Monthly Traffic Safety Analysis

49 CRASHES IN
CHARLTON, MA
MAY 2023

All metrics benchmarked againstMay 2022

In May 2023, Charlton experienced 49 crashes, an increase of 16.67% from the 42 crashes recorded in May 2022. The most notable shift was a 100% increase in fatalities, rising from 1 in the prior period to 2 in the current period.

49

16.7%was 42

Total Crash Events

2

100.0%was 1

Persons Killed

17

30.8%was 13

Persons Injured

6

200.0%was 2

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash activity in Charlton increased year-over-year, with total crashes rising by 16.67% from 42 to 49. Fatalities doubled from 1 to 2, while total injuries increased by 30.77% from 13 to 17.

6

Hit-and-Run Crashes — May 2023

200.0% vs prior (2)

Hit-and-run crashes increased substantially, rising from 2 in May 2022 to 6 in May 2023, representing a 200% increase in count. Consequently, the hit-and-run rate also increased from 4.8% to 12.2% of all crashes.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

2

Motorists Killed

Prior: 1100.0%

1

Pedestrians Injured

Prior: 0%

16

Motorists Injured

Prior: 1323.1%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · 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 in the current period, with Sunday, Thursday, and Friday each recording 8 crashes, whereas Monday was the peak day in the prior period with 12 crashes. The peak crash hour also moved from 4 p.m. with 6 crashes in the prior period to 3 p.m. with 8 crashes in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The fatal crash rate increased from 2.4% in May 2022 to 4.1% in May 2023, with the number of fatal crashes rising from 1 to 2. Total injuries increased from 13 to 17, and serious injuries (A) were recorded at 1 in the current period, compared to none in the prior period.

Outcome by Severity (Crash Events)

Fatal2fatal crashes4.1%
100.0%prior 1
Serious Injury1serious injury crashes2%
Minor Injury10minor injury crashes20.4%
42.9%prior 7
No Injury35no injury crashes71.4%
12.9%prior 31

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Most severe injury per crash record

Top Contributing Factors

The factor 'No improper driving' decreased by 42.86% in count, from 14 to 8, while 'Followed too closely' decreased by 11.11% in count, from 9 to 8. Conversely, 'Inattention' increased by 75% in count, from 4 to 7, and 'Operating vehicle in erratic, reckless, careless, negligent or aggressive manner' saw a 150% increase in count, from 2 to 5.

Officer-Reported Primary Contributing Cause

Followed too closely8 (16.3%)-11.1%prior 9
No improper driving8 (16.3%)-42.9%prior 14
Inattention7 (14.3%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner5 (10.2%)
Failed to yield right of way4 (8.2%)
Other improper action4 (8.2%)
Failure to keep in proper lane or running off road3 (6.1%)
Distracted2 (4.1%)
Over-correcting/over-steering2 (4.1%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway1 (2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crash conditions remained largely consistent year-over-year, with the majority of incidents occurring in clear weather, daylight, and on dry roads. The number of crashes on wet road surfaces slightly decreased from 5 in May 2022 to 4 in May 2023.

Weather

Clear44 (89.8%)
29.4%prior 34
Cloudy/Rain3 (6.1%)
Cloudy1 (2.0%)
Rain/Cloudy1 (2.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Weather condition at time of crash

Lighting

Daylight39 (79.6%)
25.8%prior 31
Dark - lighted roadway6 (12.2%)
Dusk2 (4.1%)
Dark - roadway not lighted1 (2.0%)
-80.0%prior 5
Dawn1 (2.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Lighting condition field

Road Surface

Dry45 (91.8%)
21.6%prior 37
Wet4 (8.2%)
-20.0%prior 5

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Road surface condition field

Vehicles & Demographics

The total number of persons involved in crashes decreased from 110 in the prior period to 101 in the current period. Toyota remained the most common vehicle make involved, with its count increasing from 10 to 16. The 26-34 age group saw a decrease in involvement from 26 to 18 persons, while the 55-64 age group increased from 9 to 18 persons.

Top Vehicle Makes (87 vehicles)

1
TOYOTA16 (18.4%)
60.0%prior 10
2
FORD12 (13.8%)
71.4%prior 7
3
NISSAN6 (6.9%)
4
HONDA6 (6.9%)
20.0%prior 5
5
CHEVROLET5 (5.7%)
6
ACURA4 (4.6%)
7
SUBARU4 (4.6%)
8
JEEP3 (3.4%)
-50.0%prior 6
9
GMC3 (3.4%)
10
VOLKSWAGEN3 (3.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Vehicle unit records

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

Sex Distribution (90 persons with recorded sex)

Male54 (60.0%)
5.9%prior 51
Female36 (40.0%)
-34.5%prior 55

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Person-level records linked to crash events

Speed Limit Zones

Fatal crashes in the current period occurred in 30 mph and 50 mph zones, with one fatality in each, whereas the prior period had one fatality in a 55 mph zone. Crashes in the 50 mph speed zone significantly increased from 2 in May 2022 to 13 in May 2023.

Fatal crashes by zone: 30 mph: 1 of 6 (16.667%) · 50 mph: 1 of 13 (7.692%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · 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: 2023-05-01 through 2023-05-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-05-01 through 2023-05-31 (31 days)
  • Geographic scope: CHARLTON, MA
  • Total crash records analyzed: 49
  • Total persons involved: 101
  • Total vehicles involved: 87

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: May 2023." Published June 21, 2026. Reporting period: 2023-05-01 to 2023-05-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/charlton/may-2023-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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Charlton, MA Crash Report — May 2023 | ThatCarHitMe.com