ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · CHARLTON, MA · 2023
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/2023-annual-report
Yearly Traffic Safety Analysis
559 CRASHES IN
CHARLTON, MA
2023
In Charlton, total traffic crashes increased slightly from 547 in 2022 to 559 in 2023, a change of approximately 2.2%. While the number of fatalities remained stable at 6 for both years, the most notable year-over-year shift was a 19.4% increase in the number of persons injured, which rose from 155 to 185.
559
▲ 2.2%was 547
Total Crash Events
6
Persons Killed
185
▲ 19.4%was 155
Persons Injured
28
▲ 27.3%was 22
Hit-and-Run Crashes
Note: "Persons Killed" (6) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 6 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend in Charlton shows a slight increase in crash volume from 2022 to 2023, with total incidents rising by 12. More significantly, the number of people injured in these crashes grew by 19.4%, from 155 to 185. Fatalities, however, held constant at 6 individuals in both periods, indicating a stable trend in the most severe outcomes despite a rise in overall injuries.
28
Hit-and-Run Crashes — 2023
▲ 27.3% vs prior (22)
Hit-and-run crashes trended upward in Charlton from 2022 to 2023. The total count of such incidents increased by 27.3%, from 22 to 28. As a result, the hit-and-run rate as a percentage of all crashes also rose, moving from 4.0% in 2022 to 5.0% in 2023.
Vulnerable Road User Casualties
0
Pedestrians Killed
6
Motorists Killed
1
Pedestrians Injured
184
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns for crashes in Charlton were largely consistent year-over-year. Friday remained the peak day for crashes, with the count increasing from 91 in 2022 to 104 in 2023. The afternoon commute window continued to be the most frequent time for collisions, with the 3 PM hour being the peak in 2023 with 50 crashes, similar to the 2 PM and 3 PM peak hours in 2022 which each saw 46 crashes.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The number of fatal crashes was unchanged at 5 for both 2022 and 2023, resulting in a nearly stable fatal crash rate of 0.9% of all crashes. While the count of serious injury crashes decreased from 13 to 8, there was a significant increase in minor injury crashes from 70 to 90. This shift contributed to an overall rise in the total number of persons injured, which increased from 155 to 185 year-over-year.
Severity is per crash event (most severe injury). 5 fatal crash events resulted in 6 persons killed.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factors to crashes saw a shift in ranking between 2022 and 2023. Crashes attributed to 'Inattention' increased from 68 to 82, a 20.6% rise in count, moving it from the third to the second most common factor. Conversely, 'Followed too closely' decreased from 85 to 73 incidents, dropping from second to third rank. Crashes involving 'Distracted' as a factor also grew from 6 to 11 instances.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crash conditions shifted between the two periods, with a notable increase in incidents occurring in adverse weather. The number of crashes on wet roads rose from 69 in 2022 to 119 in 2023, while crashes in rainy conditions increased from 28 to 42. Concurrently, the proportion of crashes occurring in daylight grew, accounting for 71.7% of incidents in 2023 compared to 64.2% in 2022.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field
Vehicles & Demographics
While the top three vehicle makes involved in crashes—Toyota, Ford, and Honda—remained the same in both years, there was a noticeable shift in the age demographics of persons involved. The 35-44 age group saw its involvement increase from 168 individuals in 2022 to 208 in 2023, making it the most represented cohort. In contrast, the number of persons in the 26-34 age group decreased from 236 to 194.
Top Vehicle Makes (976 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records
79 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (1,140 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across different speed zones changed significantly from 2022 to 2023. Crashes in 50 mph zones increased from 58 to 97, while those in 65 mph zones fell from 170 to 143. A notable shift also occurred in the location of fatal crashes; in 2022, all 5 fatal crashes with a recorded speed limit were in zones of 55 mph or higher, whereas in 2023, all 5 such crashes occurred in zones of 50 mph or lower.
Fatal crashes by zone: 30 mph: 1 of 102 (0.98%) · 45 mph: 1 of 3 (33.333%) · 50 mph: 3 of 97 (3.093%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · 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: 2023-01-01 through 2023-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-01-01 through 2023-12-31 (365 days)
- Geographic scope: CHARLTON, MA
- Total crash records analyzed: 559
- Total persons involved: 1,229
- Total vehicles involved: 976
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: 2023." Published June 21, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/charlton/2023-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: 2023-01-01 – 2023-12-31
Generated: June 21, 2026 · All rights reserved