Monthly Traffic Safety Analysis

90 CRASHES IN
ATTLEBORO, MA
JANUARY 2023

All metrics benchmarked againstJanuary 2022

The total number of crashes in ATTLEBORO, MA decreased by 10% from 100 in January 2022 to 90 in January 2023. Despite this overall decrease, crashes attributed to speeding saw a significant increase of 300% year-over-year, rising from 1 to 4.

90

-10.0%was 100

Total Crash Events

0

Persons Killed

31

6.9%was 29

Persons Injured

4

-20.0%was 5

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 · 2023-01-01 to 2023-01-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, total crashes in ATTLEBORO decreased by 10%, from 100 in January 2022 to 90 in January 2023. Total injuries, however, increased by 6.9%, rising from 29 to 31 over the same period. Fatalities remained at 0 in both months.

4

Hit-and-Run Crashes — January 2023

-20.0% vs prior (5)

Hit-and-run crashes decreased by 20%, falling from 5 in January 2022 to 4 in January 2023. The hit-and-run crash rate also decreased from 5% to 4.4% year-over-year.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

31

Motorists Injured

Prior: 296.9%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · 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 17 crashes in January 2022 to Tuesday with 21 crashes in January 2023. The peak hour also changed, moving from 11 AM with 11 crashes in January 2022 to 4 PM with 10 crashes in January 2023.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Serious injury crashes increased by 100%, rising from 1 (1% share) in January 2022 to 2 (2.2% share) in January 2023. Minor injury crashes also increased by 44.4%, from 9 (9% share) to 13 (14.4% share) year-over-year. Conversely, possible injury crashes decreased by 50%, falling from 12 (12% share) to 6 (6.7% share).

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes2.2%
100.0%prior 1
Minor Injury13minor injury crashes14.4%
44.4%prior 9
Possible Injury6possible injury crashes6.7%
-50.0%prior 12
No Injury69no injury crashes76.7%
-10.4%prior 77

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Most severe injury per crash record

Top Contributing Factors

Crashes where 'Failed to yield right of way' was a contributing factor increased from 14 in January 2022 to 18 in January 2023, a 28.6% increase in count. 'Followed too closely' crashes increased by 14.3%, from 14 to 16, while 'Inattention' crashes decreased by 16.7%, from 12 to 10. The factor 'Driving too fast for conditions' saw a 200% increase in count, rising from 1 to 3.

Officer-Reported Primary Contributing Cause

Failed to yield right of way18 (20%)28.6%prior 14
Followed too closely16 (17.8%)14.3%prior 14
Inattention10 (11.1%)-16.7%prior 12
No improper driving9 (10%)-30.8%prior 13
Failure to keep in proper lane or running off road8 (8.9%)-33.3%prior 12
Distracted7 (7.8%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner5 (5.6%)
Driving too fast for conditions3 (3.3%)
Disregarded traffic signs, signals, road markings2 (2.2%)
Made an improper turn1 (1.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes on wet road surfaces increased by 125%, from 12 in January 2022 to 27 in January 2023. Concurrently, crashes on snow-covered roads decreased by 100%, falling from 14 to 0. Crashes occurring in daylight decreased from 54 to 45, while those in dark-lighted roadway conditions decreased from 35 to 21.

Weather

Clear29 (32.6%)
-3.3%prior 30
Clear/Clear26 (29.2%)
-39.5%prior 43
Rain6 (6.7%)
Cloudy6 (6.7%)
20.0%prior 5
Rain/Cloudy5 (5.6%)
Cloudy/Cloudy4 (4.5%)
Cloudy/Rain4 (4.5%)
Cloudy/Snow2 (2.2%)
Rain/Snow1 (1.1%)
Clear/Cloudy1 (1.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Weather condition at time of crash

Lighting

Daylight45 (50.0%)
-16.7%prior 54
Dark - lighted roadway21 (23.3%)
-40.0%prior 35
Dark - roadway not lighted16 (17.8%)
220.0%prior 5
Dusk4 (4.4%)
Dark - unknown roadway lighting2 (2.2%)
Dawn2 (2.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Lighting condition field

Road Surface

Dry63 (70.0%)
-6.0%prior 67
Wet27 (30.0%)
125.0%prior 12

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes decreased from 181 in January 2022 to 169 in January 2023. Among top vehicle makes, Toyota crashes increased from 32 to 37, and Honda crashes increased from 21 to 28, while Ford crashes decreased from 24 to 13. The age group with the highest number of persons involved shifted from 26-34 (45 persons) in January 2022 to 26-34 (36 persons) in January 2023, with the 65+ age group seeing an increase from 12 to 28 persons involved.

Top Vehicle Makes (169 vehicles)

1
TOYOTA37 (21.9%)
15.6%prior 32
2
HONDA28 (16.6%)
33.3%prior 21
3
FORD13 (7.7%)
-45.8%prior 24
4
JEEP11 (6.5%)
5
CHEVROLET11 (6.5%)
-21.4%prior 14
6
NISSAN10 (5.9%)
-37.5%prior 16
7
KIA9 (5.3%)
28.6%prior 7
8
HYUNDAI8 (4.7%)
0.0%prior 8
9
VOLKSWAGEN6 (3.6%)
10
MAZDA5 (3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Vehicle unit records

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

Sex Distribution (192 persons with recorded sex)

Male98 (51.0%)
-17.6%prior 119
Female94 (49.0%)
0.0%prior 94

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in the 30 MPH speed limit zone decreased from 37 in January 2022 to 32 in January 2023. Conversely, crashes in the 65 MPH speed limit zone increased from 13 to 18 during the same period. There was no change in the number of crashes in the 40 MPH speed limit zone, remaining at 14 for both periods.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · 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: 2023-01-01 through 2023-01-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-01-01 through 2023-01-31 (31 days)
  • Geographic scope: ATTLEBORO, MA
  • Total crash records analyzed: 90
  • Total persons involved: 210
  • Total vehicles involved: 169

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). "ATTLEBORO, MA Crash Intelligence Report: January 2023." Published June 21, 2026. Reporting period: 2023-01-01 to 2023-01-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/attleboro/january-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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