ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CHARLTON, MA · SEPTEMBER 2022
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/september-2022-report
Monthly Traffic Safety Analysis
46 CRASHES IN
CHARLTON, MA
SEPTEMBER 2022
CHARLTON experienced a slight decrease in total crashes, falling by 4.2% from 48 crashes in September 2021 to 46 crashes in September 2022. While total injuries saw a modest 7.7% reduction, the most significant shift was a substantial 80% decrease in speeding-related crashes, which dropped from 5 to 1 year-over-year. Fatalities remained at zero in both periods.
46
▼ -4.2%was 48
Total Crash Events
0
Persons Killed
12
▼ -7.7%was 13
Persons Injured
2
▲ 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. 1 crash with unreported severity is not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend indicates a slight decrease in crash incidents year-over-year, with total crashes falling by 4.2% from 48 to 46. Total injuries also saw a minor reduction, decreasing from 13 to 12. This suggests a relatively stable but slightly improving safety trend for the month.
2
Hit-and-Run Crashes — September 2022
▲ 100.0% vs prior (1)
Hit-and-run crashes increased by 100% year-over-year, rising from 1 incident in September 2021 to 2 incidents in September 2022. Consequently, the hit-and-run crash rate also increased, from 2.1% of total crashes to 4.3%. This indicates an upward trend in hit-and-run incidents.
Vulnerable Road User Casualties
0
Motorists Killed
12
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · 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 Thursday in September 2021, with 10 crashes, to Friday in September 2022, with 14 crashes. The peak hour remained 2 PM in both periods, though the number of crashes at that hour decreased from 7 in September 2021 to 6 in September 2022. Overall, crash distribution across the week saw shifts, with Friday experiencing a notable increase.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Fatal crashes remained at zero in both September 2021 and September 2022. The proportion of serious injuries (A) increased from 0% to 2.2% (1 crash), while minor injuries (B) decreased from 14.6% (7 crashes) to 8.7% (4 crashes). The percentage of crashes resulting in no injury rose from 77.1% to 82.6% year-over-year.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Most severe injury per crash record
Top Contributing Factors
Crashes attributed to 'Followed too closely' increased by 22.2%, from 9 crashes in September 2021 to 11 crashes in September 2022. 'Inattention' crashes doubled, rising from 5 to 10 incidents, and 'No improper driving' crashes increased by 66.7%, from 6 to 10. Conversely, 'Failed to yield right of way' crashes saw a significant 80% decrease, falling from 5 to 1.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The proportion of crashes occurring in clear weather conditions increased from 72.9% (35 crashes) in September 2021 to 84.8% (39 crashes) in September 2022. Similarly, crashes on dry road surfaces rose from 77.1% (37 crashes) to 87.0% (40 crashes), while wet road crashes decreased from 11 to 5. Crashes occurring in dark conditions (lighted or unlighted) collectively decreased from 14 to 8, with daylight crashes increasing from 32 to 36.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Road surface condition field
Vehicles & Demographics
Among top vehicle makes, HONDA crashes doubled from 5 to 10, while TOYOTA crashes decreased by 50% from 16 to 8. The 55-64 age group saw a significant increase in persons involved in crashes, rising from 10 to 21, whereas the 65+ age group decreased from 16 to 9. The 21-25 age group also experienced an increase, from 11 to 16 persons.
Top Vehicle Makes (86 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Vehicle unit records
6 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (99 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Person-level records linked to crash events
Speed Limit Zones
Crashes occurring in 65 mph speed zones doubled, increasing from 8 in September 2021 to 16 in September 2022. Conversely, crashes in 40 mph zones decreased by 58.3%, falling from 12 to 5. Crashes in 25 mph zones saw a notable increase from 1 to 6, while 30 mph zones experienced a decrease from 11 to 8 crashes.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · 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: 2022-09-01 through 2022-09-30
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-09-01 through 2022-09-30 (30 days)
- Geographic scope: CHARLTON, MA
- Total crash records analyzed: 46
- Total persons involved: 108
- Total vehicles involved: 86
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: September 2022." Published June 21, 2026. Reporting period: 2022-09-01 to 2022-09-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/charlton/september-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
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly
Period: 2022-09-01 – 2022-09-30
Generated: June 21, 2026 · All rights reserved