Monthly Traffic Safety Analysis

32 CRASHES IN
HINGHAM, MA
NOVEMBER 2025

All metrics benchmarked againstNovember 2024

Total crashes in HINGHAM, MA increased by 18.5% year-over-year, from 27 crashes in November 2024 to 32 crashes in November 2025. Despite the increase in total crashes, the number of total injuries decreased significantly by 55.6%, from 9 injuries in the prior period to 4 in the current period. Notably, hit-and-run crashes saw a substantial increase, rising from 1 to 3 incidents.

32

18.5%was 27

Total Crash Events

0

Persons Killed

4

-55.6%was 9

Persons Injured

3

200.0%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. 3 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, total crashes in HINGHAM, MA showed an upward trend, increasing by 18.5% from 27 crashes in November 2024 to 32 crashes in November 2025. Fatalities remained at 0 in both periods. Total injuries decreased by 55.6%, falling from 9 injuries in the prior period to 4 injuries in the current period.

3

Hit-and-Run Crashes — November 2025

200.0% vs prior (1)

Hit-and-run crashes increased significantly year-over-year, rising by 200% from 1 incident in November 2024 to 3 incidents in November 2025. This resulted in an increase in the hit-and-run rate, which climbed from 3.7% in the prior period to 9.4% in the current period.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

4

Motorists Injured

Prior: 8-50.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · 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 Friday with 8 crashes in November 2024 to Tuesday with 7 crashes in November 2025. The peak hour for crashes changed from 1 PM, which had 4 crashes in the prior period, to 3 PM, which also recorded 4 crashes in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatal crashes remained at 0 for both November 2024 and November 2025. Serious injury crashes (severity 'A') decreased from 2 incidents (7.4% of total crashes) in the prior period to 1 incident (3.1% of total crashes) in the current period. While minor injury crashes (severity 'B') remained at 3 incidents, their proportion decreased from 11.1% to 9.4% of total crashes, and possible injury crashes ('C') present in the prior period were absent in the current period.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes3.1%
-50.0%prior 2
Minor Injury3minor injury crashes9.4%
0.0%prior 3
No Injury25no injury crashes78.1%
25.0%prior 20

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factor shifted from 'Operating vehicle in erratic, reckless, careless, negligent or aggressive manner' (7 crashes) in the prior period to 'Inattention' (7 crashes) in the current period. Crashes attributed to 'Inattention' increased by 40% in count, from 5 to 7. 'No improper driving' crashes doubled, increasing by 3 crashes from 3 to 6, and 'Distracted' crashes also doubled, increasing by 1 crash from 1 to 2.

Officer-Reported Primary Contributing Cause

Inattention7 (21.9%)40.0%prior 5
No improper driving6 (18.8%)
Failed to yield right of way3 (9.4%)
Distracted2 (6.3%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway2 (6.3%)
Failure to keep in proper lane or running off road2 (6.3%)
Made an improper turn2 (6.3%)
Fatigued/asleep2 (6.3%)
Illness1 (3.1%)
Followed too closely1 (3.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear' weather conditions increased by 7 incidents, from 19 to 26, while those in 'Rain' decreased by 3 incidents, from 4 to 1. Crashes during 'Daylight' conditions increased by 7 incidents, from 14 to 21, and incidents on 'Dry' road surfaces increased by 8, from 21 to 29. Conversely, crashes on 'Wet' road surfaces decreased by 4 incidents, from 6 to 2.

Weather

Clear26 (81.3%)
36.8%prior 19
Clear/Clear2 (6.3%)
Cloudy2 (6.3%)
Rain1 (3.1%)
Rain/Cloudy1 (3.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Weather condition at time of crash

Lighting

Daylight21 (65.6%)
50.0%prior 14
Dark - lighted roadway7 (21.9%)
-12.5%prior 8
Dusk2 (6.3%)
Dark - roadway not lighted1 (3.1%)
Dark - unknown roadway lighting1 (3.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Lighting condition field

Road Surface

Dry29 (90.6%)
38.1%prior 21
Wet2 (6.3%)
-66.7%prior 6
Sand, mud, dirt, oil, gravel1 (3.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (58 vehicles)

1
TOYOTA11 (19%)
57.1%prior 7
2
FORD5 (8.6%)
-16.7%prior 6
3
SUBARU5 (8.6%)
4
HYUNDAI3 (5.2%)
5
VOLKSWAGEN3 (5.2%)
6
HONDA3 (5.2%)
7
JEEP3 (5.2%)
8
CHEVROLET3 (5.2%)
9
NISSAN2 (3.4%)
-60.0%prior 5
10
DODGE2 (3.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Vehicle unit records

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

Sex Distribution (65 persons with recorded sex)

Male45 (69.2%)
87.5%prior 24
Female20 (30.8%)
-4.8%prior 21

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 15 mph zones increased by 5 incidents, from 1 to 6, and crashes in 40 mph zones increased by 2 incidents, from 3 to 5. Conversely, crashes in 25 mph zones decreased by 4 incidents, from 7 to 3, and those in 35 mph zones decreased by 3 incidents, from 6 to 3. No fatalities were recorded in any speed zone for either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · 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: 2025-11-01 through 2025-11-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2025-11-01 through 2025-11-30 (30 days)
  • Geographic scope: HINGHAM, MA
  • Total crash records analyzed: 32
  • Total persons involved: 72
  • Total vehicles involved: 58

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). "HINGHAM, MA Crash Intelligence Report: November 2025." Published June 21, 2026. Reporting period: 2025-11-01 to 2025-11-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/hingham/november-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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Hingham, MA Crash Report — November 2025 | ThatCarHitMe.com