ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · KINGSTON, MA · 2022
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/2022-annual-report
Yearly Traffic Safety Analysis
251 CRASHES IN
KINGSTON, MA
2022
In 2022, Kingston recorded 251 total traffic crashes, a 2.7% decrease from the 258 crashes recorded in 2021. While total crashes and injuries (66 in 2022 vs. 84 in 2021) declined, the number of fatalities doubled from one to two. The most notable year-over-year shift was the number of hit-and-run incidents, which increased by 100% from 5 in 2021 to 10 in 2022.
251
▼ -2.7%was 258
Total Crash Events
2
▲ 100.0%was 1
Persons Killed
66
▼ -21.4%was 84
Persons Injured
10
▲ 100.0%was 5
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. 11 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall traffic crashes in Kingston showed a slight downward trend, decreasing by 2.7% from 258 in 2021 to 251 in 2022. This was accompanied by a 21.4% reduction in total injuries, which fell from 84 to 66. However, this positive trend was contrasted by a doubling of traffic fatalities, which increased from one in 2021 to two in 2022.
10
Hit-and-Run Crashes — 2022
▲ 100.0% vs prior (5)
Hit-and-run crashes increased significantly between the two periods. The total count of hit-and-run incidents doubled from 5 in 2021 to 10 in 2022. Consequently, the hit-and-run rate more than doubled, rising from 1.9% of all crashes in the prior year to 4.0% in the current year, indicating a strong upward trend.
Vulnerable Road User Casualties
0
Pedestrians Killed
2
Motorists Killed
2
Pedestrians Injured
64
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes showed some shifts between the two years. The peak day for crashes moved from Wednesday (51 crashes) in 2021 to Friday (45 crashes) in 2022. The peak hour for collisions shifted slightly earlier, from 4 p.m. (24 crashes) in 2021 to 3 p.m. (23 crashes) in 2022, though the afternoon hours remained the most frequent time for crashes in both periods.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes worsened year-over-year, even as the total number of crashes declined. Fatal crashes doubled from one in 2021 to two in 2022, increasing the fatal crash rate from 0.4% to 0.8% of all crashes. While the count of serious injury crashes decreased from 8 to 6, the proportion of crashes resulting in no injuries also decreased from 75.2% in 2021 to 72.5% in 2022.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Most severe injury per crash record
Top Contributing Factors
While 'No improper driving' remained the most common circumstance in both periods, its count decreased from 88 in 2021 to 81 in 2022. The second-most cited contributing factor shifted from 'Failed to yield right of way' in 2021 (31 crashes) to 'Inattention' in 2022 (26 crashes). The count of crashes involving inattention increased by 23.8%, from 21 to 26, while crashes involving a driver who 'Failed to yield right of way' decreased by 22.6%, from 31 to 24.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes in both 2021 and 2022 predominantly occurred in clear and dry conditions. Collisions on dry road surfaces increased from 196 to 203, while those on wet surfaces decreased from 47 to 36. Similarly, crashes in daylight conditions were most common, with 181 incidents in 2022 compared to 179 in 2021. Crashes occurring in 'Dark - lighted roadway' conditions saw a notable decrease from 40 in 2021 to 28 in 2022.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Road surface condition field
Vehicles & Demographics
The top five vehicle makes involved in crashes—Toyota, Ford, Honda, Chevrolet, and Jeep—were consistent across both years, with only minor changes in their rank order. Toyota was the most frequently involved make in both 2021 (93 vehicles) and 2022 (82 vehicles). Among persons involved in crashes, the 65+ age group saw a notable increase in representation, growing from 66 individuals in 2021 to 85 in 2022.
Top Vehicle Makes (454 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records
31 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (489 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Person-level records linked to crash events
Speed Limit Zones
In 2022, crashes were most frequent in 35 mph zones (66 crashes) and 30 mph zones (62 crashes), a shift from 2021 where 30 mph zones saw the highest number of incidents (86 crashes). The two fatal crashes in 2022 occurred in 30 mph and 60 mph speed zones. This contrasts with 2021, where the single fatal crash occurred in a 30 mph zone, marking the appearance of a fatality in a high-speed zone in the current period.
Fatal crashes by zone: 30 mph: 1 of 62 (1.613%) · 60 mph: 1 of 32 (3.125%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · 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: 2022-01-01 through 2022-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-01-01 through 2022-12-31 (365 days)
- Geographic scope: KINGSTON, MA
- Total crash records analyzed: 251
- Total persons involved: 538
- Total vehicles involved: 454
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: 2022." Published June 21, 2026. Reporting period: 2022-01-01 to 2022-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/kingston/2022-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: 2022-01-01 – 2022-12-31
Generated: June 21, 2026 · All rights reserved