Monthly Traffic Safety Analysis

44 CRASHES IN
GARDNER, MA
JANUARY 2025

All metrics benchmarked againstJanuary 2024

Total crashes in Gardner decreased by 18.5% from 54 in January 2024 to 44 in January 2025. While fatalities remained at zero in both periods, crashes attributed to 'Driving too fast for conditions' saw a notable reduction from 6 crashes to 1 crash year-over-year.

44

▼ -18.5%was 54

Total Crash Events

0

Persons Killed

4

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.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-01-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash incidents in Gardner decreased year-over-year, with a reduction of 10 crashes, representing an 18.5% decrease from 54 crashes in January 2024 to 44 crashes in January 2025. This indicates a positive trend in overall traffic safety for the period.

2

Hit-and-Run Crashes — January 2025

4.5% hit-and-run rate this period vs 0.0% prior. Prior period: 0.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 0→0.0%

0

Other Killed

Prior: 0→0.0%

3

Motorists Injured

Prior: 3→0.0%

1

Other Injured

Prior: 0→%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-01-31 · 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 Tuesday (12 crashes) in January 2024 to Monday and Thursday (both 9 crashes) in January 2025. Similarly, the peak hour for crashes changed from 4 PM (6 crashes) in the prior period to 1 PM (7 crashes) in the current period, indicating a shift in high-risk times.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-01-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-01-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatalities remained at 0 in both January 2024 and January 2025, and the total number of injuries also stayed constant at 4. However, the distribution of injury severity changed, with 1 serious injury reported in the prior period and none in the current period, while minor injuries increased from 2 to 3.

Outcome by Severity (Crash Events)

Minor Injury3minor injury crashes6.8%
↑50.0%prior 2
Possible Injury1possible injury crashes2.3%
→0.0%prior 1
No Injury40no injury crashes90.9%
↓-18.4%prior 49

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-01-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-01-31 · Most severe injury per crash record

Top Contributing Factors

Crashes attributed to 'Driving too fast for conditions' saw the most significant decrease, dropping from 6 crashes in January 2024 to 1 crash in January 2025. Conversely, 'Failed to yield right of way' increased by 2 crashes, from 6 to 8, and became the second most frequent contributing factor in January 2025, with a 18.2% share of crashes.

Officer-Reported Primary Contributing Cause

Inattention11 (25%)↓-8.3%prior 12
Failed to yield right of way8 (18.2%)↑33.3%prior 6
No improper driving8 (18.2%)↓-20.0%prior 10
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner3 (6.8%)
Followed too closely1 (2.3%)
Glare1 (2.3%)
Driving too fast for conditions1 (2.3%)↓-83.3%prior 6
Made an improper turn1 (2.3%)
Distracted1 (2.3%)
Other improper action1 (2.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-01-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

There was a notable shift towards less adverse conditions in the current period, with dry road surface crashes increasing from 16 to 33, while snow-related road surface crashes decreased from 14 to 6. Crashes occurring during daylight hours also slightly decreased from 33 to 29, while dawn crashes increased from 1 to 4.

Weather

Clear28 (63.6%)
↑12.0%prior 25
Clear/Cloudy6 (13.6%)
Snow5 (11.4%)
↓-61.5%prior 13
Cloudy/Clear1 (2.3%)
Rain/Cloudy1 (2.3%)
Snow/Sleet, hail (freezing rain or drizzle)1 (2.3%)
Clear/Rain1 (2.3%)
Cloudy1 (2.3%)
↓-85.7%prior 7

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-01-31 · Weather condition at time of crash

Lighting

Daylight29 (65.9%)
↓-12.1%prior 33
Dark - lighted roadway7 (15.9%)
↓-41.7%prior 12
Dark - roadway not lighted4 (9.1%)
↓-20.0%prior 5
Dawn4 (9.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-01-31 · Lighting condition field

Road Surface

Dry33 (75.0%)
↑106.3%prior 16
Snow6 (13.6%)
↓-57.1%prior 14
Slush2 (4.5%)
Wet2 (4.5%)
↓-80.0%prior 10
Ice1 (2.3%)
↓-87.5%prior 8

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-01-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes saw a shift, with Chevrolet increasing from 5 to 12 incidents, while Toyota decreased from 14 to 8 and Subaru from 13 to 5. The age group 65+ experienced a doubling of involvement, from 10 persons in January 2024 to 20 persons in January 2025.

Top Vehicle Makes (84 vehicles)

1
CHEVROLET12 (14.3%)
↑140.0%prior 5
2
HONDA9 (10.7%)
→0.0%prior 9
3
TOYOTA8 (9.5%)
↓-42.9%prior 14
4
FORD8 (9.5%)
↓-11.1%prior 9
5
JEEP6 (7.1%)
↓-14.3%prior 7
6
NISSAN6 (7.1%)
7
SUBARU5 (6%)
↓-61.5%prior 13
8
HYUNDAI5 (6%)
9
DODGE5 (6%)
10
BMW3 (3.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-01-31 · Vehicle unit records

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

Sex Distribution (102 persons with recorded sex)

Male60 (58.8%)
↑15.4%prior 52
Female42 (41.2%)
↑2.4%prior 41

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-01-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 40 mph zones significantly decreased from 5 to 1, and in 55 mph zones from 5 to 2. Conversely, crashes in 20 mph zones slightly increased from 7 to 8, while 30 mph zones remained the most frequent crash location with 22 incidents, a slight decrease from 23.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-01-31 · 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: 2025-01-01 through 2025-01-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2025-01-01 through 2025-01-31 (31 days)
  • Geographic scope: GARDNER, MA
  • Total crash records analyzed: 44
  • Total persons involved: 111
  • Total vehicles involved: 84

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). "GARDNER, MA Crash Intelligence Report: January 2025." Published June 21, 2026. Reporting period: 2025-01-01 to 2025-01-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/gardner/january-2025-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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Gardner, MA Crash Report — January 2025 | ThatCarHitMe.com