ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · FOXBOROUGH, MA · 2024
Purpose: Machine-readable JSON endpoint for AI agents, LLMs, researchers, and programmatic consumers. Returns all underlying crash data and AI-generated commentary without HTML.
Authentication: None required. Public endpoint.
GET: https://thatcarhitme.com/api/crash-data/reports/data/massachusetts/foxborough/2024-annual-report
Yearly Traffic Safety Analysis
560 CRASHES IN
FOXBOROUGH, MA
2024
In Foxborough, total crashes increased by 3.5% from 541 in 2023 to 560 in 2024. While total injuries decreased from 182 to 169, the most significant year-over-year change was a substantial increase in traffic fatalities, which rose from 1 to 5.
560
▲ 3.5%was 541
Total Crash Events
5
▲ 400.0%was 1
Persons Killed
169
▼ -7.1%was 182
Persons Injured
35
▲ 12.9%was 31
Hit-and-Run Crashes
Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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 crash volume in Foxborough saw a modest year-over-year increase of 3.5%, rising from 541 incidents in 2023 to 560 in 2024. While total injuries decreased by 7.1% from 182 to 169, the number of fatalities increased from 1 to 5, indicating a rise in crash severity despite a relatively stable total crash count.
35
Hit-and-Run Crashes — 2024
▲ 12.9% vs prior (31)
Hit-and-run incidents in Foxborough showed an upward trend in 2024 compared to the prior year. The total number of hit-and-run crashes increased from 31 to 35. This corresponds to an increase in the hit-and-run rate, which rose from 5.7% of all crashes in 2023 to 6.3% in 2024.
Vulnerable Road User Casualties
0
Pedestrians Killed
5
Motorists Killed
0
Other Killed
3
Pedestrians Injured
164
Motorists Injured
2
Other Injured
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 in Foxborough remained largely consistent year-over-year. Thursday was the peak day for crashes in both 2023 (94 crashes) and 2024 (104 crashes). Similarly, the 4 p.m. hour was the peak time for collisions in both periods, with 72 crashes in 2023 and 71 in 2024, indicating a stable afternoon commute risk profile.
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
The severity of crashes worsened significantly in 2024, with the number of fatal crashes increasing from 1 to 4 and total fatalities rising from 1 to 5. This pushed the fatal crash rate up from 0.18% to 0.71%. While the number of crashes involving serious injuries remained unchanged at 12, crashes resulting in minor or possible injuries saw a combined decrease from 121 in 2023 to 108 in 2024.
Severity is per crash event (most severe injury). 4 fatal crash events resulted in 5 persons killed.
Outcome by Severity (Crash Events)
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 three primary contributing factors remained consistent between periods: 'No improper driving', 'Followed too closely', and 'Inattention'. However, the counts for some key factors shifted; crashes attributed to 'Followed too closely' decreased from a count of 91 to 86, and 'Inattention' dropped from 89 to 81. Conversely, crashes where a driver 'Failed to yield right of way' increased by 23.5%, from 51 incidents in 2023 to 63 in 2024.
Officer-Reported Primary Contributing Cause
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
Crash conditions remained broadly similar year-over-year, with the majority of incidents in both periods occurring in clear weather on dry roads during daylight hours. The proportion of crashes happening in daylight increased from 65.1% of all crashes in 2023 (352 incidents) to 70.2% in 2024 (393 incidents). Similarly, the share of crashes on dry roads rose slightly from 80.0% to 81.3%.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field
Vehicles & Demographics
The vehicle makes most frequently involved in crashes were consistent, with Toyota, Ford, and Honda comprising the top three in both years, though Honda and Ford swapped second and third place. The number of Toyotas involved increased from 159 to 180. Regarding the age of people involved in crashes, there was a notable shift in older demographics; the number of individuals aged 65 and over increased by 23.3% from 120 to 148, while the 55-64 age group saw a 14.7% decrease from 170 to 145.
Top Vehicle Makes (1,061 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records
74 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (1,231 persons with recorded sex)
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
While the total number of crashes in the 65 mph speed zone decreased from 183 to 171, the number of fatal crashes within that zone doubled from one to two. The year-over-year comparison also shows a new emergence of fatalities in lower speed zones. In 2024, one fatal crash was recorded in the 35 mph zone and another in the 45 mph zone, whereas no fatalities occurred in these zones in 2023.
Fatal crashes by zone: 35 mph: 1 of 65 (1.538%) · 45 mph: 1 of 39 (2.564%) · 65 mph: 2 of 171 (1.17%)
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: FOXBOROUGH, MA
- Total crash records analyzed: 560
- Total persons involved: 1,336
- Total vehicles involved: 1,061
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: 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/foxborough/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
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly
Period: 2024-01-01 – 2024-12-31
Generated: June 21, 2026 · All rights reserved