ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · FOXBOROUGH, MA · APRIL 2023
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/april-2023-report
Monthly Traffic Safety Analysis
44 CRASHES IN
FOXBOROUGH, MA
APRIL 2023
In April 2023, FOXBOROUGH experienced 44 crashes, a significant increase from the 26 crashes recorded in April 2022, representing a 69.23% rise year-over-year. Total injuries also rose sharply, from 6 in the prior period to 19 in the current period. Notably, both periods reported zero traffic fatalities.
44
▲ 69.2%was 26
Total Crash Events
0
Persons Killed
19
▲ 216.7%was 6
Persons Injured
3
▲ 200.0%was 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 · 2023-04-01 to 2023-04-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend indicates a substantial increase in crash activity in FOXBOROUGH, with total crashes rising by 69.23% from 26 to 44 year-over-year. Correspondingly, total injuries saw a dramatic increase of 216.67%, climbing from 6 to 19 persons. Fatalities remained at zero for both April 2022 and April 2023.
3
Hit-and-Run Crashes — April 2023
▲ 200.0% vs prior (1)
The number of hit-and-run crashes increased from 1 in April 2022 to 3 in April 2023. Concurrently, the hit-and-run rate rose from 3.8% of total crashes in the prior period to 6.8% in the current period, indicating an upward trend.
Vulnerable Road User Casualties
0
Motorists Killed
19
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The peak day for crashes shifted from Friday with 8 crashes in April 2022 to Saturday with 9 crashes in April 2023. While 4 PM remained the peak hour for crashes in both periods, the count increased from 4 crashes in April 2022 to 6 crashes in April 2023. Crashes on Sunday and Saturday saw notable increases, rising from 2 to 8 and 3 to 9 respectively.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Fatal crashes remained at zero in both April 2022 and April 2023. However, serious injuries increased from 0 to 3, and minor injuries rose from 3 to 6 year-over-year. The proportion of crashes resulting in no injury decreased from 76.9% in April 2022 to 65.9% in April 2023, indicating a higher incidence of injury-involved crashes in the current period.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Most severe injury per crash record
Top Contributing Factors
Several contributing factors saw an increase in crash counts year-over-year. 'Inattention' crashes rose from 2 to 11, an increase of 9 crashes, while 'Failed to yield right of way' crashes increased from 1 to 7. 'No improper driving' crashes also saw an increase, from 5 to 8. The top contributing factor in April 2023 was 'Inattention' with 11 crashes, up from its lower rank in April 2022.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring under 'Clear' weather conditions increased from 18 to 32 year-over-year, while those in 'Cloudy' conditions rose from 3 to 9. Similarly, crashes during 'Daylight' hours increased from 20 to 35, and crashes on 'Dry' road surfaces increased from 22 to 40. There was no change in the count of crashes during 'Rain' or on 'Wet' road surfaces, both remaining at 2 and 4 crashes respectively.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes increased from 50 to 92 year-over-year. All reported age groups experienced an increase in involved persons, with the 35-44 age group seeing the largest numerical increase from 10 to 19 persons. Toyota became the most frequently involved make in April 2023 with 19 vehicles, surpassing Honda which had 9 vehicles in the prior period and 11 in the current.
Top Vehicle Makes (92 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Vehicle unit records
10 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (105 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Person-level records linked to crash events
Speed Limit Zones
Crashes occurring in 65 mph speed zones increased from 12 to 16 year-over-year, while those in 35 mph zones saw a substantial rise from 1 to 10 crashes. Crashes in 30 mph zones increased from 4 to 5, and crashes in 20 mph zones decreased from 2 to 1. No fatal crashes were recorded in any speed zone for either period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · 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: 2023-04-01 through 2023-04-30
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-04-01 through 2023-04-30 (30 days)
- Geographic scope: FOXBOROUGH, MA
- Total crash records analyzed: 44
- Total persons involved: 114
- Total vehicles involved: 92
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: April 2023." Published June 21, 2026. Reporting period: 2023-04-01 to 2023-04-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/foxborough/april-2023-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: 2023-04-01 – 2023-04-30
Generated: June 21, 2026 · All rights reserved