Monthly Traffic Safety Analysis

10 CRASHES IN
UPTON, MA
DECEMBER 2022

All metrics benchmarked againstDecember 2021

In December 2022, Upton experienced 10 crashes, a significant decrease compared to the 22 crashes reported in December 2021. This represents a 54.55% reduction in total crashes year-over-year. The most notable shift was the complete absence of fatalities and injuries in December 2022, down from 1 fatality and 5 injuries in the prior year.

10

-54.5%was 22

Total Crash Events

0

-100.0%was 1

Persons Killed

0

-100.0%was 5

Persons Injured

0

-100.0%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. 10 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 crash trends in Upton show a significant decrease year-over-year. Total crashes fell by 54.55%, from 22 in December 2021 to 10 in December 2022. Additionally, fatalities decreased from 1 to 0, and total injuries dropped from 5 to 0 during the same period.

When Crashes Happen

The temporal distribution of crashes shifted year-over-year, with the peak day moving from Monday in December 2021 (6 crashes) to Wednesday in December 2022 (3 crashes). The peak hour remained 7 AM in both periods, although the number of crashes at that hour decreased from 4 in December 2021 to 3 in December 2022. Overall, crash counts were lower across most days and hours in the current period.

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

Contributing factors showed shifts in prevalence year-over-year. Crashes attributed to Inattention decreased from 5 in December 2021 to 1 in December 2022, a reduction of 4 crashes. Glare was a factor in 2 crashes in both periods, showing no change in count. Factors like 'No improper driving' (6 crashes in prior year) and 'Distracted' (2 crashes in prior year) were not among the listed contributing factors in December 2022.

Officer-Reported Primary Contributing Cause

Visibility obstructed2 (20%)
Glare2 (20%)
Inattention1 (10%)-80.0%prior 5
Made an improper turn1 (10%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway1 (10%)
Failed to yield right of way1 (10%)
Illness1 (10%)

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

Crash conditions showed notable shifts year-over-year. Crashes occurring in clear weather conditions decreased from 15 in December 2021 to 5 in December 2022, while rain-related crashes increased from 3 to 5. Crashes during daylight hours significantly decreased from 17 in December 2021 to 6 in December 2022. Correspondingly, crashes on dry road surfaces decreased from 15 to 5, and crashes on wet surfaces decreased slightly from 6 to 5, with no crashes reported on ice in the current period compared to 1 in the prior period.

Weather

Clear4 (40.0%)
-73.3%prior 15
Clear/Other1 (10.0%)
Cloudy/Rain1 (10.0%)
Rain1 (10.0%)
Rain/Cloudy1 (10.0%)
Rain/Severe crosswinds1 (10.0%)
Rain/Unknown1 (10.0%)

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

Lighting

Daylight6 (60.0%)
-64.7%prior 17
Dark - lighted roadway3 (30.0%)
Dark - roadway not lighted1 (10.0%)

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

Road Surface

Dry5 (50.0%)
-66.7%prior 15
Wet5 (50.0%)
-16.7%prior 6

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 (17 vehicles)

1
TOYOTA3 (17.6%)
2
FORD2 (11.8%)
3
JEEP2 (11.8%)
4
RAM2 (11.8%)
5
AUDI1 (5.9%)
6
MERCEDES-BENZ1 (5.9%)
7
MITS1 (5.9%)
8
NISSAN1 (5.9%)
9
KIA1 (5.9%)
10
GMC1 (5.9%)

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

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

Sex Distribution (20 persons with recorded sex)

Male14 (70.0%)
-39.1%prior 23
Female6 (30.0%)
-68.4%prior 19

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 at the 30 MPH speed limit decreased from 14 in December 2021 to 7 in December 2022. The 35 MPH and 40 MPH speed zones maintained consistent crash counts, with 2 crashes and 1 crash respectively in both periods. Notably, December 2021 recorded 1 fatal crash within the 30 MPH speed limit, whereas December 2022 had no fatal crashes across any speed zone. Several speed zones (5 MPH, 25 MPH, 45 MPH, and 65 MPH) that collectively accounted for 5 crashes in December 2021 did not report any crashes in December 2022.

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: UPTON, MA
  • Total crash records analyzed: 10
  • Total persons involved: 21
  • Total vehicles involved: 17

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). "UPTON, 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/upton/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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Upton, MA Crash Report — December 2022 | ThatCarHitMe.com