Monthly Traffic Safety Analysis

40 CRASHES IN
NORTH ATTLEBOROUGH, MA
NOVEMBER 2024

All metrics benchmarked againstNovember 2023

Total crashes in NORTH ATTLEBOROUGH decreased by 23.08%, from 52 in November 2023 to 40 in November 2024. Total injuries also saw a significant decrease, falling by 58.82% from 17 to 7 over the same period. The hit-and-run crash rate notably increased, rising from 3.8% to 15%.

40

▼ -23.1%was 52

Total Crash Events

0

Persons Killed

7

▼ -58.8%was 17

Persons Injured

6

▲ 200.0%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 · 2024-11-01 to 2024-11-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, NORTH ATTLEBOROUGH experienced a downward trend in crash activity, with total crashes decreasing by 23.08% from 52 to 40. This reduction was accompanied by a substantial 58.82% decrease in total injuries, falling from 17 to 7. Fatalities remained at zero in both November 2023 and November 2024.

6

Hit-and-Run Crashes — November 2024

▲ 200.0% vs prior (2)

Hit-and-run crashes increased from 2 in November 2023 to 6 in November 2024. This change resulted in the hit-and-run crash rate rising from 3.8% to 15%.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 0→0.0%

7

Motorists Injured

Prior: 17↓-58.8%

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 Wednesday in November 2023 (13 crashes) to Thursday in November 2024 (11 crashes). The peak crash hour remained consistent at 5 PM in both periods, though the number of crashes at this hour decreased from 10 in November 2023 to 9 in November 2024.

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

No fatal crashes occurred in either November 2023 or November 2024. The total number of injuries decreased from 17 to 7, representing a 58.82% reduction year-over-year. Minor injuries (severity B) decreased from 9 to 4, and possible injuries (severity C) decreased from 5 to 3.

Outcome by Severity (Crash Events)

Minor Injury4minor injury crashes10%
↓-55.6%prior 9
Possible Injury3possible injury crashes7.5%
↓-40.0%prior 5
No Injury32no injury crashes80%
↓-13.5%prior 37

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 attributed to "Failed to yield right of way" decreased from 14 in November 2023 to 9 in November 2024, a 35.7% reduction in count. Conversely, crashes attributed to "Followed too closely" increased from 6 to 9, a 50% increase in count. "Inattention" also saw a significant decrease, falling from 8 crashes to 1 crash.

Officer-Reported Primary Contributing Cause

Followed too closely9 (22.5%)↑50.0%prior 6
Failed to yield right of way9 (22.5%)↓-35.7%prior 14
No improper driving7 (17.5%)↓-41.7%prior 12
Failure to keep in proper lane or running off road3 (7.5%)
Visibility obstructed1 (2.5%)
Glare1 (2.5%)
Driving too fast for conditions1 (2.5%)
Disregarded traffic signs, signals, road markings1 (2.5%)
Inattention1 (2.5%)↓-87.5%prior 8
Made an improper turn1 (2.5%)

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 "Clear" weather conditions decreased from 26 in November 2023 to 11 in November 2024. Conversely, crashes in "Clear/Clear" conditions increased from 15 to 21. Crashes occurring during "Daylight" decreased from 29 to 20, while crashes in "Dark - lighted roadway" decreased from 17 to 12.

Weather

Clear/Clear21 (52.5%)
↑40.0%prior 15
Clear11 (27.5%)
↓-57.7%prior 26
Rain5 (12.5%)
Cloudy/Clear1 (2.5%)
Cloudy/Cloudy1 (2.5%)
Rain/Cloudy1 (2.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Weather condition at time of crash

Lighting

Daylight20 (50.0%)
↓-31.0%prior 29
Dark - lighted roadway12 (30.0%)
↓-29.4%prior 17
Dark - roadway not lighted5 (12.5%)
↓-16.7%prior 6
Dawn2 (5.0%)
Dusk1 (2.5%)

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

Road Surface

Dry34 (85.0%)
↓-24.4%prior 45
Wet6 (15.0%)
↓-14.3%prior 7

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

Vehicles & Demographics

The total number of vehicles involved in crashes decreased from 95 to 74. TOYOTA, the top make in November 2023 with 13 vehicles, decreased to 10 vehicles, while HONDA vehicles involved increased from 7 to 11. Regarding person demographics, the 21-25 age group saw a decrease in persons involved from 19 to 10, and the 65+ age group also decreased from 17 to 11.

Top Vehicle Makes (74 vehicles)

1
HONDA11 (14.9%)
↑57.1%prior 7
2
TOYOTA10 (13.5%)
↓-23.1%prior 13
3
HYUNDAI6 (8.1%)
→0.0%prior 6
4
NISSAN5 (6.8%)
↓-37.5%prior 8
5
SUBARU5 (6.8%)
↓-16.7%prior 6
6
FORD4 (5.4%)
↓-66.7%prior 12
7
JEEP3 (4.1%)
8
MAZDA3 (4.1%)
9
DODGE3 (4.1%)
10
CHEVROLET2 (2.7%)
↓-75.0%prior 8

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

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

Sex Distribution (89 persons with recorded sex)

Male46 (51.7%)
↓-25.8%prior 62
Female43 (48.3%)
↓-14.0%prior 50

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 20 mph zones significantly decreased from 8 in November 2023 to 1 in November 2024. Conversely, crashes in 30 mph zones increased from 9 to 12. Crashes in 40 mph zones decreased from 12 to 7, and those in 65 mph zones decreased from 6 to 2.

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: NORTH ATTLEBOROUGH, MA
  • Total crash records analyzed: 40
  • Total persons involved: 98
  • Total vehicles involved: 74

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). "NORTH ATTLEBOROUGH, 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/north-attleborough/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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North Attleborough, MA Crash Report — November 2024 | ThatCarHitMe.com