Monthly Traffic Safety Analysis

74 CRASHES IN
ATTLEBORO, MA
JUNE 2022

All metrics benchmarked againstJune 2021

Total crashes in ATTLEBORO, MA decreased by 33.9% year-over-year, from 112 crashes in June 2021 to 74 crashes in June 2022. The most notable shift was the absence of fatalities in June 2022, compared to 1 fatality in June 2021.

74

▼ -33.9%was 112

Total Crash Events

0

▼ -100.0%was 1

Persons Killed

31

▼ -13.9%was 36

Persons Injured

3

▼ -57.1%was 7

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 · 2022-06-01 to 2022-06-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates a decrease in crash incidents, with total crashes falling from 112 to 74, representing a 33.9% reduction. Fatalities also decreased from 1 to 0, and total injuries declined from 36 to 31 year-over-year.

3

Hit-and-Run Crashes — June 2022

▼ -57.1% vs prior (7)

The number of hit-and-run crashes decreased from 7 in June 2021 to 3 in June 2022. The hit-and-run rate also saw a decrease, falling from 6.3% to 4.1% year-over-year.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 1↓-100.0%

31

Motorists Injured

Prior: 36↓-13.9%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes remained Wednesday in both periods, though the count decreased from 26 crashes in June 2021 to 18 crashes in June 2022. The peak crash hour shifted from 3 PM with 13 crashes in the prior period to 4 PM with 8 crashes in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatalities decreased from 1 in June 2021 to 0 in June 2022, and serious injuries (code A) were present with 3 crashes in the prior period but absent in the current period. The proportion of crashes resulting in no injury decreased from 73.2% in June 2021 to 66.2% in June 2022.

Outcome by Severity (Crash Events)

Minor Injury11minor injury crashes14.9%
→0.0%prior 11
Possible Injury14possible injury crashes18.9%
→0.0%prior 14
No Injury49no injury crashes66.2%
↓-40.2%prior 82

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Most severe injury per crash record

Top Contributing Factors

The top contributing factor shifted from 'Inattention' (28 crashes) in the prior period to 'Failed to yield right of way' (19 crashes) in the current period. 'Inattention' crashes decreased by 19, from 28 to 9, while 'Failed to yield right of way' crashes increased by 12, from 7 to 19. 'Followed too closely' crashes decreased from 14 to 9.

Officer-Reported Primary Contributing Cause

Failed to yield right of way19 (25.7%)↑171.4%prior 7
Inattention9 (12.2%)↓-67.9%prior 28
Followed too closely9 (12.2%)↓-35.7%prior 14
No improper driving7 (9.5%)↓-50.0%prior 14
Failure to keep in proper lane or running off road7 (9.5%)↓-41.7%prior 12
Disregarded traffic signs, signals, road markings5 (6.8%)
Other improper action3 (4.1%)
Operating defective equipment3 (4.1%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner3 (4.1%)
Distracted2 (2.7%)↓-66.7%prior 6

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The proportion of crashes occurring on wet road surfaces increased from 7.1% (8 crashes) in June 2021 to 17.6% (13 crashes) in June 2022. Conversely, crashes on dry road surfaces decreased from 92.9% (104 crashes) to 82.4% (61 crashes). The distribution of crashes by lighting conditions remained largely similar, with daylight accounting for 78.6% and 78.4% of crashes in the prior and current periods, respectively.

Weather

Clear/Clear35 (47.3%)
↓-40.7%prior 59
Clear24 (32.4%)
↓-25.0%prior 32
Rain3 (4.1%)
Rain/Cloudy3 (4.1%)
Rain/Rain2 (2.7%)
Cloudy2 (2.7%)
↓-77.8%prior 9
Cloudy/Rain2 (2.7%)
Cloudy/Cloudy1 (1.4%)
Clear/Rain1 (1.4%)
Clear/Cloudy1 (1.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Weather condition at time of crash

Lighting

Daylight58 (78.4%)
↓-34.1%prior 88
Dark - lighted roadway10 (13.5%)
↓-37.5%prior 16
Dark - roadway not lighted5 (6.8%)
↓-16.7%prior 6
Dark - unknown roadway lighting1 (1.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Lighting condition field

Road Surface

Dry61 (82.4%)
↓-41.3%prior 104
Wet13 (17.6%)
↑62.5%prior 8

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes decreased from 194 to 141. Toyota remained the most common vehicle make involved, though its count decreased from 36 to 28. The 16-20 age group saw a decrease from 30 to 23 persons involved, while the 0-15 age group saw an increase from 12 to 18 persons involved.

Top Vehicle Makes (141 vehicles)

1
TOYOTA28 (19.9%)
↓-22.2%prior 36
2
CHEVROLET16 (11.3%)
↓-5.9%prior 17
3
HONDA13 (9.2%)
↓-53.6%prior 28
4
NISSAN10 (7.1%)
↓-16.7%prior 12
5
HYUNDAI10 (7.1%)
↓-16.7%prior 12
6
FORD8 (5.7%)
↓-61.9%prior 21
7
KIA6 (4.3%)
↓-33.3%prior 9
8
SUBARU6 (4.3%)
9
JEEP6 (4.3%)
↓-14.3%prior 7
10
DODGE5 (3.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Vehicle unit records

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

Sex Distribution (181 persons with recorded sex)

Male100 (55.2%)
↓-20.6%prior 126
Female81 (44.8%)
↓-18.2%prior 99

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 30 mph zones decreased from 33 to 26, and the single fatal crash in a 30 mph zone in June 2021 was not present in June 2022. Crashes in 45 mph zones decreased from 10 to 4, while crashes in 35 mph zones increased from 10 to 12. The number of crashes in 65 mph zones remained stable at 13.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · 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: 2022-06-01 through 2022-06-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2022-06-01 through 2022-06-30 (30 days)
  • Geographic scope: ATTLEBORO, MA
  • Total crash records analyzed: 74
  • Total persons involved: 185
  • Total vehicles involved: 141

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: June 2022." Published June 21, 2026. Reporting period: 2022-06-01 to 2022-06-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/attleboro/june-2022-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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Attleboro, MA Crash Report — June 2022 | ThatCarHitMe.com