ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · KINGSTON, MA · 2025
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/kingston/2025-annual-report
Yearly Traffic Safety Analysis
257 CRASHES IN
KINGSTON, MA
2025
In 2025, Kingston recorded 257 total crashes, a 3.7% decrease from the 267 crashes in 2024. While total collisions saw a slight decline, the most notable change was a reduction in crash severity, with the number of persons suffering serious injuries falling from 11 in the prior year to 6 in the current year.
257
▼ -3.7%was 267
Total Crash Events
1
Persons Killed
88
▼ -18.5%was 108
Persons Injured
12
Hit-and-Run Crashes
Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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 · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, traffic crashes in Kingston showed a downward trend year-over-year. Total crashes decreased by 3.7%, from 267 in 2024 to 257 in 2025. The number of people injured in these incidents also declined by 18.5%, from 108 to 88, while fatalities remained unchanged with one death recorded in each period.
12
Hit-and-Run Crashes — 2025
▼ 0.0% vs prior (12)
The number of hit-and-run incidents remained unchanged, with 12 crashes recorded in both 2025 and 2024. The hit-and-run rate, expressed as a percentage of total crashes, was also stable, showing a negligible increase from 4.5% in the prior year to 4.7% in the current year. This indicates a stable trend for this type of crash.
Vulnerable Road User Casualties
0
Cyclists Killed
1
Motorists Killed
1
Cyclists Injured
87
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes shifted between the two periods. The most frequent day for crashes moved from Thursday (53 crashes) in 2024 to Friday (47 crashes) in 2025. Similarly, the peak hour for collisions changed from 2 p.m. in the prior year (26 crashes) to the 5 p.m. hour in the current year (30 crashes), indicating a shift towards the evening commute period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Crash severity generally decreased year-over-year. While both 2024 and 2025 recorded one fatal crash each, resulting in a stable fatal crash rate (0.37% vs. 0.39%), the proportion of injury-related crashes declined. Crashes resulting in serious injuries dropped from 11 (4.1% of total) to 4 (1.6% of total), and minor injury crashes decreased from 48 to 44. Consequently, the share of crashes with no reported injuries increased from 68.9% to 72.8%.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factors to crashes remained consistent year-over-year, with 'No improper driving' and 'Inattention' being the top two cited factors in both periods. The count for crashes involving 'Inattention' increased by 16.1%, from 31 in 2024 to 36 in 2025. Conversely, crashes attributed to 'Failed to yield right of way' saw a notable decrease in count, falling by 28.6% from 28 incidents to 20.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crash conditions remained broadly similar between 2024 and 2025. In both years, the majority of crashes occurred in daylight (66.7% vs. 66.5%) and on dry roads (82.0% vs. 78.6%). There was a slight increase in the proportion of crashes occurring on non-dry road surfaces, which rose from 17.6% of crashes in 2024 to 21.4% in 2025, primarily involving wet, snowy, or icy conditions.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field
Vehicles & Demographics
The top three vehicle makes involved in crashes were consistent across both years: Toyota, Ford, and Chevrolet. In 2025, Jeep (37 vehicles) surpassed Honda (36 vehicles) to enter the top four most common makes. Analysis of persons involved in crashes shows a shift in age demographics; the proportion of individuals aged 16-20 increased from 11.7% to 16.0% year-over-year, while the involvement of persons aged 0-15 decreased significantly from 12.5% to 3.3%.
Top Vehicle Makes (481 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records
43 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (526 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across different speed zones showed some changes year-over-year. Crashes in 60 mph zones decreased from 39 in 2024 to 28 in 2025. The single fatal crash in 2025 occurred in a 30 mph zone, whereas the fatal crash in 2024 took place in a 60 mph zone. The highest number of crashes in both years occurred in 30 mph zones, with 78 in 2024 and 72 in 2025.
Fatal crashes by zone: 30 mph: 1 of 72 (1.389%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · 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: 2025-01-01 through 2025-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2025-01-01 through 2025-12-31 (365 days)
- Geographic scope: KINGSTON, MA
- Total crash records analyzed: 257
- Total persons involved: 569
- Total vehicles involved: 481
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: 2025." Published June 21, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/kingston/2025-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: 2025-01-01 – 2025-12-31
Generated: June 21, 2026 · All rights reserved