ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CHARLTON, MA · 2025
Purpose: Machine-readable JSON endpoint for AI agents, LLMs, researchers, and programmatic consumers. Returns all underlying crash data and AI-generated commentary without HTML.
Authentication: None required. Public endpoint.
GET: https://thatcarhitme.com/api/crash-data/reports/data/massachusetts/charlton/2025-annual-report
Yearly Traffic Safety Analysis
690 CRASHES IN
CHARLTON, MA
2025
In Charlton, total traffic crashes increased from 644 in the prior year to 690 in the current year, representing a 7.1% rise. While the total number of injuries saw a slight decrease, the number of fatal crashes doubled from one to two. A notable shift in contributing factors was a 38% increase in crashes attributed to failing to yield the right of way, which rose from 58 to 80 incidents year-over-year.
690
▲ 7.1%was 644
Total Crash Events
2
▲ 100.0%was 1
Persons Killed
215
▼ -3.2%was 222
Persons Injured
42
▲ 2.4%was 41
Hit-and-Run Crashes
Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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 · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend shows an increase in crash incidents in Charlton. Total crashes rose by 7.1% from 644 to 690 year-over-year, and the number of fatalities increased from one to two. Conversely, the total number of people injured in these incidents decreased slightly from 222 to 215.
42
Hit-and-Run Crashes — 2025
▲ 2.4% vs prior (41)
The number of hit-and-run incidents remained stable year-over-year, with 42 crashes in the current period compared to 41 in the prior period. As a percentage of total crashes, the hit-and-run rate saw a slight decrease, moving from 6.4% to 6.1%. This indicates that the frequency of hit-and-run events did not increase with the overall rise in total crashes.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
2
Motorists Killed
1
Pedestrians Injured
1
Cyclists Injured
213
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The timing of crashes showed a significant shift in the peak day of the week. In the current year, Sunday was the most frequent day for crashes with 113 incidents, a change from the prior year when Thursday was the peak day with 111 incidents. The peak hour for crashes remained consistent at 3 p.m. in both periods, though the number of crashes during this hour decreased from 76 to 61.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes intensified year-over-year. The number of fatal crashes doubled from one to two, and the fatal crash rate increased from 0.16% to 0.29%. The proportion of serious injury crashes also saw a slight increase, rising from 2.3% to 2.5% of all incidents. While the share of minor injury crashes decreased from 17.9% to 13.2%, crashes resulting in possible injury increased their share from 4.3% to 7.7%.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record
Top Contributing Factors
The top three contributing factors remained the same in both periods: 'No improper driving', 'Followed too closely', and 'Inattention'. However, there were significant shifts in the counts of other factors. Crashes attributed to 'Failed to yield right of way' increased by 38%, from 58 incidents to 80. In contrast, incidents involving 'Driving too fast for conditions' saw a 44% decrease in count, falling from 59 to 33.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crash conditions varied between the two periods, particularly concerning road surface conditions. The number of crashes occurring on snow-covered roads decreased significantly, from 65 in the prior year to 38 in the current year. Conversely, crashes on unlit dark roadways increased from 75 to 105 incidents. The number of crashes on dry and wet roads also saw increases, rising from 456 to 511 and 95 to 110, respectively.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field
Vehicles & Demographics
The composition of vehicles involved in crashes remained largely consistent year-over-year. Toyota, Ford, and Honda were the top three most frequently involved vehicle makes in both periods, with their rankings and counts remaining stable. Similarly, the age distribution of persons involved in crashes showed little change, with the 26-34 age group representing the largest share (17.0%) in both the current and prior years.
Top Vehicle Makes (1,263 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records
120 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (1,432 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Person-level records linked to crash events
Speed Limit Zones
Year-over-year, the number of crashes in high-speed zones (50 mph and above) was unchanged at 290 incidents. However, fatalities in these zones increased, with the current year seeing one fatality in a 50 mph zone and another in a 55 mph zone, compared to a single fatality in a 50 mph zone in the prior year. Crashes in 30 mph zones saw a notable increase, rising from 100 to 131 incidents.
Fatal crashes by zone: 50 mph: 1 of 85 (1.176%) · 55 mph: 1 of 18 (5.556%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · 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: 2025-01-01 through 2025-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2025-01-01 through 2025-12-31 (365 days)
- Geographic scope: CHARLTON, MA
- Total crash records analyzed: 690
- Total persons involved: 1,562
- Total vehicles involved: 1,263
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). "CHARLTON, MA Crash Intelligence Report: 2025." Published June 21, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/charlton/2025-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
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly
Period: 2025-01-01 – 2025-12-31
Generated: June 21, 2026 · All rights reserved