Monthly Traffic Safety Analysis

91 CRASHES IN
MILFORD, MA
FEBRUARY 2022

All metrics benchmarked againstFebruary 2021

Total crashes in Milford, MA increased by 46.77% year-over-year, rising from 62 crashes in February 2021 to 91 crashes in February 2022. This period saw a notable increase in hit-and-run incidents, which grew from 1 to 9 crashes.

91

46.8%was 62

Total Crash Events

0

Persons Killed

15

25.0%was 12

Persons Injured

9

800.0%was 1

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-02-01 to 2022-02-28 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates a significant increase in crash incidents, with total crashes rising from 62 to 91, marking a 46.77% increase. Injuries also saw an increase, with 15 injuries reported in February 2022 compared to 12 in February 2021, while fatalities remained at zero in both periods.

9

Hit-and-Run Crashes — February 2022

800.0% vs prior (1)

Hit-and-run crashes increased significantly year-over-year, rising from 1 incident in February 2021 to 9 incidents in February 2022. This resulted in the hit-and-run rate increasing from 1.6% to 9.9% of all crashes.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

15

Motorists Injured

Prior: 1136.4%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-02-01 to 2022-02-28 · 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 Wednesday with 15 crashes in February 2021 to Sunday with 22 crashes in February 2022. The peak hour also changed, moving from 3 PM with 9 crashes in the prior period to 6 PM with 11 crashes in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-02-01 to 2022-02-28 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-02-01 to 2022-02-28 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There were no fatal crashes in either February 2021 or February 2022. Total injuries increased from 12 to 15 year-over-year, with minor injuries rising from 4 to 11, while serious injuries decreased from 1 to 0. Crashes resulting in no injury also increased from 46 to 70.

Outcome by Severity (Crash Events)

Minor Injury11minor injury crashes12.1%
175.0%prior 4
Possible Injury4possible injury crashes4.4%
-20.0%prior 5
No Injury70no injury crashes76.9%
52.2%prior 46

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-02-01 to 2022-02-28 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-02-01 to 2022-02-28 · Most severe injury per crash record

Top Contributing Factors

Several contributing factors saw notable increases year-over-year. Crashes attributed to 'Driving too fast for conditions' rose significantly from 2 in February 2021 to 12 in February 2022. 'No improper driving' also increased from 8 crashes to 17 crashes, and 'Failed to yield right of way' increased from 11 to 15 crashes. Conversely, 'Followed too closely' decreased from 7 crashes to 2 crashes.

Officer-Reported Primary Contributing Cause

No improper driving17 (18.7%)112.5%prior 8
Inattention15 (16.5%)25.0%prior 12
Failed to yield right of way15 (16.5%)36.4%prior 11
Driving too fast for conditions12 (13.2%)
Failure to keep in proper lane or running off road8 (8.8%)
Followed too closely2 (2.2%)-71.4%prior 7
Emotional2 (2.2%)
Glare1 (1.1%)
Fatigued/asleep1 (1.1%)
Distracted1 (1.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-02-01 to 2022-02-28 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Snow' weather conditions saw a substantial increase from 5 in February 2021 to 18 in February 2022. Crashes on 'Ice' road surfaces, which were not reported in February 2021, accounted for 17 incidents in February 2022. Additionally, crashes in 'Dark - lighted roadway' conditions more than doubled, rising from 13 to 27.

Weather

Clear40 (44.9%)
0.0%prior 40
Snow18 (20.2%)
260.0%prior 5
Cloudy7 (7.9%)
Sleet, hail (freezing rain or drizzle)5 (5.6%)
Clear/Cloudy5 (5.6%)
Snow/Cloudy2 (2.2%)
Snow/Sleet, hail (freezing rain or drizzle)2 (2.2%)
Rain2 (2.2%)
Cloudy/Rain2 (2.2%)
Clear/Other1 (1.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-02-01 to 2022-02-28 · Weather condition at time of crash

Lighting

Daylight50 (54.9%)
22.0%prior 41
Dark - lighted roadway27 (29.7%)
107.7%prior 13
Dark - roadway not lighted7 (7.7%)
Dusk4 (4.4%)
Dark - unknown roadway lighting3 (3.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-02-01 to 2022-02-28 · Lighting condition field

Road Surface

Dry45 (50.0%)
15.4%prior 39
Ice17 (18.9%)
Wet14 (15.6%)
55.6%prior 9
Snow13 (14.4%)
0.0%prior 13
Slush1 (1.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-02-01 to 2022-02-28 · Road surface condition field

Vehicles & Demographics

The age group 16-20 experienced a significant increase in persons involved in crashes, rising from 6 in February 2021 to 18 in February 2022. The 26-34 age group also saw a substantial rise from 26 to 50 persons involved. In terms of vehicle makes, Honda moved from 3rd to 2nd with an increase from 12 to 20 vehicles, and Chevrolet saw a notable increase from 7 to 18 vehicles involved.

Top Vehicle Makes (166 vehicles)

1
TOYOTA21 (12.7%)
23.5%prior 17
2
HONDA20 (12%)
66.7%prior 12
3
FORD18 (10.8%)
0.0%prior 18
4
CHEVROLET18 (10.8%)
157.1%prior 7
5
NISSAN17 (10.2%)
240.0%prior 5
6
HYUNDAI9 (5.4%)
7
SUBARU6 (3.6%)
8
JEEP5 (3%)
9
ACURA4 (2.4%)
10
GMC4 (2.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-02-01 to 2022-02-28 · Vehicle unit records

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

Sex Distribution (157 persons with recorded sex)

Male81 (51.6%)
17.4%prior 69
Female76 (48.4%)
90.0%prior 40

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-02-01 to 2022-02-28 · Person-level records linked to crash events

Speed Limit Zones

Crashes in the 30 mph speed zone increased from 39 in February 2021 to 52 in February 2022. The 25 mph zone also experienced a rise in crashes, from 4 to 11, and the 65 mph zone saw an increase from 7 to 10 crashes. There were no fatal crashes recorded in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-02-01 to 2022-02-28 · 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: 2022-02-01 through 2022-02-28
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2022-02-01 through 2022-02-28 (28 days)
  • Geographic scope: MILFORD, MA
  • Total crash records analyzed: 91
  • Total persons involved: 188
  • Total vehicles involved: 166

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). "MILFORD, MA Crash Intelligence Report: February 2022." Published June 21, 2026. Reporting period: 2022-02-01 to 2022-02-28. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/milford/february-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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Milford, MA Crash Report — February 2022 | ThatCarHitMe.com