Monthly Traffic Safety Analysis

23 CRASHES IN
DUXBURY, MA
MAY 2023

All metrics benchmarked againstMay 2022

In May 2023, Duxbury experienced 23 total crashes, an 8% decrease compared to the 25 crashes recorded in May 2022. Despite this reduction in overall crash count, the number of total injuries saw a substantial increase, rising from 3 in May 2022 to 13 in May 2023. Fatalities remained at zero in both periods.

23

-8.0%was 25

Total Crash Events

0

Persons Killed

13

333.3%was 3

Persons Injured

0

Fatal Crash Events

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.

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

The overall trend indicates a slight decrease in total crashes, from 25 in May 2022 to 23 in May 2023, representing an 8% reduction. However, total injuries increased significantly by 333.3%, from 3 persons injured in May 2022 to 13 persons injured in May 2023. Fatalities remained consistent at zero for both periods.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

13

Motorists Injured

Prior: 3333.3%

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 temporal distribution of crashes shifted year-over-year. In May 2022, the peak day for crashes was Thursday with 5 incidents, while in May 2023, Monday became the peak day with 6 crashes. The peak hour for crashes also changed, moving from 8 PM with 3 crashes in May 2022 to 11 AM with 3 crashes in May 2023, indicating a shift in the timing of peak crash activity.

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 severity distribution of crashes changed notably between the two periods. While fatal crashes remained at zero in both May 2022 and May 2023, total injuries increased from 3 to 13. Minor Injury crashes, which were not present in May 2022, accounted for 6 crashes (26.1% of total crashes) in May 2023, while Possible Injury crashes decreased from 3 (12% of total crashes) to 1 (4.3% of total crashes).

Outcome by Severity (Crash Events)

Minor Injury6minor injury crashes26.1%
Possible Injury1possible injury crashes4.3%
-66.7%prior 3
No Injury16no injury crashes69.6%
-27.3%prior 22

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 distribution of contributing factors showed some shifts. 'No improper driving' decreased from 9 crashes (36% share) in May 2022 to 6 crashes (26.1% share) in May 2023. Conversely, 'Failed to yield right of way' increased from 3 crashes (12% share) to 5 crashes (21.7% share), and 'Followed too closely' increased from 1 crash (4% share) to 2 crashes (8.7% share). 'Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway' appeared in May 2023 with 2 crashes, not being recorded in May 2022.

Officer-Reported Primary Contributing Cause

No improper driving6 (26.1%)-33.3%prior 9
Failed to yield right of way5 (21.7%)
Inattention4 (17.4%)
Followed too closely2 (8.7%)
Other improper action2 (8.7%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway2 (8.7%)
Fatigued/asleep1 (4.3%)
Failure to keep in proper lane or running off road1 (4.3%)

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

Regarding crash conditions, incidents during 'Daylight' decreased from 18 in May 2022 to 16 in May 2023. Crashes occurring during 'Dusk' increased from 1 in May 2022 to 2 in May 2023. The number of crashes on 'Dry' road surfaces decreased from 24 to 21, while crashes on 'Wet' road surfaces increased from 1 to 2.

Weather

Clear20 (87.0%)
11.1%prior 18
Cloudy1 (4.3%)
Cloudy/Rain1 (4.3%)
Rain1 (4.3%)

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

Lighting

Daylight16 (69.6%)
-11.1%prior 18
Dark - roadway not lighted3 (13.0%)
Dark - lighted roadway2 (8.7%)
Dusk2 (8.7%)

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

Road Surface

Dry21 (91.3%)
-12.5%prior 24
Wet2 (8.7%)

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

Vehicles & Demographics

Top Vehicle Makes (42 vehicles)

1
TOYOTA12 (28.6%)
9.1%prior 11
2
NISSAN6 (14.3%)
3
SUBARU3 (7.1%)
4
FORD3 (7.1%)
5
CHEVROLET2 (4.8%)
6
HONDA2 (4.8%)
7
CHRYSLER2 (4.8%)
8
DODGE2 (4.8%)
9
RAM2 (4.8%)
10
VOLVO1 (2.4%)

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

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

Sex Distribution (45 persons with recorded sex)

Female23 (51.1%)
21.1%prior 19
Male22 (48.9%)
-18.5%prior 27

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

Crashes in the 30 mph speed zone decreased from 10 in May 2022 to 6 in May 2023, and crashes in the 60 mph zone also decreased from 9 to 6. Conversely, crashes in the 25 mph zone increased from 1 to 3, and those in the 40 mph zone increased from 1 to 4. No fatal crashes were recorded in any speed zone during either period.

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: DUXBURY, MA
  • Total crash records analyzed: 23
  • Total persons involved: 51
  • Total vehicles involved: 42

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). "DUXBURY, 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/duxbury/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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Duxbury, MA Crash Report — May 2023 | ThatCarHitMe.com