Yearly Traffic Safety Analysis

305 CRASHES IN
NANTUCKET, MA
2024

All metrics benchmarked against2023

In Nantucket, total traffic crashes increased by 20.1% from 254 in 2023 to 305 in 2024. The most significant year-over-year change was the occurrence of one fatal crash in 2024, whereas there were no fatalities recorded in the prior year. Despite the increase in total and fatal crashes, the number of persons injured remained stable, decreasing slightly from 52 to 50.

305

20.1%was 254

Total Crash Events

1

Persons Killed

50

-3.8%was 52

Persons Injured

43

19.4%was 36

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. 36 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 Nantucket show a notable increase in the number of crashes year-over-year. Collisions rose from 254 in 2023 to 305 in 2024, representing a 20.1% increase. While the total number of injuries saw a slight decrease from 52 to 50, the city recorded one fatality in 2024 after having none in the previous year.

43

Hit-and-Run Crashes — 2024

19.4% vs prior (36)

The absolute number of hit-and-run crashes increased from 36 in 2023 to 43 in 2024, a rise of 19.4%. However, because total crashes also increased significantly, the hit-and-run rate as a percentage of all crashes remained nearly unchanged. The rate was 14.2% in 2023 and decreased slightly to 14.1% in 2024, indicating that the frequency of hit-and-runs grew proportionally with the overall increase in collisions.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

1

Motorists Killed

Prior: 0%

8

Pedestrians Injured

Prior: 2300.0%

10

Cyclists Injured

Prior: 911.1%

32

Motorists Injured

Prior: 38-15.8%

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 shifted between the two periods. In 2024, Monday was the most common day for crashes with 56 incidents, a change from 2023 when Friday saw the highest frequency at 44 crashes. The peak hour for collisions moved slightly later in the day, from the 1 PM hour in 2023 (28 crashes) to the 2 PM hour in 2024 (29 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

Crash severity worsened in 2024, marked by the city's first fatal crash in this two-year period. In 2023, there were no fatal or serious injury crashes reported, but 2024 saw 1 fatal crash and 7 serious injury crashes. This increase in high-severity incidents occurred even as the total count of minor injury crashes dropped from 34 in 2023 to 27 in 2024.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.3%
Serious Injury7serious injury crashes2.3%
Minor Injury27minor injury crashes8.9%
-20.6%prior 34
Possible Injury11possible injury crashes3.6%
57.1%prior 7
No Injury223no injury crashes73.1%
29.7%prior 172

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 in ranking, with 'Failed to yield right of way' and 'Inattention' being the most common after crashes with no improper driving cited. However, the counts for these factors increased; crashes from failing to yield grew by 30% from 30 to 39 incidents, and those involving inattention rose by 36.8% from 19 to 26. Notably, crashes involving an 'erratic, reckless, careless, negligent or aggressive' driving manner doubled in count, from 6 in 2023 to 12 in 2024.

Officer-Reported Primary Contributing Cause

No improper driving83 (27.2%)45.6%prior 57
Failed to yield right of way39 (12.8%)30.0%prior 30
Inattention26 (8.5%)36.8%prior 19
Failure to keep in proper lane or running off road15 (4.9%)15.4%prior 13
Other improper action13 (4.3%)8.3%prior 12
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner12 (3.9%)100.0%prior 6
Made an improper turn8 (2.6%)-11.1%prior 9
Followed too closely8 (2.6%)-33.3%prior 12
Driving too fast for conditions7 (2.3%)
Exceeded authorized speed limit6 (2%)-40.0%prior 10

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

In both years, the majority of crashes occurred during daylight hours on dry roads. In 2024, 71.8% of crashes happened in daylight, a slight proportional increase from 68.9% in 2023. Crashes on dry roads accounted for 78.0% of the total in 2024, compared to 81.9% in the prior year. The proportion of crashes occurring under adverse weather or road surface conditions remained relatively stable year-over-year.

Weather

Clear/Clear178 (58.9%)
38.0%prior 129
Clear62 (20.5%)
29.2%prior 48
Cloudy13 (4.3%)
85.7%prior 7
Cloudy/Cloudy12 (4.0%)
-57.1%prior 28
Rain8 (2.6%)
Rain/Rain6 (2.0%)
-25.0%prior 8
Clear/Cloudy5 (1.7%)
Unknown/Unknown3 (1.0%)
-50.0%prior 6
Cloudy/Rain3 (1.0%)
-40.0%prior 5
Snow/Snow3 (1.0%)

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

Lighting

Daylight219 (72.8%)
25.1%prior 175
Dark - roadway not lighted32 (10.6%)
23.1%prior 26
Dark - lighted roadway29 (9.6%)
16.0%prior 25
Dusk11 (3.7%)
10.0%prior 10
Dark - unknown roadway lighting7 (2.3%)
-22.2%prior 9
Dawn3 (1.0%)

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

Road Surface

Dry238 (80.7%)
14.4%prior 208
Wet23 (7.8%)
4.5%prior 22
Sand, mud, dirt, oil, gravel21 (7.1%)
110.0%prior 10
Ice5 (1.7%)
Snow3 (1.0%)
Other2 (0.7%)
Slush2 (0.7%)
Water (standing, moving)1 (0.3%)

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

Vehicles & Demographics

The top three vehicle makes involved in crashes were Ford, Jeep, and Toyota across both years, with Ford being the most frequent in both periods. In 2024, Toyota (84 vehicles) surpassed Jeep (80 vehicles) for the second-most common make, reversing their 2023 ranking. Analysis of persons involved shows a significant increase in the 35-44 age group, which grew from 83 individuals in 2023 to 122 in 2024, a 47% rise.

Top Vehicle Makes (509 vehicles)

1
FORD142 (27.9%)
31.5%prior 108
2
TOYOTA84 (16.5%)
25.4%prior 67
3
JEEP80 (15.7%)
15.9%prior 69
4
CHEVROLET35 (6.9%)
45.8%prior 24
5
HONDA19 (3.7%)
-9.5%prior 21
6
NISSAN15 (2.9%)
7.1%prior 14
7
RAM14 (2.8%)
8
DODGE11 (2.2%)
10.0%prior 10
9
KIA8 (1.6%)
10
VOLKSWAGEN7 (1.4%)
40.0%prior 5

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

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

Sex Distribution (575 persons with recorded sex)

Male368 (64.0%)
21.9%prior 302
Female207 (36.0%)
31.8%prior 157

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

In both years, a significant number of crashes with a recorded speed limit occurred in very low-speed environments, particularly in 5 mph zones which saw 23 crashes in 2023 and 6 in 2024. The 2023 data includes unusual speed limits such as 1, 2, 3, and 85 mph, which were not present in the 2024 data, making a direct trend analysis difficult. The single fatal crash in 2024 is not listed within the provided speed zone data.

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: NANTUCKET, MA
  • Total crash records analyzed: 305
  • Total persons involved: 661
  • Total vehicles involved: 509

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). "NANTUCKET, 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/nantucket/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

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Nantucket, MA Crash Report — 2024 | ThatCarHitMe.com