Monthly Traffic Safety Analysis

42 CRASHES IN
BELCHERTOWN, MA
JANUARY 2026

All metrics benchmarked againstJanuary 2025

In January 2026, BELCHERTOWN experienced 42 crashes, a significant increase of 90.9% compared to 22 crashes in January 2025. The most notable year-over-year shift was in speeding-related crashes, which surged from 1 in the prior period to 7 in the current period, representing a 600% increase.

42

90.9%was 22

Total Crash Events

0

Persons Killed

6

20.0%was 5

Persons Injured

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. 1 crash with unreported severity is not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates a substantial increase in crash activity year-over-year, with total crashes rising by 90.9% from 22 to 42. Total injuries also saw an increase of 20%, from 5 injured persons in the prior period to 6 in the current period.

1

Hit-and-Run Crashes — January 2026

2.4% hit-and-run rate this period vs 0.0% prior. Prior period: 0.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

6

Motorists Injured

Prior: 520.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · 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 Wednesday, with 7 crashes in the prior period, to Saturday, with 15 crashes in the current period. While the peak hour remained 5p, the number of crashes at this hour increased from 3 to 5. There was a particularly sharp increase in Saturday crashes, rising by 650% from 2 to 15.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

No fatalities were reported in either period. The total number of injured persons increased from 5 in the prior period to 6 in the current period. The proportion of crashes resulting in any injury increased from 9.1% (2 injury crashes out of 22 total crashes) in the prior period to 11.9% (5 injury crashes out of 42 total crashes) in the current period, with the current period also reporting one serious injury crash.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes2.4%
Minor Injury3minor injury crashes7.1%
50.0%prior 2
Possible Injury1possible injury crashes2.4%
No Injury36no injury crashes85.7%
80.0%prior 20

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factor, 'No improper driving', increased from 8 crashes (36.4% share of prior crashes) to 17 crashes (40.5% share of current crashes). 'Driving too fast for conditions' saw a notable increase in count, rising from 1 crash (4.5% share of prior crashes) to 3 crashes (7.1% share of current crashes). Conversely, 'Inattention' decreased from 4 crashes (18.2% share of prior crashes) to 3 crashes (7.1% share of current crashes).

Officer-Reported Primary Contributing Cause

No improper driving17 (40.5%)112.5%prior 8
Followed too closely3 (7.1%)
Inattention3 (7.1%)
Failed to yield right of way3 (7.1%)
Driving too fast for conditions3 (7.1%)
Other improper action2 (4.8%)
Failure to keep in proper lane or running off road2 (4.8%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (2.4%)
Exceeded authorized speed limit1 (2.4%)
Physical impairment1 (2.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

There was a significant shift towards adverse weather and road conditions in the current period. Crashes occurring in 'Snow' weather increased from 0 to 13, and on 'Snow' road surfaces from 1 to 20. Conversely, crashes in 'Clear' weather decreased in share from 81.8% to 54.8%, and on 'Dry' road surfaces from 86.4% to 33.3%. Crashes during 'Daylight' hours increased from 10 to 24.

Weather

Clear23 (54.8%)
27.8%prior 18
Snow13 (31.0%)
Rain2 (4.8%)
Snow/Sleet, hail (freezing rain or drizzle)2 (4.8%)
Cloudy1 (2.4%)
Snow/Blowing sand, snow1 (2.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Weather condition at time of crash

Lighting

Daylight24 (57.1%)
140.0%prior 10
Dark - roadway not lighted12 (28.6%)
100.0%prior 6
Dark - lighted roadway3 (7.1%)
-40.0%prior 5
Dusk3 (7.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Lighting condition field

Road Surface

Snow20 (47.6%)
Dry14 (33.3%)
-26.3%prior 19
Wet6 (14.3%)
Ice2 (4.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (62 vehicles)

1
TOYOTA10 (16.1%)
2
SUBARU8 (12.9%)
3
HONDA8 (12.9%)
4
FORD8 (12.9%)
60.0%prior 5
5
NISSAN5 (8.1%)
6
HYUNDAI3 (4.8%)
7
GMC3 (4.8%)
8
KIA3 (4.8%)
9
CHEVROLET2 (3.2%)
10
OT2 (3.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Vehicle unit records

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

Sex Distribution (83 persons with recorded sex)

Female44 (53.0%)
131.6%prior 19
Male39 (47.0%)
85.7%prior 21

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes increased across several speed zones, most notably in the 30 mph zone, which saw an increase from 5 crashes in the prior period to 16 crashes in the current period. Crashes in the 15 mph zone also increased from 1 to 4. Additionally, crashes occurred in higher speed zones of 45 mph (6 crashes) and 50 mph (1 crash) in the current period, which were not present in the prior period, while no fatal crashes were recorded in any speed zone during either period.

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

Data Coverage

  • Reporting period: 2026-01-01 through 2026-01-31 (31 days)
  • Geographic scope: BELCHERTOWN, MA
  • Total crash records analyzed: 42
  • Total persons involved: 86
  • Total vehicles involved: 62

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