Monthly Traffic Safety Analysis

84 CRASHES IN
ATTLEBORO, MA
MARCH 2022

All metrics benchmarked againstMarch 2021

ATTLEBORO experienced a decrease in overall crashes in March 2022 compared to March 2021, with total crashes falling from 104 to 84, a 19.23% reduction. The most notable year-over-year shift was a significant 66.7% decrease in hit-and-run crashes, from 9 to 3 incidents.

84

-19.2%was 104

Total Crash Events

0

Persons Killed

20

-50.0%was 40

Persons Injured

3

-66.7%was 9

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. 2 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crashes in ATTLEBORO are trending downward year-over-year. Total crashes decreased by 19.23%, from 104 in March 2021 to 84 in March 2022. Concurrently, total injuries saw a 50% reduction, falling from 40 to 20.

3

Hit-and-Run Crashes — March 2022

-66.7% vs prior (9)

Hit-and-run crashes significantly decreased from 9 in March 2021 to 3 in March 2022, representing a 66.7% reduction in count. The hit-and-run rate also decreased from 8.7% of total crashes in March 2021 to 3.6% in March 2022.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

20

Motorists Injured

Prior: 40-50.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · 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 Wednesday, with 22 crashes in March 2021, to Friday, with 20 crashes in March 2022. The peak crash hour remained 4 PM in both periods, with 14 crashes in March 2021 and 16 crashes in March 2022.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There were no fatal crashes in either March 2021 or March 2022. Total injuries decreased from 40 to 20 between the two periods. The number of 'Possible Injury' crashes decreased from 17 (16.3% of crashes) to 6 (7.1% of crashes).

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes1.2%
0.0%prior 1
Minor Injury8minor injury crashes9.5%
-20.0%prior 10
Possible Injury6possible injury crashes7.1%
-64.7%prior 17
No Injury67no injury crashes79.8%
-9.5%prior 74

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Most severe injury per crash record

Top Contributing Factors

The top contributing factor, 'Inattention,' decreased in count from 23 crashes in March 2021 to 18 crashes in March 2022. 'Followed too closely' remained stable at 12 crashes in both periods. 'Failed to yield right of way' saw an increase in count from 9 to 10 crashes, while 'Failure to keep in proper lane or running off road' decreased from 14 to 10 crashes.

Officer-Reported Primary Contributing Cause

Inattention18 (21.4%)-21.7%prior 23
Followed too closely12 (14.3%)0.0%prior 12
No improper driving10 (11.9%)-9.1%prior 11
Failed to yield right of way10 (11.9%)11.1%prior 9
Failure to keep in proper lane or running off road10 (11.9%)-28.6%prior 14
Disregarded traffic signs, signals, road markings4 (4.8%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner3 (3.6%)-40.0%prior 5
Driving too fast for conditions3 (3.6%)
Distracted2 (2.4%)
Operating defective equipment1 (1.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear' weather conditions decreased from 52 in March 2021 to 27 in March 2022. Similarly, crashes on 'Dry' road surfaces decreased from 93 to 67 incidents. The number of crashes during 'Daylight' hours decreased from 80 to 58, while crashes in 'Dark - lighted roadway' increased from 12 to 15.

Weather

Clear/Clear33 (39.3%)
6.5%prior 31
Clear27 (32.1%)
-48.1%prior 52
Cloudy5 (6.0%)
-16.7%prior 6
Cloudy/Cloudy4 (4.8%)
-33.3%prior 6
Rain/Rain3 (3.6%)
Unknown/Unknown2 (2.4%)
Cloudy/Rain2 (2.4%)
Rain2 (2.4%)
Cloudy/Clear1 (1.2%)
Rain/Cloudy1 (1.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Weather condition at time of crash

Lighting

Daylight58 (69.0%)
-27.5%prior 80
Dark - lighted roadway15 (17.9%)
25.0%prior 12
Dark - roadway not lighted4 (4.8%)
-33.3%prior 6
Dawn3 (3.6%)
Dusk3 (3.6%)
Dark - unknown roadway lighting1 (1.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Lighting condition field

Road Surface

Dry67 (81.7%)
-28.0%prior 93
Wet12 (14.6%)
9.1%prior 11
Ice1 (1.2%)
Slush1 (1.2%)
Snow1 (1.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes decreased from 192 in March 2021 to 155 in March 2022, a reduction of 19.27%. Toyota vehicles involved increased from 28 to 35, becoming the most frequently involved make. Conversely, Subaru saw a notable decrease in involvement from 12 to 2 vehicles.

Top Vehicle Makes (155 vehicles)

1
TOYOTA35 (22.6%)
25.0%prior 28
2
HONDA15 (9.7%)
-25.0%prior 20
3
FORD14 (9%)
-26.3%prior 19
4
NISSAN13 (8.4%)
-27.8%prior 18
5
CHEVROLET13 (8.4%)
-13.3%prior 15
6
HYUNDAI7 (4.5%)
-41.7%prior 12
7
JEEP5 (3.2%)
8
RAM4 (2.6%)
9
VOLKSWAGEN4 (2.6%)
10
KIA3 (1.9%)
-50.0%prior 6

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Vehicle unit records

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

Sex Distribution (166 persons with recorded sex)

Male91 (54.8%)
-24.8%prior 121
Female75 (45.2%)
-20.2%prior 94

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 30 mph zones increased from 28 in March 2021 to 32 in March 2022. Conversely, crashes in 40 mph zones decreased from 13 to 8. Crashes in 65 mph zones remained relatively stable, decreasing slightly from 18 to 17.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · 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: 2022-03-01 through 2022-03-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2022-03-01 through 2022-03-31 (31 days)
  • Geographic scope: ATTLEBORO, MA
  • Total crash records analyzed: 84
  • Total persons involved: 189
  • Total vehicles involved: 155

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: March 2022." Published June 21, 2026. Reporting period: 2022-03-01 to 2022-03-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/attleboro/march-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 — March 2022 | ThatCarHitMe.com