Monthly Traffic Safety Analysis

5 CRASHES IN
HAMPDEN, MA
DECEMBER 2022

All metrics benchmarked againstDecember 2021

Total crashes in HAMPDEN, MA decreased by 16.67%, from 6 crashes in December 2021 to 5 crashes in December 2022. Neither period reported any fatalities or injuries. The most notable shift was the absence of 'Failed to yield right of way' as a contributing factor in the current period, which accounted for 2 crashes in the prior period.

5

-16.7%was 6

Total Crash Events

0

Persons Killed

0

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crashes in HAMPDEN, MA showed a decreasing trend year-over-year, with a 16.67% reduction from 6 crashes in December 2021 to 5 crashes in December 2022. Both periods reported zero fatalities and zero injuries, indicating stable outcomes in terms of severe consequences despite the change in crash volume.

When Crashes Happen

The peak day for crashes shifted from Friday in December 2021, which saw 2 crashes, to Sunday in December 2022, which recorded 3 crashes. Similarly, the peak hour changed from 8p with 1 crash in the prior period to 3p and 4p, each with 2 crashes, in the current period. This indicates a shift in crash timing from late evening to afternoon and from weekdays to weekends.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)

Top Contributing Factors

The contributing factor 'No improper driving' remained constant at 3 crashes in both periods, though its share of total crashes increased from 50% to 60% due to the overall decrease in crash volume. 'Driving too fast for conditions' also remained constant at 1 crash. Notably, 'Failed to yield right of way,' which was a factor in 2 crashes in December 2021, was not reported as a factor in December 2022 crashes.

Officer-Reported Primary Contributing Cause

No improper driving3 (60%)
Driving too fast for conditions1 (20%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Lighting conditions saw a shift, with the current period having 3 crashes occur during 'Daylight' compared to none in the prior period, where 'Dark - lighted roadway' was the most frequent condition with 4 crashes. Road surface conditions also varied, with the prior period reporting 4 crashes on 'Dry' surfaces, while the current period showed a more even distribution with 2 crashes on 'Snow' and 2 on 'Wet' surfaces. Weather conditions remained diverse across both periods, with no single dominant condition emerging.

Weather

Clear1 (20.0%)
Clear/Unknown1 (20.0%)
Cloudy/Snow1 (20.0%)
Rain/Severe crosswinds1 (20.0%)
Snow/Blowing sand, snow1 (20.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Weather condition at time of crash

Lighting

Daylight3 (60.0%)
Dark - lighted roadway1 (20.0%)
Dark - unknown roadway lighting1 (20.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Lighting condition field

Road Surface

Snow2 (40.0%)
Wet2 (40.0%)
Dry1 (20.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (6 vehicles)

1
CHEVROLET3 (50%)
2
HONDA1 (16.7%)
3
RAM1 (16.7%)
4
SUBARU1 (16.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Vehicle unit records

Sex Distribution (8 persons with recorded sex)

Male6 (75.0%)
0.0%prior 6
Female1 (12.5%)
-75.0%prior 4
X / Unspecified1 (12.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 30 mph speed zones decreased from 5 crashes in December 2021 to 2 crashes in December 2022. Conversely, 3 crashes occurred in 40 mph zones in the current period, whereas no crashes were reported in 40 mph zones in the prior period. Neither period recorded any fatal crashes within any speed zone.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · 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: 2022-12-01 through 2022-12-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2022-12-01 through 2022-12-31 (31 days)
  • Geographic scope: HAMPDEN, MA
  • Total crash records analyzed: 5
  • Total persons involved: 8
  • Total vehicles involved: 6

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). "HAMPDEN, MA Crash Intelligence Report: December 2022." Published June 21, 2026. Reporting period: 2022-12-01 to 2022-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/hampden/december-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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Hampden, MA Crash Report — December 2022 | ThatCarHitMe.com