Yearly Traffic Safety Analysis

15 CRASHES IN
OAKHAM, MA
2024

All metrics benchmarked against2023

In Oakham, total vehicle crashes decreased by 25%, from 20 incidents in the prior year to 15 in the current period. There were no fatalities recorded in either year, and the number of people injured saw a significant year-over-year reduction of 75%, falling from 8 to 2.

15

-25.0%was 20

Total Crash Events

0

Persons Killed

2

-75.0%was 8

Persons Injured

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.

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

The overall trend in traffic incidents shows a notable decline year-over-year. Total crashes fell by 25%, from 20 in the prior period to 15 in the current year. This downward trend was also reflected in crash outcomes, with total injuries decreasing by 75% from 8 to 2, while fatalities remained at zero for both periods.

1

Hit-and-Run Crashes — 2024

6.7% hit-and-run rate this period vs 0.0% prior. Prior period: 0.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

2

Motorists Injured

Prior: 8-75.0%

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 showed some shifts between the two periods. The peak day for crashes moved from Wednesday (5 crashes) in the prior year to a three-way tie between Tuesday, Thursday, and Friday (3 crashes each) in the current year. Similarly, the single busiest hour for incidents shifted an hour earlier, from 5 PM in the prior period to 4 PM in the current period, which saw 4 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 improved year-over-year, with no fatal incidents recorded in either period. The proportion of crashes resulting in an injury decreased from 30% in the prior year (6 of 20 crashes) to 13.3% in the current year (2 of 15 crashes). Correspondingly, the share of crashes with no reported injuries increased from 60% to 86.7%.

Outcome by Severity (Crash Events)

Minor Injury2minor injury crashes13.3%
0.0%prior 2
No Injury13no injury crashes86.7%
8.3%prior 12

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

In both periods, 'No improper driving' was the most frequently cited factor, though its count decreased from 12 crashes in the prior year to 8 in the current year. 'Failed to yield right of way' emerged as a notable factor in the current period with 2 incidents, whereas it was not listed in the prior year's top factors. Conversely, factors such as 'Fatigued/asleep' and 'Driving too fast for conditions,' which accounted for 2 and 1 crashes respectively in the prior year, were not present in the current year's data.

Officer-Reported Primary Contributing Cause

No improper driving8 (53.3%)-33.3%prior 12
Failed to yield right of way2 (13.3%)
Inattention1 (6.7%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway1 (6.7%)

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 proportion of crashes occurring on adverse road surfaces increased from 45% in the prior year to 53.3% in the current year, with 8 of 15 crashes happening on wet, snowy, icy, or slushy roads. While the share of crashes in daylight remained stable at around 50% for both periods, the percentage of incidents occurring in adverse weather conditions (such as snow, sleet, or rain) grew from 35% to 46.7% year-over-year. The share of crashes on dry roads decreased from 55% to 46.7%.

Weather

Clear7 (46.7%)
-22.2%prior 9
Sleet, hail (freezing rain or drizzle)2 (13.3%)
Snow/Blowing sand, snow2 (13.3%)
Sleet, hail (freezing rain or drizzle)/Snow1 (6.7%)
Fog, smog, smoke1 (6.7%)
Clear/Cloudy1 (6.7%)
Clear/Rain1 (6.7%)

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

Lighting

Daylight7 (46.7%)
-30.0%prior 10
Dark - roadway not lighted6 (40.0%)
-33.3%prior 9
Dawn1 (6.7%)
Dusk1 (6.7%)

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

Road Surface

Dry7 (46.7%)
-36.4%prior 11
Snow3 (20.0%)
Ice2 (13.3%)
Wet2 (13.3%)
-71.4%prior 7
Slush1 (6.7%)

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

Vehicles & Demographics

Top Vehicle Makes (18 vehicles)

1
TOYOTA4 (22.2%)
2
SUBARU3 (16.7%)
3
CHEVROLET3 (16.7%)
4
FORD2 (11.1%)
5
ICRP1 (5.6%)
6
BMW1 (5.6%)
7
CADI1 (5.6%)
8
DODGE1 (5.6%)
9
HONDA1 (5.6%)

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

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

Sex Distribution (19 persons with recorded sex)

Male14 (73.7%)
-17.6%prior 17
Female5 (26.3%)
-58.3%prior 12

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 40 mph speed zone remained the most frequent location for crashes in both periods, accounting for 9 incidents in the prior year and 8 in the current year. There was a decrease in crashes within 30 mph zones, from 4 incidents to 2. Conversely, crashes in the 50 mph zone increased from 1 to 2. No fatal crashes were recorded in any speed zone during either period.

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: OAKHAM, MA
  • Total crash records analyzed: 15
  • Total persons involved: 21
  • Total vehicles involved: 18

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). "OAKHAM, 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/oakham/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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Oakham, MA Crash Report — 2024 | ThatCarHitMe.com