ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · KINGSTON, MA · DECEMBER 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/kingston/december-2023-report
Monthly Traffic Safety Analysis
26 CRASHES IN
KINGSTON, MA
DECEMBER 2023
In Kingston, December 2023 saw a notable decrease in overall crash incidents, with 26 crashes reported compared to 36 in December 2022, representing a 27.8% reduction. The most significant year-over-year shift was the absence of fatalities in December 2023, down from 1 fatality in the prior period.
26
▼ -27.8%was 36
Total Crash Events
0
▼ -100.0%was 1
Persons Killed
4
▼ -63.6%was 11
Persons Injured
4
▲ 300.0%was 1
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. 3 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend indicates a decrease in crash activity in Kingston, with total crashes falling from 36 in December 2022 to 26 in December 2023, a reduction of 27.8%. Concurrently, total injuries decreased by 63.6%, from 11 to 4, and fatalities dropped from 1 to 0.
4
Hit-and-Run Crashes — December 2023
▲ 300.0% vs prior (1)
Hit-and-run crashes increased significantly from 1 incident in December 2022 to 4 incidents in December 2023. This change resulted in the hit-and-run crash rate rising from 2.8% to 15.4% year-over-year, indicating an upward trend in these incidents.
Vulnerable Road User Casualties
0
Motorists Killed
4
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 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 of crashes shifted significantly year-over-year. In December 2023, Monday became the peak day for crashes with 7 incidents, a change from Saturday being the peak day with 8 incidents in December 2022. The peak hour for crashes also shifted from 4 PM (7 crashes) in the prior year to 8 AM (4 crashes) in the current year.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity distribution of crashes improved significantly, with no fatalities recorded in December 2023, compared to 1 fatality in December 2022. Total injuries decreased from 11 to 4, and the proportion of crashes resulting in minor injuries remained relatively stable at 15.4% (4 out of 26) compared to 16.7% (6 out of 36) in the prior period.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Most severe injury per crash record
Top Contributing Factors
Comparing contributing factors, crashes attributed to 'No improper driving' increased by 3, from 10 in December 2022 to 13 in December 2023. Conversely, 'Inattention' decreased by 4 crashes, from 6 to 2, and 'Failed to yield right of way' also decreased by 4 crashes, from 5 to 1. The factor 'Distracted' was reported in 3 crashes in the prior period but not explicitly listed in the current period's top factors.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
In December 2023, the proportion of crashes occurring in clear weather decreased to 46.2% (12 crashes) from 63.9% (23 crashes) in the prior year. Concurrently, crashes on wet road surfaces saw an increase in proportion, accounting for 46.2% (12 crashes) of incidents compared to 36.1% (13 crashes) in December 2022. The proportion of crashes occurring in daylight remained relatively stable, with 57.7% (15 crashes) in 2023 compared to 55.6% (20 crashes) in 2022.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Road surface condition field
Vehicles & Demographics
Top Vehicle Makes (46 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Vehicle unit records
5 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (41 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Person-level records linked to crash events
Speed Limit Zones
Overall, crash counts decreased across most speed zones from December 2022 to December 2023, with the 25 mph, 30 mph, 35 mph, and 45 mph zones all seeing fewer incidents. The 60 mph speed zone maintained 3 crashes in both periods, but the prior period recorded 1 fatal crash in this zone, while the current period had no fatalities in any speed zone. Crashes in the 20 mph zone (2 crashes) were present in the prior period but not in the current, while the 10 mph zone saw 1 crash in the current period, not present in the prior.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 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: 2023-12-01 through 2023-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-12-01 through 2023-12-31 (31 days)
- Geographic scope: KINGSTON, MA
- Total crash records analyzed: 26
- Total persons involved: 47
- Total vehicles involved: 46
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: December 2023." Published June 21, 2026. Reporting period: 2023-12-01 to 2023-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/kingston/december-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
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly
Period: 2023-12-01 – 2023-12-31
Generated: June 21, 2026 · All rights reserved