Monthly Traffic Safety Analysis

20 CRASHES IN
KINGSTON, MA
MAY 2025

All metrics benchmarked againstMay 2024

Total crashes in Kingston decreased from 32 in May 2024 to 20 in May 2025, representing a 37.5% reduction. Concurrently, total injuries increased by 150%, rising from 6 to 15. The most notable year-over-year shift was this significant increase in reported injuries despite a decrease in overall crash incidents.

20

-37.5%was 32

Total Crash Events

0

Persons Killed

15

150.0%was 6

Persons Injured

1

-66.7%was 3

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.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, total crashes in Kingston decreased by 37.5%, from 32 crashes in May 2024 to 20 crashes in May 2025. This represents a notable downward trend in the number of crash incidents year-over-year.

1

Hit-and-Run Crashes — May 2025

-66.7% vs prior (3)

Hit-and-run crashes decreased from 3 incidents in May 2024 to 1 incident in May 2025. The hit-and-run rate also decreased from 9.4% of total crashes in May 2024 to 5% in May 2025.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

15

Motorists Injured

Prior: 6150.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes remained Thursday in both periods, with 6 crashes in May 2025 compared to 10 in May 2024. The peak crash hour shifted from 2 PM with 5 crashes in May 2024 to 5 PM with 4 crashes in May 2025. While the total number of crashes decreased, the daily and hourly distribution shows a consistent peak day but a shift in the peak hour.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatalities remained at 0 in both May 2024 and May 2025. Total injuries increased significantly by 150%, rising from 6 injuries in May 2024 to 15 injuries in May 2025. Minor injuries increased from 3 (9.4% of crashes) to 5 (25% of crashes), and possible injuries increased from 2 (6.3% of crashes) to 3 (15% of crashes).

Outcome by Severity (Crash Events)

Minor Injury5minor injury crashes25%
66.7%prior 3
Possible Injury3possible injury crashes15%
50.0%prior 2
No Injury12no injury crashes60%
-55.6%prior 27

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Most severe injury per crash record

Top Contributing Factors

Contributing factor 'No improper driving' decreased by 8 crashes, from 13 in May 2024 to 5 in May 2025. Conversely, 'Inattention' increased by 2 crashes, from 3 in May 2024 to 5 in May 2025. 'Followed too closely' decreased by 4 crashes, from 5 in May 2024 to 1 in May 2025, while 'Failed to yield right of way' decreased by 1 crash, from 3 to 2.

Officer-Reported Primary Contributing Cause

Inattention5 (25%)
No improper driving5 (25%)-61.5%prior 13
Failed to yield right of way2 (10%)
Made an improper turn1 (5%)
Followed too closely1 (5%)-80.0%prior 5
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (5%)
Other improper action1 (5%)
Wrong side or wrong way1 (5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The proportion of crashes occurring in adverse weather conditions (Rain, Rain/Cloudy, Cloudy/Rain) remained relatively stable, with 3 crashes (15% of total crashes) in May 2025 and 5 crashes (15.6% of total crashes) in May 2024. Crashes occurring during dark, dawn, or dusk lighting conditions increased from 4 crashes (12.5% of total crashes) in May 2024 to 6 crashes (30% of total crashes) in May 2025. The number of crashes on wet road surfaces decreased from 7 (21.9% of total crashes) in May 2024 to 4 (20% of total crashes) in May 2025.

Weather

Clear13 (65.0%)
-38.1%prior 21
Clear/Clear2 (10.0%)
Cloudy2 (10.0%)
-60.0%prior 5
Cloudy/Rain1 (5.0%)
Rain1 (5.0%)
Rain/Cloudy1 (5.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Weather condition at time of crash

Lighting

Daylight14 (70.0%)
-50.0%prior 28
Dark - lighted roadway2 (10.0%)
Dark - roadway not lighted1 (5.0%)
Dark - unknown roadway lighting1 (5.0%)
Dawn1 (5.0%)
Dusk1 (5.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Lighting condition field

Road Surface

Dry16 (80.0%)
-36.0%prior 25
Wet4 (20.0%)
-42.9%prior 7

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (45 vehicles)

1
TOYOTA7 (15.6%)
0.0%prior 7
2
FORD7 (15.6%)
-12.5%prior 8
3
CHEVROLET6 (13.3%)
20.0%prior 5
4
HONDA5 (11.1%)
0.0%prior 5
5
MERCEDES-BENZ3 (6.7%)
6
GMC2 (4.4%)
7
NISSAN2 (4.4%)
-66.7%prior 6
8
JEEP2 (4.4%)
-66.7%prior 6
9
SUBARU1 (2.2%)
10
AUDI1 (2.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Vehicle unit records

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

Sex Distribution (51 persons with recorded sex)

Female26 (51.0%)
-27.8%prior 36
Male25 (49.0%)
-32.4%prior 37

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in the 30 MPH speed zone decreased from 13 in May 2024 to 6 in May 2025, and in the 35 MPH zone from 9 to 4. Crashes in the 60 MPH speed zone also decreased from 5 to 2. The current period reported crashes in 20 MPH, 40 MPH, and 65 MPH zones, which were not present in the prior period, while the 15 MPH zone reported previously was not present in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · 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: 2025-05-01 through 2025-05-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2025-05-01 through 2025-05-31 (31 days)
  • Geographic scope: KINGSTON, MA
  • Total crash records analyzed: 20
  • Total persons involved: 54
  • Total vehicles involved: 45

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). "KINGSTON, MA Crash Intelligence Report: May 2025." Published June 21, 2026. Reporting period: 2025-05-01 to 2025-05-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/kingston/may-2025-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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Kingston, MA Crash Report — May 2025 | ThatCarHitMe.com