Monthly Traffic Safety Analysis

20 CRASHES IN
WINCHENDON, MA
JANUARY 2022

All metrics benchmarked againstJanuary 2021

In January 2022, Winchendon experienced 20 total crashes, a 100% increase from the 10 crashes recorded in January 2021. Total injuries also rose from 4 to 7, representing a 75% increase. The most notable year-over-year shift was the doubling of total crashes.

20

100.0%was 10

Total Crash Events

0

Persons Killed

7

75.0%was 4

Persons Injured

0

Fatal Crash Events

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 · 2022-01-01 to 2022-01-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates a significant increase in crash activity year-over-year, with total crashes rising from 10 in January 2021 to 20 in January 2022. This represents a 100% increase in the number of crashes. Total injuries also increased from 4 to 7, a 75% rise.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

7

Motorists Injured

Prior: 475.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

Temporal patterns shifted notably between the two periods. The peak day for crashes moved from Saturday with 3 crashes in January 2021 to Wednesday with 5 crashes in January 2022. Similarly, the peak hour for crashes changed from 10 p.m. with 1 crash in the prior year to 8 a.m. with 4 crashes in the current year.

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

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

Crash Severity Breakdown

Fatal crashes remained at 0 in both January 2021 and January 2022. The number of serious injuries remained constant at 1 in both periods, while minor injuries increased from 2 to 3. The proportion of crashes resulting in serious injury decreased from 10% (1 of 10) in the prior period to 5% (1 of 20) in the current period.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes5%
0.0%prior 1
Minor Injury3minor injury crashes15%
50.0%prior 2
No Injury16no injury crashes80%
128.6%prior 7

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

Severity Distribution (Crash Events)

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

Top Contributing Factors

Among the common contributing factors, 'Inattention' crashes increased from 1 in January 2021 to 2 in January 2022, while 'Failed to yield right of way' remained at 1 crash in both periods. 'No improper driving' was the leading factor in January 2022 with 7 crashes, but was not listed as a factor in January 2021. Conversely, 'Exceeded authorized speed limit' accounted for 1 crash in January 2021 but was not among the listed factors in January 2022.

Officer-Reported Primary Contributing Cause

No improper driving7 (35%)
Driving too fast for conditions3 (15%)
Disregarded traffic signs, signals, road markings2 (10%)
Inattention2 (10%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner2 (10%)
Failed to yield right of way1 (5%)
Visibility obstructed1 (5%)
Failure to keep in proper lane or running off road1 (5%)
Other improper action1 (5%)

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

Road & Environmental Conditions

Crashes occurring in clear weather conditions increased from 4 in January 2021 to 13 in January 2022. Crashes on dry road surfaces also increased, from 6 to 9, while those on icy road surfaces rose from 0 to 4. Crashes occurring during daylight hours saw a substantial increase from 5 to 17.

Weather

Clear13 (65.0%)
Cloudy/Rain4 (20.0%)
Cloudy/Snow2 (10.0%)
Cloudy1 (5.0%)

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

Lighting

Daylight17 (85.0%)
240.0%prior 5
Dark - lighted roadway2 (10.0%)
Dusk1 (5.0%)

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

Road Surface

Dry9 (45.0%)
50.0%prior 6
Ice4 (20.0%)
Wet4 (20.0%)
Snow2 (10.0%)
Slush1 (5.0%)

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

Vehicles & Demographics

Top Vehicle Makes (38 vehicles)

1
TOYOTA7 (18.4%)
2
CHEVROLET5 (13.2%)
3
FORD4 (10.5%)
-20.0%prior 5
4
GMC3 (7.9%)
5
MAZDA3 (7.9%)
6
HYUNDAI2 (5.3%)
7
DODGE2 (5.3%)
8
LEX2 (5.3%)
9
JPEG2 (5.3%)
10
CHRYSLER1 (2.6%)

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

Sex Distribution (49 persons with recorded sex)

Male27 (55.1%)
125.0%prior 12
Female22 (44.9%)
175.0%prior 8

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

Speed Limit Zones

Crashes in 30 mph speed zones increased from 3 in January 2021 to 7 in January 2022, while crashes in 40 mph zones rose from 1 to 2. Crashes in 35 mph zones decreased from 2 to 1, and 50 mph zones remained stable with 4 crashes in both periods. Additionally, crashes occurred in 5 mph, 15 mph, 25 mph, and 45 mph zones in January 2022, which were not recorded in January 2021.

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

Data Coverage

  • Reporting period: 2022-01-01 through 2022-01-31 (31 days)
  • Geographic scope: WINCHENDON, MA
  • Total crash records analyzed: 20
  • Total persons involved: 49
  • Total vehicles involved: 38

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). "WINCHENDON, MA Crash Intelligence Report: January 2022." Published June 21, 2026. Reporting period: 2022-01-01 to 2022-01-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/winchendon/january-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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Winchendon, MA Crash Report — January 2022 | ThatCarHitMe.com