Monthly Traffic Safety Analysis

18 CRASHES IN
FREETOWN, MA
FEBRUARY 2023

All metrics benchmarked againstFebruary 2022

In February 2023, Freetown experienced 18 total crashes, a decrease of 14.3% compared to the 21 crashes recorded in February 2022. Despite the reduction in total crashes, total injuries increased by 80%, from 5 in the prior year to 9 in the current year. A notable shift was the emergence of 2 hit-and-run crashes and 1 DUI crash in the current period, both of which had zero occurrences in the prior year.

18

-14.3%was 21

Total Crash Events

0

Persons Killed

9

80.0%was 5

Persons Injured

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 · 2023-02-01 to 2023-02-28 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend shows a decrease in total crashes year-over-year, with a 14.3% reduction from 21 crashes in February 2022 to 18 crashes in February 2023. However, total injuries saw a significant increase of 80%, rising from 5 to 9 during the same period. This suggests that while crash frequency decreased, the severity of outcomes for individuals involved in crashes increased.

2

Hit-and-Run Crashes — February 2023

11.1% hit-and-run rate this period vs 0.0% prior. Prior period: 0.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

9

Motorists Injured

Prior: 580.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · 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 February 2022, which had 9 crashes, to Saturday in February 2023, with 3 crashes. The peak hour also changed from 7 AM with 4 crashes in the prior period to 8 AM with 3 crashes in the current period. Crashes on Fridays decreased from 9 to 3, while crashes on Saturdays increased from 0 to 3.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There were no fatalities reported in either February 2022 or February 2023. However, the number of injury crashes increased from 2 in February 2022 to 7 in February 2023, raising the proportion of injury crashes from 9.5% to 38.9% of all crashes. Additionally, 1 serious injury crash (Severity A) occurred in the current period, compared to none in the prior period.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes5.6%
Minor Injury4minor injury crashes22.2%
300.0%prior 1
Possible Injury2possible injury crashes11.1%
100.0%prior 1
No Injury10no injury crashes55.6%
-44.4%prior 18

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Most severe injury per crash record

Top Contributing Factors

The contributing factor 'No improper driving' decreased from 6 crashes in February 2022 to 4 crashes in February 2023. 'Driving too fast for conditions', which accounted for 6 crashes in the prior period, was not reported in the current period, while 'Failure to keep in proper lane or running off road' increased from 0 to 4 crashes. Crashes attributed to 'Exceeded authorized speed limit' increased from 1 to 2, and 'Fatigued/asleep' crashes increased from 0 to 2.

Officer-Reported Primary Contributing Cause

Failure to keep in proper lane or running off road4 (22.2%)
No improper driving4 (22.2%)-33.3%prior 6
Fatigued/asleep2 (11.1%)
Exceeded authorized speed limit2 (11.1%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway1 (5.6%)
Distracted1 (5.6%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (5.6%)
Other improper action1 (5.6%)
Physical impairment1 (5.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear' weather conditions increased from 8 in February 2022 to 10 in February 2023, while snow-related crashes decreased significantly from 8 to 2. The road surface conditions shifted dramatically, with dry road crashes increasing from 6 to 15, and crashes on snow, ice, or slush decreasing from 11 to 0. Crashes in 'Daylight' conditions decreased from 11 to 7, while those in 'Dark - roadway not lighted' conditions increased from 4 to 7.

Weather

Clear10 (58.8%)
25.0%prior 8
Cloudy3 (17.6%)
Snow2 (11.8%)
Cloudy/Rain1 (5.9%)
Rain1 (5.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Weather condition at time of crash

Lighting

Dark - roadway not lighted7 (41.2%)
Daylight7 (41.2%)
-36.4%prior 11
Dawn2 (11.8%)
Dark - lighted roadway1 (5.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Lighting condition field

Road Surface

Dry15 (83.3%)
150.0%prior 6
Wet3 (16.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (24 vehicles)

1
TOYOTA5 (20.8%)
2
HONDA4 (16.7%)
3
NISSAN4 (16.7%)
4
FORD3 (12.5%)
5
CHRYSLER2 (8.3%)
6
CHEVROLET2 (8.3%)
7
FREIGHTLINER1 (4.2%)
8
SUBARU1 (4.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Vehicle unit records

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

Sex Distribution (36 persons with recorded sex)

Male21 (58.3%)
10.5%prior 19
Female15 (41.7%)
7.1%prior 14

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · Person-level records linked to crash events

Speed Limit Zones

Crashes in the 65 mph speed zone decreased from 7 in February 2022 to 6 in February 2023. There were 2 crashes in the 20 mph zone and 1 in the 25 mph zone in the prior period, neither of which had any occurrences in the current period. Conversely, crashes in the 40 mph zone increased from 4 to 5, and in the 35 mph zone from 2 to 3.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-02-01 to 2023-02-28 · 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: 2023-02-01 through 2023-02-28
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-02-01 through 2023-02-28 (28 days)
  • Geographic scope: FREETOWN, MA
  • Total crash records analyzed: 18
  • Total persons involved: 40
  • Total vehicles involved: 24

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). "FREETOWN, MA Crash Intelligence Report: February 2023." Published June 21, 2026. Reporting period: 2023-02-01 to 2023-02-28. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/freetown/february-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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