Yearly Traffic Safety Analysis

18 CRASHES IN
CHARLEMONT, MA
2024

All metrics benchmarked against2023

In 2024, Charlemont recorded 18 total crashes, a 5.3% decrease from the 19 crashes reported in 2023. While overall crashes slightly declined, the number of incidents where driver alcohol use was suspected increased from one in the prior year to three in the current year.

18

-5.3%was 19

Total Crash Events

1

Persons Killed

4

-20.0%was 5

Persons Injured

0

-100.0%was 1

Hit-and-Run Crashes

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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 · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash totals in Charlemont remained relatively stable year-over-year, with a slight decrease from 19 incidents in 2023 to 18 in 2024. The number of fatalities was unchanged at one death in each period, while total injuries saw a minor reduction from five to four.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 10.0%

4

Motorists Injured

Prior: 5-20.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

Temporal crash patterns shifted significantly between the two periods. The peak day for crashes moved from Monday (5 crashes) in 2023 to the weekend in 2024, with both Saturday and Sunday recording 4 crashes each. Similarly, the peak hour for incidents changed from 11 AM in the prior year to 8 PM in the current year, which saw 5 crashes.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity outcomes showed some changes year-over-year. While the number of fatal crashes remained constant at one incident in both 2023 and 2024, the fatal crash rate slightly increased from 5.3% to 5.6% due to the lower overall crash total. The proportion of crashes resulting in minor injuries decreased from 21.1% in 2023 to 11.1% in 2024, while the share of no-injury crashes rose from 73.7% to 83.3%.

Outcome by Severity (Crash Events)

Fatal1fatal crashes5.6%
0.0%prior 1
Minor Injury2minor injury crashes11.1%
-50.0%prior 4
No Injury15no injury crashes83.3%
7.1%prior 14

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factor in both periods was 'No improper driving,' with its count decreasing from 8 crashes in 2023 to 7 in 2024. Crashes attributed to 'Driving too fast for conditions' saw a notable drop, from 3 incidents in 2023 to 1 in 2024. Conversely, crashes involving 'Followed too closely' doubled in count from 1 to 2, and 'Physical impairment' was cited in 2 crashes in 2024, a factor not present among the top contributors in the prior year.

Officer-Reported Primary Contributing Cause

No improper driving7 (38.9%)-12.5%prior 8
Failure to keep in proper lane or running off road3 (16.7%)
Physical impairment2 (11.1%)
Followed too closely2 (11.1%)
Exceeded authorized speed limit1 (5.6%)
Wrong side or wrong way1 (5.6%)
Driving too fast for conditions1 (5.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Analysis of crash conditions reveals a pronounced shift in lighting. Crashes occurring in 'Dark - roadway not lighted' conditions increased from 5 incidents in 2023 to 9 in 2024, accounting for half of all crashes in the current period. In contrast, crashes during 'Daylight' hours decreased from 10 to 8. Correspondingly, crashes during rainy weather decreased from 6 in 2023 to 2 in 2024, while crashes on dry road surfaces increased from 10 to 12.

Weather

Clear10 (55.6%)
0.0%prior 10
Clear/Clear2 (11.1%)
Cloudy2 (11.1%)
Rain2 (11.1%)
-60.0%prior 5
Cloudy/Severe crosswinds1 (5.6%)
Snow/Sleet, hail (freezing rain or drizzle)1 (5.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash

Lighting

Dark - roadway not lighted9 (50.0%)
80.0%prior 5
Daylight8 (44.4%)
-20.0%prior 10
Dusk1 (5.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field

Road Surface

Dry12 (66.7%)
20.0%prior 10
Wet4 (22.2%)
-33.3%prior 6
Sand, mud, dirt, oil, gravel1 (5.6%)
Snow1 (5.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (24 vehicles)

1
TOYOTA7 (29.2%)
16.7%prior 6
2
HYUNDAI2 (8.3%)
3
CHEVROLET2 (8.3%)
4
HONDA2 (8.3%)
5
SUBARU2 (8.3%)
-60.0%prior 5
6
MACK1 (4.2%)
7
MAZDA1 (4.2%)
8
NISSAN1 (4.2%)
9
PRCA1 (4.2%)
10
JEEP1 (4.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records

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

Sex Distribution (30 persons with recorded sex)

Female17 (56.7%)
70.0%prior 10
Male13 (43.3%)
-35.0%prior 20

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Person-level records linked to crash events

Speed Limit Zones

The distribution of crashes across speed zones changed notably year-over-year. There was a significant reduction in crashes within 30 mph zones, which fell from 8 incidents in 2023 to just 2 in 2024. Conversely, crashes in 45 mph zones increased from 3 to 5. The number of crashes in 50 mph zones remained constant at 5, and this zone was the location of the single fatal crash recorded in both 2023 and 2024.

Fatal crashes by zone: 50 mph: 1 of 5 (20%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · 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: 2024-01-01 through 2024-12-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2024-01-01 through 2024-12-31 (366 days)
  • Geographic scope: CHARLEMONT, MA
  • Total crash records analyzed: 18
  • Total persons involved: 32
  • Total vehicles involved: 24

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). "CHARLEMONT, MA Crash Intelligence Report: 2024." Published June 21, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/charlemont/2024-annual-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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Charlemont, MA Crash Report — 2024 | ThatCarHitMe.com