Monthly Traffic Safety Analysis

44 CRASHES IN
FOXBOROUGH, MA
AUGUST 2024

All metrics benchmarked againstAugust 2023

FOXBOROUGH experienced an increase in total crashes from 36 in August 2023 to 44 in August 2024, representing a 22.22% rise. This period also saw a significant 100% increase in total injuries, from 8 to 16, and the occurrence of one fatality in August 2024 compared to none in the prior year.

44

22.2%was 36

Total Crash Events

1

Persons Killed

16

100.0%was 8

Persons Injured

2

-33.3%was 3

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. 1 crash with unreported severity is not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-08-01 to 2024-08-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates an increase in crash activity year-over-year, with total crashes rising by 8 incidents, from 36 to 44. Concurrently, total fatalities increased from 0 to 1, and total injuries doubled from 8 to 16, signaling a worsening safety trend.

2

Hit-and-Run Crashes — August 2024

-33.3% vs prior (3)

Hit-and-run crashes decreased from 3 incidents in August 2023 to 2 incidents in August 2024. Consequently, the hit-and-run rate decreased from 8.3% in the prior period to 4.5% in the current period, indicating a downward trend in these types of incidents.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 0%

16

Motorists Injured

Prior: 6166.7%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-08-01 to 2024-08-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes remained Friday in both periods, with 9 crashes in August 2023 and 12 in August 2024. The peak hour for crashes shifted from 4 PM with 4 incidents in the prior period to 5 PM with 6 incidents in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-08-01 to 2024-08-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-08-01 to 2024-08-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatal crashes increased from 0 in August 2023 to 1 in August 2024, resulting in a fatal crash rate of 2.27% for the current period. The proportion of minor injury crashes slightly increased from 11.1% to 13.6%, while possible injury crashes saw a minor decrease from 8.3% to 6.8%. Overall, the number of injury-related crashes (minor and possible) rose from 7 to 9.

Outcome by Severity (Crash Events)

Fatal1fatal crashes2.3%
Minor Injury6minor injury crashes13.6%
50.0%prior 4
Possible Injury3possible injury crashes6.8%
0.0%prior 3
No Injury33no injury crashes75%
17.9%prior 28

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-08-01 to 2024-08-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-08-01 to 2024-08-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factor, 'Inattention,' saw a substantial increase from 4 crashes in August 2023 to 12 crashes in August 2024, a 200% rise, moving it to the top rank. Conversely, 'Followed too closely' crashes decreased by 5, from 8 to 3, representing a 62.5% reduction and dropping from the top factor to fourth. 'No improper driving' and 'Failure to keep in proper lane or running off road' both saw slight increases in count, from 4 to 6 and 4 to 5 crashes respectively.

Officer-Reported Primary Contributing Cause

Inattention12 (27.3%)
No improper driving6 (13.6%)
Failure to keep in proper lane or running off road5 (11.4%)
Followed too closely3 (6.8%)-62.5%prior 8
Disregarded traffic signs, signals, road markings2 (4.5%)
Distracted2 (4.5%)
Fatigued/asleep2 (4.5%)
Wrong side or wrong way2 (4.5%)
Failed to yield right of way1 (2.3%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (2.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-08-01 to 2024-08-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in clear weather conditions increased from 24 to 32, and those on dry road surfaces increased from 28 to 35. Crashes during daylight hours also rose from 24 to 36. The proportion of crashes occurring in dark conditions (lighted or unlighted roadways) decreased from approximately 30.6% in August 2023 to 18.2% in August 2024.

Weather

Clear32 (72.7%)
33.3%prior 24
Rain4 (9.1%)
-20.0%prior 5
Rain/Cloudy4 (9.1%)
Cloudy3 (6.8%)
Cloudy/Rain1 (2.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-08-01 to 2024-08-31 · Weather condition at time of crash

Lighting

Daylight36 (81.8%)
50.0%prior 24
Dark - lighted roadway6 (13.6%)
-33.3%prior 9
Dark - roadway not lighted2 (4.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-08-01 to 2024-08-31 · Lighting condition field

Road Surface

Dry35 (79.5%)
25.0%prior 28
Wet9 (20.5%)
12.5%prior 8

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-08-01 to 2024-08-31 · Road surface condition field

Vehicles & Demographics

Toyota crashes significantly increased from 8 to 23 incidents, an increase of 187.5%, making it the most involved make in the current period. Honda crashes decreased from 10 to 7, a 30% reduction, shifting its rank from first to second. A notable shift in person demographics occurred in the 45-54 age group, which saw an increase from 6 persons involved in crashes in the prior period to 20 persons in the current period.

Top Vehicle Makes (81 vehicles)

1
TOYOTA23 (28.4%)
187.5%prior 8
2
HONDA7 (8.6%)
-30.0%prior 10
3
CHEVROLET5 (6.2%)
-16.7%prior 6
4
FORD5 (6.2%)
-37.5%prior 8
5
HYUNDAI4 (4.9%)
6
KIA4 (4.9%)
7
NISSAN3 (3.7%)
8
DODGE2 (2.5%)
9
JEEP2 (2.5%)
10
MAZDA2 (2.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-08-01 to 2024-08-31 · Vehicle unit records

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

Sex Distribution (100 persons with recorded sex)

Male57 (57.0%)
3.6%prior 55
Female43 (43.0%)
38.7%prior 31

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-08-01 to 2024-08-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in the 65 mph speed zone increased from 10 to 11, and this zone recorded the sole fatality in the current period, compared to zero fatalities in this zone previously. Crashes in the 30 mph zone slightly decreased from 9 to 8. The 50 mph speed zone also saw an increase in crashes, rising from 1 to 6 incidents.

Fatal crashes by zone: 65 mph: 1 of 11 (9.091%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-08-01 to 2024-08-31 · 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: 2024-08-01 through 2024-08-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2024-08-01 through 2024-08-31 (31 days)
  • Geographic scope: FOXBOROUGH, MA
  • Total crash records analyzed: 44
  • Total persons involved: 106
  • Total vehicles involved: 81

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). "FOXBOROUGH, MA Crash Intelligence Report: August 2024." Published June 21, 2026. Reporting period: 2024-08-01 to 2024-08-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/foxborough/august-2024-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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