Yearly Traffic Safety Analysis

166 CRASHES IN
SHERBORN, MA
2024

All metrics benchmarked against2023

In 2024, Sherborn recorded 166 total traffic crashes, a 23.9% increase from the 134 crashes reported in 2023. Total injuries also rose from 31 to 44 during this period. The most significant change was the occurrence of one fatal crash in 2024, whereas no fatalities were recorded in the prior year.

166

23.9%was 134

Total Crash Events

1

Persons Killed

44

41.9%was 31

Persons Injured

4

-20.0%was 5

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. 2 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

Year-over-year data indicates a rising trend in traffic incidents in Sherborn. Total crashes increased by 23.9%, from 134 in 2023 to 166 in 2024. Similarly, the number of people injured in these crashes rose by 41.9%, from 31 to 44, and one fatality was recorded in 2024 compared to none in the previous year.

4

Hit-and-Run Crashes — 2024

-20.0% vs prior (5)

The number of hit-and-run incidents in Sherborn showed a slight decrease year-over-year. In 2024, there were 4 hit-and-run crashes, down from 5 in 2023. Correspondingly, the hit-and-run rate as a percentage of total crashes declined from 3.7% in the prior year to 2.4% in the current year.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 0%

44

Motorists Injured

Prior: 3141.9%

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 temporal patterns of crashes shifted between the two periods. The peak day for crashes moved from Tuesday (24 crashes) in 2023 to Friday (35 crashes) in 2024. While the peak hour remained the 5 p.m. hour in both years, the number of incidents during that time increased from 13 to 15. Crashes on Fridays more than doubled, increasing from 17 in the prior year to 35 in the current year.

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 increased in 2024 compared to 2023. One fatal crash occurred in 2024, representing 0.6% of all incidents, whereas no fatal crashes were recorded in the prior year. The number of serious injury crashes more than doubled from 2 to 5, with their share of total crashes rising from 1.5% to 3.0%. Minor injury crashes also saw an increase in both count, from 14 to 25, and proportion, from 10.4% to 15.1%.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.6%
Serious Injury5serious injury crashes3%
150.0%prior 2
Minor Injury25minor injury crashes15.1%
78.6%prior 14
Possible Injury6possible injury crashes3.6%
-33.3%prior 9
No Injury127no injury crashes76.5%
18.7%prior 107

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 2024, 'Inattention' became the leading contributing factor with 25 crashes, an increase from 16 crashes in 2023. 'Driving too fast for conditions' also saw a notable rise in count, from 9 incidents to 16. Similarly, crashes attributed to 'Failure to keep in proper lane or running off road' increased from 7 to 13. While 'Failed to yield right of way' remained a top factor, its count only slightly increased from 16 to 18 incidents.

Officer-Reported Primary Contributing Cause

No improper driving34 (20.5%)-10.5%prior 38
Inattention25 (15.1%)56.3%prior 16
Failed to yield right of way18 (10.8%)12.5%prior 16
Driving too fast for conditions16 (9.6%)77.8%prior 9
Failure to keep in proper lane or running off road13 (7.8%)85.7%prior 7
Followed too closely8 (4.8%)0.0%prior 8
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner7 (4.2%)40.0%prior 5
Disregarded traffic signs, signals, road markings6 (3.6%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway5 (3%)
Fatigued/asleep4 (2.4%)

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

While most crashes in both periods occurred in clear weather on dry roads during daylight, there was a notable increase in crashes under adverse conditions. Crashes on snowy road surfaces increased from 5 in 2023 to 22 in 2024, and those attributed to snowy weather conditions rose from 3 to 11. Incidents in the dark on unlit roadways also increased from 17 to 28. Concurrently, crashes during daylight hours rose from 88 to 108.

Weather

Clear109 (65.7%)
9.0%prior 100
Cloudy15 (9.0%)
87.5%prior 8
Snow11 (6.6%)
Cloudy/Rain9 (5.4%)
28.6%prior 7
Snow/Sleet, hail (freezing rain or drizzle)7 (4.2%)
Rain4 (2.4%)
-55.6%prior 9
Rain/Cloudy4 (2.4%)
Cloudy/Snow2 (1.2%)
Sleet, hail (freezing rain or drizzle)/Cloudy1 (0.6%)
Rain/Fog, smog, smoke1 (0.6%)

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

Lighting

Daylight108 (65.1%)
22.7%prior 88
Dark - roadway not lighted28 (16.9%)
64.7%prior 17
Dark - lighted roadway20 (12.0%)
11.1%prior 18
Dusk5 (3.0%)
-16.7%prior 6
Dark - unknown roadway lighting3 (1.8%)
Dawn2 (1.2%)

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

Road Surface

Dry119 (71.7%)
19.0%prior 100
Snow22 (13.3%)
340.0%prior 5
Wet20 (12.0%)
-13.0%prior 23
Ice4 (2.4%)
Slush1 (0.6%)

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

Vehicles & Demographics

The ranking of the top three vehicle makes involved in crashes remained consistent year-over-year, with Toyota, Honda, and Ford leading in both periods; however, the count for each make increased. Toyota-involved crashes rose from 31 to 45. Regarding driver and passenger demographics, the 26-34 age group was the most frequently involved in both years, with counts rising from 40 to 49. Notably, the number of individuals aged 65 and older involved in crashes increased from 29 to 43.

Top Vehicle Makes (253 vehicles)

1
TOYOTA45 (17.8%)
45.2%prior 31
2
HONDA32 (12.6%)
18.5%prior 27
3
FORD24 (9.5%)
14.3%prior 21
4
SUBARU21 (8.3%)
162.5%prior 8
5
CHEVROLET20 (7.9%)
53.8%prior 13
6
NISSAN12 (4.7%)
50.0%prior 8
7
JEEP10 (4%)
-28.6%prior 14
8
BMW9 (3.6%)
9
KIA7 (2.8%)
10
GMC7 (2.8%)
0.0%prior 7

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

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

Sex Distribution (303 persons with recorded sex)

Male165 (54.5%)
9.3%prior 151
Female138 (45.5%)
42.3%prior 97

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

Crashes increased across several speed zones from 2023 to 2024. The 30 mph zone remained the location with the highest number of crashes, rising from 57 to 65 incidents. The most significant proportional increase occurred in 35 mph zones, where crashes rose from 18 to 32. The single fatality recorded in 2024 occurred in a 35 mph zone; no fatalities were recorded in any speed zone in the prior year.

Fatal crashes by zone: 35 mph: 1 of 32 (3.125%)

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: SHERBORN, MA
  • Total crash records analyzed: 166
  • Total persons involved: 312
  • Total vehicles involved: 253

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). "SHERBORN, 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/sherborn/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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Sherborn, MA Crash Report — 2024 | ThatCarHitMe.com