Monthly Traffic Safety Analysis

29 CRASHES IN
HOLDEN, MA
NOVEMBER 2023

All metrics benchmarked againstNovember 2022

Total crashes in Holden increased by 7.4% from 27 in November 2022 to 29 in November 2023. The most significant shift was a 150% increase in total injuries, rising from 4 to 10. Fatal crashes remained at 0 in both periods.

29

7.4%was 27

Total Crash Events

0

Persons Killed

10

150.0%was 4

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.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crashes in Holden increased by 7.4% year-over-year, from 27 crashes in November 2022 to 29 crashes in November 2023. Concurrently, total injuries rose significantly by 150%, from 4 to 10, while fatalities remained at 0 in both periods.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

10

Motorists Injured

Prior: 4150.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · 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 Tuesday with 8 crashes in November 2022 to Saturday with 7 crashes in November 2023. The peak hour also changed, moving from 9 PM with 3 crashes in the prior period to 9 AM with 6 crashes in the current period. Notably, Saturday crashes increased from 1 to 7, and 9 AM crashes increased from 2 to 6.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Total injuries increased by 150%, from 4 in November 2022 to 10 in November 2023. Serious injuries doubled from 1 to 2, and possible injuries increased from 2 to 4. Crashes resulting in no injury decreased from 85.2% to 75.9% of all crashes.

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes6.9%
100.0%prior 1
Minor Injury1minor injury crashes3.4%
0.0%prior 1
Possible Injury4possible injury crashes13.8%
100.0%prior 2
No Injury22no injury crashes75.9%
-4.3%prior 23

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Most severe injury per crash record

Top Contributing Factors

Crashes attributed to 'No improper driving' decreased by 4, from 13 in November 2022 to 9 in November 2023. Conversely, 'Inattention' increased by 1 crash (from 3 to 4), 'Failure to keep in proper lane or running off road' increased by 2 crashes (from 1 to 3), and 'Failed to yield right of way' increased by 1 crash (from 1 to 2). The factor 'Operating vehicle in erratic, reckless, careless, negligent or aggressive manner' was not present in the prior period's top factors but accounted for 2 crashes in the current period.

Officer-Reported Primary Contributing Cause

No improper driving9 (31%)-30.8%prior 13
Inattention4 (13.8%)
Failure to keep in proper lane or running off road3 (10.3%)
Failed to yield right of way2 (6.9%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner2 (6.9%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway2 (6.9%)
Operating defective equipment1 (3.4%)
Disregarded traffic signs, signals, road markings1 (3.4%)
Over-correcting/over-steering1 (3.4%)
Followed too closely1 (3.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear' weather conditions increased by 11, from 13 in November 2022 to 24 in November 2023. Conversely, crashes in 'Clear/Cloudy' conditions decreased by 5, from 6 to 1, and crashes in 'Snow' conditions decreased from 3 to 0. Regarding lighting, crashes during 'Daylight' increased by 5 (from 15 to 20), while those in 'Dark - roadway not lighted' decreased by 2 (from 4 to 2).

Weather

Clear24 (82.8%)
84.6%prior 13
Cloudy2 (6.9%)
Rain2 (6.9%)
Clear/Cloudy1 (3.4%)
-83.3%prior 6

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Weather condition at time of crash

Lighting

Daylight20 (69.0%)
33.3%prior 15
Dark - lighted roadway6 (20.7%)
-14.3%prior 7
Dark - roadway not lighted2 (6.9%)
Dusk1 (3.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Lighting condition field

Road Surface

Dry26 (89.7%)
13.0%prior 23
Wet3 (10.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (50 vehicles)

1
TOYOTA10 (20%)
42.9%prior 7
2
FORD7 (14%)
-12.5%prior 8
3
HONDA5 (10%)
-28.6%prior 7
4
JEEP4 (8%)
5
NISSAN3 (6%)
6
MAZDA2 (4%)
7
ACURA2 (4%)
8
AUDI2 (4%)
9
CHEVROLET2 (4%)
10
DODGE2 (4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Vehicle unit records

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

Sex Distribution (66 persons with recorded sex)

Female33 (50.0%)
32.0%prior 25
Male33 (50.0%)
0.0%prior 33

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 35 mph zones increased by 3, from 16 in November 2022 to 19 in November 2023. Similarly, crashes in 30 mph zones increased by 3, from 3 to 6. Crashes in 65 mph zones decreased by 1, from 3 to 2, while 25 mph zones maintained 1 crash in both periods.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · 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: 2023-11-01 through 2023-11-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-11-01 through 2023-11-30 (30 days)
  • Geographic scope: HOLDEN, MA
  • Total crash records analyzed: 29
  • Total persons involved: 68
  • Total vehicles involved: 50

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). "HOLDEN, MA Crash Intelligence Report: November 2023." Published June 21, 2026. Reporting period: 2023-11-01 to 2023-11-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/holden/november-2023-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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Holden, MA Crash Report — November 2023 | ThatCarHitMe.com