Monthly Traffic Safety Analysis

30 CRASHES IN
NORTH ATTLEBOROUGH, MA
SEPTEMBER 2023

All metrics benchmarked againstSeptember 2022

NORTH ATTLEBOROUGH experienced a notable decrease in overall crash incidents in September 2023 compared to September 2022. Total crashes fell from 44 to 30, representing a 31.8% reduction. A significant shift was observed in DUI-related crashes, which decreased from 3 incidents in the prior period to 0 in the current period.

30

-31.8%was 44

Total Crash Events

0

Persons Killed

12

-29.4%was 17

Persons Injured

2

-33.3%was 3

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.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates a decrease in crash activity year-over-year. Total crashes decreased by 31.8%, from 44 incidents in September 2022 to 30 incidents in September 2023. Similarly, total injuries decreased by 29.4%, from 17 injured persons to 12.

2

Hit-and-Run Crashes — September 2023

-33.3% vs prior (3)

Hit-and-run crashes decreased from 3 incidents in September 2022 to 2 incidents in September 2023. The hit-and-run rate remained largely stable, decreasing marginally from 6.8% to 6.7% of total crashes.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

12

Motorists Injured

Prior: 15-20.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · 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 in September 2022, which had 13 crashes, to Wednesday in September 2023, which recorded 8 crashes. The peak hour also shifted, from 11 AM with 6 crashes in the prior period to 10 AM with 4 crashes in the current period. Notably, Friday crashes decreased significantly from 13 to 3, while Wednesday crashes increased from 2 to 8.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatalities remained at 0 in both September 2022 and September 2023. Serious injuries increased from 0 in the prior period to 1 in the current period. Possible injuries decreased from 8 to 2, while minor injuries remained constant at 6 crashes in both periods.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes3.3%
Minor Injury6minor injury crashes20%
0.0%prior 6
Possible Injury2possible injury crashes6.7%
-75.0%prior 8
No Injury21no injury crashes70%
-27.6%prior 29

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Most severe injury per crash record

Top Contributing Factors

Among contributing factors, 'No improper driving' saw the largest decrease in count, falling from 11 crashes to 4. 'Inattention' also decreased from 6 crashes to 2, and 'Followed too closely' decreased from 6 crashes to 4. Conversely, 'Failed to yield right of way' increased slightly from 8 crashes to 9, and 'Other improper action' increased from 1 crash to 2.

Officer-Reported Primary Contributing Cause

Failed to yield right of way9 (30%)12.5%prior 8
Followed too closely4 (13.3%)-33.3%prior 6
No improper driving4 (13.3%)-63.6%prior 11
Other improper action2 (6.7%)
Inattention2 (6.7%)-66.7%prior 6
Made an improper turn1 (3.3%)
Fatigued/asleep1 (3.3%)
Glare1 (3.3%)
Driving too fast for conditions1 (3.3%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (3.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The number of crashes occurring in clear weather conditions decreased from 21 to 12, and 'Clear/Clear' conditions decreased from 11 to 5. Conversely, crashes reported in 'Rain' conditions increased from 2 to 8. Crashes on dry road surfaces decreased from 37 to 19, while crashes on wet surfaces increased from 7 to 9, and one crash occurred on a road with standing water in the current period.

Weather

Clear12 (41.4%)
-42.9%prior 21
Rain8 (27.6%)
Clear/Clear5 (17.2%)
-54.5%prior 11
Clear/Cloudy1 (3.4%)
Cloudy1 (3.4%)
-80.0%prior 5
Cloudy/Clear1 (3.4%)
Cloudy/Cloudy1 (3.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Weather condition at time of crash

Lighting

Daylight24 (82.8%)
-20.0%prior 30
Dark - lighted roadway4 (13.8%)
-50.0%prior 8
Dark - unknown roadway lighting1 (3.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Lighting condition field

Road Surface

Dry19 (65.5%)
-48.6%prior 37
Wet9 (31.0%)
28.6%prior 7
Water (standing, moving)1 (3.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes decreased from 79 to 56 year-over-year. The top vehicle makes involved shifted, with Toyota decreasing from 18 vehicles to 7, and Honda decreasing from 12 to 7. Jeep vehicles involved increased from 5 to 8, making it the top make in the current period.

Top Vehicle Makes (56 vehicles)

1
JEEP8 (14.3%)
60.0%prior 5
2
HONDA7 (12.5%)
-41.7%prior 12
3
TOYOTA7 (12.5%)
-61.1%prior 18
4
FORD6 (10.7%)
20.0%prior 5
5
NISSAN6 (10.7%)
-25.0%prior 8
6
HYUNDAI4 (7.1%)
7
CHEVROLET4 (7.1%)
8
MAZDA3 (5.4%)
9
DODGE2 (3.6%)
10
VOLVO1 (1.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Vehicle unit records

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

Sex Distribution (74 persons with recorded sex)

Male39 (52.7%)
-13.3%prior 45
Female35 (47.3%)
-27.1%prior 48

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Person-level records linked to crash events

Speed Limit Zones

Crash counts decreased across all recorded speed zones year-over-year. Crashes in the 30 mph zone decreased from 13 to 6, and in the 40 mph zone from 14 to 12. The 65 mph zone saw a decrease from 7 crashes to 4, and the 20 mph zone decreased from 2 crashes to 1. No fatalities were recorded in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · 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: 2023-09-01 through 2023-09-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-09-01 through 2023-09-30 (30 days)
  • Geographic scope: NORTH ATTLEBOROUGH, MA
  • Total crash records analyzed: 30
  • Total persons involved: 80
  • Total vehicles involved: 56

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: September 2023." Published June 21, 2026. Reporting period: 2023-09-01 to 2023-09-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/north-attleborough/september-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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North Attleborough, MA Crash Report — September 2023 | ThatCarHitMe.com