Yearly Traffic Safety Analysis

267 CRASHES IN
KINGSTON, MA
2024

All metrics benchmarked against2023

In 2024, Kingston recorded 267 total traffic crashes, a 2.7% increase from the 260 crashes reported in 2023. While total crashes saw a slight rise, the number of reported injuries increased more substantially by 30.1%, from 83 in the prior year to 108 in the current year. The most significant change was observed in the number of serious injury crashes, which increased from 3 in 2023 to 11 in 2024.

267

2.7%was 260

Total Crash Events

1

Persons Killed

108

30.1%was 83

Persons Injured

12

20.0%was 10

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. 5 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic safety trends in Kingston show a slight increase in crash volume year-over-year. Total crashes rose from 260 in 2023 to 267 in 2024, an increase of 2.7%. The number of individuals injured in these crashes grew by 30.1%, while fatalities remained stable with one death recorded in each period.

12

Hit-and-Run Crashes — 2024

20.0% vs prior (10)

Hit-and-run incidents showed an upward trend in Kingston. The total number of hit-and-run crashes increased from 10 in 2023 to 12 in 2024, a 20% increase in count. Consequently, the hit-and-run rate, which measures the proportion of all crashes that are hit-and-runs, also rose from 3.8% to 4.5% year-over-year.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 10.0%

108

Motorists Injured

Prior: 8035.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes in Kingston shifted between the two periods. In 2024, the peak day for crashes was Thursday with 53 incidents, a change from the previous year's peak on Tuesday with 49 incidents. The most frequent crash hour also moved earlier in the day, from the 4 p.m. hour in 2023 (29 crashes) to the 2 p.m. hour in 2024 (26 crashes).

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the number of fatal crashes remained constant at one for both years, the distribution of injury severity shifted. The proportion of crashes resulting in serious injuries increased significantly, rising from 1.2% of total crashes (3 incidents) in 2023 to 4.1% (11 incidents) in 2024. Correspondingly, the share of crashes with no injuries decreased from 72.3% in the prior year to 68.9% in the current year.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.4%
0.0%prior 1
Serious Injury11serious injury crashes4.1%
266.7%prior 3
Minor Injury48minor injury crashes18%
0.0%prior 48
Possible Injury18possible injury crashes6.7%
38.5%prior 13
No Injury184no injury crashes68.9%
-2.1%prior 188

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record

Top Contributing Factors

The top contributing factors remained consistent, with 'No improper driving' cited most often in both 2024 (84 crashes) and 2023 (104 crashes). However, there were notable shifts in other driver behaviors. Crashes attributed to 'Disregarded traffic signs, signals, road markings' increased from 1 crash in 2023 to 11 in 2024. Similarly, crashes involving a 'Distracted' driver rose from 2 to 9, and those involving 'Failed to yield right of way' increased from 23 to 28 incidents.

Officer-Reported Primary Contributing Cause

No improper driving84 (31.5%)-19.2%prior 104
Inattention31 (11.6%)-13.9%prior 36
Failed to yield right of way28 (10.5%)21.7%prior 23
Followed too closely17 (6.4%)6.3%prior 16
Disregarded traffic signs, signals, road markings11 (4.1%)
Failure to keep in proper lane or running off road11 (4.1%)0.0%prior 11
Distracted9 (3.4%)
Made an improper turn6 (2.2%)
Exceeded authorized speed limit5 (1.9%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway4 (1.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The majority of crashes in both periods occurred in clear weather and on dry roads. In 2024, 82.0% of crashes happened on dry surfaces, up from 73.1% in 2023, while crashes on wet surfaces decreased from 65 to 40. Similarly, crashes during rainfall decreased from 33 in the prior year to 22 in the current year. The distribution of crashes by lighting condition remained largely unchanged, with daylight crashes accounting for 178 incidents in 2024 compared to 173 in 2023.

Weather

Clear182 (68.7%)
5.2%prior 173
Cloudy37 (14.0%)
19.4%prior 31
Rain22 (8.3%)
-33.3%prior 33
Snow5 (1.9%)
Clear/Clear4 (1.5%)
Cloudy/Cloudy3 (1.1%)
Rain/Cloudy3 (1.1%)
Rain/Snow2 (0.8%)
Fog, smog, smoke2 (0.8%)
Clear/Rain1 (0.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash

Lighting

Daylight178 (66.9%)
2.9%prior 173
Dark - lighted roadway36 (13.5%)
5.9%prior 34
Dark - roadway not lighted31 (11.7%)
-13.9%prior 36
Dusk11 (4.1%)
57.1%prior 7
Dawn8 (3.0%)
33.3%prior 6
Dark - unknown roadway lighting2 (0.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field

Road Surface

Dry219 (82.0%)
15.3%prior 190
Wet40 (15.0%)
-38.5%prior 65
Snow5 (1.9%)
0.0%prior 5
Ice2 (0.7%)
Water (standing, moving)1 (0.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field

Vehicles & Demographics

Toyota remained the most common vehicle make involved in crashes in both years, with 78 vehicles in 2024 and 79 in 2023. Ford's involvement decreased from 63 to 48 vehicles, while Chevrolet's increased from 38 to 47. The age demographics of people involved in crashes also shifted, with a notable increase in the 35-44 age group (from 74 to 94 people) and the 0-15 age group (from 39 to 81 people). Conversely, the number of people aged 65 and older involved in crashes decreased from 90 to 77.

Top Vehicle Makes (477 vehicles)

1
TOYOTA78 (16.4%)
-1.3%prior 79
2
FORD48 (10.1%)
-23.8%prior 63
3
CHEVROLET47 (9.9%)
23.7%prior 38
4
HONDA41 (8.6%)
7.9%prior 38
5
NISSAN32 (6.7%)
-13.5%prior 37
6
JEEP30 (6.3%)
-25.0%prior 40
7
HYUNDAI27 (5.7%)
42.1%prior 19
8
VOLKSWAGEN13 (2.7%)
30.0%prior 10
9
MERCEDES-BENZ13 (2.7%)
8.3%prior 12
10
SUBARU11 (2.3%)
-42.1%prior 19

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records

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

Sex Distribution (600 persons with recorded sex)

Male317 (52.8%)
15.7%prior 274
Female282 (47.0%)
13.3%prior 249
X / Unspecified1 (0.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Person-level records linked to crash events

Speed Limit Zones

The distribution of crashes across speed zones showed some changes year-over-year. While the 30 mph zone had the highest number of crashes in both periods (78 incidents each year), crashes in 35 mph zones increased from 47 to 59. The single fatal crash in 2024 occurred in a 60 mph zone, whereas the fatal crash in 2023 took place in a 30 mph zone. Crashes in 25 mph zones saw a decrease from 35 to 31.

Fatal crashes by zone: 60 mph: 1 of 39 (2.564%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · 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: 2024-01-01 through 2024-12-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2024-01-01 through 2024-12-31 (366 days)
  • Geographic scope: KINGSTON, MA
  • Total crash records analyzed: 267
  • Total persons involved: 647
  • Total vehicles involved: 477

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: 2024." Published June 21, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/kingston/2024-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

Kingston, MA Crash Report — 2024 | ThatCarHitMe.com