
Dark - lighted roadway3 (6.7%)
Dark - unknown roadway lighting1 (2.2%)
Dawn1 (2.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Lighting condition field

Road Surface

Dry33 (73.3%)
3.1%prior 32
Wet12 (26.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes remained largely consistent, with Toyota and Honda being prominent in both periods; Toyota surpassed Honda as the most frequent make in October 2025 (13 vs 12). There was a noticeable shift in the age distribution of persons involved; the 21-25 age group saw a decrease from 22 to 10 persons, while the 35-44 age group increased from 13 to 20 persons. The 65+ age group also saw an increase from 3 to 12 persons involved.

Top Vehicle Makes (90 vehicles)

1
TOYOTA13 (14.4%)
18.2%prior 11
2
HONDA12 (13.3%)
-7.7%prior 13
3
SUBARU8 (8.9%)
33.3%prior 6
4
FORD7 (7.8%)
5
CHEVROLET6 (6.7%)
6
HYUNDAI6 (6.7%)
7
NISSAN6 (6.7%)
20.0%prior 5
8
JEEP4 (4.4%)
-50.0%prior 8
9
VOLKSWAGEN3 (3.3%)
10
LEXUS3 (3.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Vehicle unit records

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

Sex Distribution (104 persons with recorded sex)

Male70 (67.3%)
40.0%prior 50
Female34 (32.7%)
3.0%prior 33

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Person-level records linked to crash events

Speed Limit Zones

The highest number of crashes in October 2024 was in the 65 mph speed zone with 12 crashes, which decreased to 10 crashes in October 2025. Crashes in the 35 mph zone saw a notable increase from 8 to 14. No fatal crashes were recorded in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · 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: 2025-10-01 through 2025-10-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2025-10-01 through 2025-10-31 (31 days)
  • Geographic scope: SHARON, MA
  • Total crash records analyzed: 45
  • Total persons involved: 105
  • Total vehicles involved: 90

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: October 2025." Published June 21, 2026. Reporting period: 2025-10-01 to 2025-10-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/sharon/october-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 — October 2025 | ThatCarHitMe.com