Monthly Traffic Safety Analysis

7 CRASHES IN
DIGHTON, MA
NOVEMBER 2024

All metrics benchmarked againstNovember 2023

Total crashes in Dighton decreased significantly from 12 in November 2023 to 7 in November 2024, marking a 41.67% reduction year-over-year. Despite this overall decrease, the number of reported injuries increased from 1 to 2. The most notable shift is the substantial reduction in the total number of crash incidents.

7

-41.7%was 12

Total Crash Events

0

Persons Killed

2

100.0%was 1

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 · 2024-11-01 to 2024-11-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crashes in Dighton saw a downward trend, decreasing by 41.67% from 12 crashes in November 2023 to 7 crashes in November 2024. Conversely, the total number of injuries increased from 1 in November 2023 to 2 in November 2024. There were no fatalities recorded in either period.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

2

Motorists Injured

Prior: 1100.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · 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 (2 incidents) in November 2023 to Wednesday (2 incidents) in November 2024, with Monday also recording 2 crashes in the current period. The peak hour for crashes remained consistent at 5 p.m. in both November 2023 and November 2024, each with 3 reported incidents. Crash occurrences during early morning hours, specifically 3 a.m. (2 crashes) and 4 a.m. (1 crash), were observed in November 2024 but not in the prior year.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The proportion of crashes resulting in some form of injury increased from 8.3% (1 minor injury crash) in November 2023 to 14.3% (1 possible injury crash) in November 2024. While crashes with no injury decreased from 11 to 6, the total number of injured persons increased from 1 to 2. Both November 2023 and November 2024 reported zero fatal crashes.

Outcome by Severity (Crash Events)

Possible Injury1possible injury crashes14.3%
No Injury6no injury crashes85.7%
-45.5%prior 11

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Most severe injury per crash record

Top Contributing Factors

Crashes where 'No improper driving' was cited as a contributing factor decreased from 6 in November 2023 to 4 in November 2024. 'Distracted' driving was a factor in 1 crash in November 2024, a category not present in the top factors for November 2023. Factors such as 'Inattention' (2 crashes) and 'Followed too closely' (1 crash) were reported in November 2023 but were not among the leading factors in November 2024.

Officer-Reported Primary Contributing Cause

No improper driving4 (57.1%)-33.3%prior 6
Distracted1 (14.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Dark - lighted roadway' conditions decreased from 6 in November 2023 to 4 in November 2024. Incidents during 'Daylight' hours significantly reduced from 5 in November 2023 to 1 in November 2024, while 'Dark - roadway not lighted' crashes remained at 1 in both periods. Weather and road surface condition data for November 2024 are not available for comparison.

Lighting

Dark - lighted roadway4 (57.1%)
-33.3%prior 6
Dark - roadway not lighted1 (14.3%)
Daylight1 (14.3%)
-80.0%prior 5
Dusk1 (14.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Lighting condition field

Vehicles & Demographics

Top Vehicle Makes (9 vehicles)

1
TOYOTA3 (33.3%)
2
CHEVROLET1 (11.1%)
3
DODGE1 (11.1%)
4
ACURA1 (11.1%)
5
MERCEDES-BENZ1 (11.1%)
6
SUBARU1 (11.1%)
7
HONDA1 (11.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Vehicle unit records

Sex Distribution (13 persons with recorded sex)

Male8 (61.5%)
0.0%prior 8
Female5 (38.5%)
-44.4%prior 9

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 30 mph zones decreased from 2 in November 2023 to 1 in November 2024, and in 40 mph zones from 3 to 1. Incidents in 45 mph zones also saw a reduction from 5 in November 2023 to 2 in November 2024. Notably, 2 crashes occurred in 55 mph zones in November 2024, a speed limit not present in the November 2023 data, while 1 crash in a 35 mph zone in November 2023 was not observed in November 2024.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · 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: 2024-11-01 through 2024-11-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2024-11-01 through 2024-11-30 (30 days)
  • Geographic scope: DIGHTON, MA
  • Total crash records analyzed: 7
  • Total persons involved: 13
  • Total vehicles involved: 9

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). "DIGHTON, MA Crash Intelligence Report: November 2024." Published June 21, 2026. Reporting period: 2024-11-01 to 2024-11-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/dighton/november-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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Dighton, MA Crash Report — November 2024 | ThatCarHitMe.com