ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · FOXBOROUGH, MA · 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/2023-annual-report
Yearly Traffic Safety Analysis
541 CRASHES IN
FOXBOROUGH, MA
2023
In Foxborough, total traffic crashes increased by 6.5%, from 508 in 2022 to 541 in 2023. While the overall crash volume rose, the most notable year-over-year change was a significant decrease in traffic fatalities, which fell from 5 in the prior year to 1 in the current year. The proportion of crashes resulting in any injury remained stable at 24.6% for both periods.
541
▲ 6.5%was 508
Total Crash Events
1
▼ -80.0%was 5
Persons Killed
182
▼ -2.2%was 186
Persons Injured
31
▲ 55.0%was 20
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. 7 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend in traffic collisions shows a moderate increase year-over-year. Total crashes rose from 508 in 2022 to 541 in 2023, representing a 6.5% increase in volume. However, this increase in total crashes was accompanied by a substantial 80% reduction in fatalities, from 5 down to 1.
31
Hit-and-Run Crashes — 2023
▲ 55.0% vs prior (20)
Hit-and-run incidents showed a clear upward trend. The total count of hit-and-run crashes increased by 55%, from 20 in 2022 to 31 in 2023. Consequently, the hit-and-run rate as a percentage of all crashes rose from 3.9% in the prior year to 5.7% in the current year.
Vulnerable Road User Casualties
0
Pedestrians Killed
1
Motorists Killed
5
Pedestrians Injured
177
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes remained largely consistent year-over-year. The peak hour for collisions was the 4 p.m. hour in both 2022 and 2023, though the number of crashes during this hour increased from 58 to 72. The peak day for crashes shifted slightly, from Friday (95 crashes) in 2022 to Thursday (94 crashes) in 2023.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While total crashes increased, their severity profile improved. Fatal crashes decreased from 5 in 2022 to 1 in 2023, causing the fatal crash rate to drop from 1.0% to 0.2% of all collisions. The overall proportion of crashes involving any level of injury (Serious, Minor, or Possible) was unchanged at 24.6% for both years. However, crashes classified as 'Serious Injury' increased from 4 to 12, even as total persons injured remained nearly flat (186 vs. 182).
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record
Top Contributing Factors
The top contributing factors remained consistent, though their ranking shifted. In 2023, the top three factors were 'No improper driving' (94 incidents), 'Followed too closely' (91 incidents), and 'Inattention' (89 incidents). This is a change from 2022, when 'Inattention' was the leading factor with 87 incidents. The most significant change was in crashes attributed to 'Followed too closely,' which saw its count increase by 32% from 69 in 2022 to 91 in 2023.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The distribution of crashes across environmental conditions was stable between 2022 and 2023. In both periods, approximately 65% of crashes occurred in daylight, about 72% happened in clear weather, and roughly 80% took place on dry road surfaces. The total number of crashes on non-dry surfaces (wet, snow, ice, or slush) was identical year-over-year, with 104 incidents recorded in both 2022 and 2023.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field
Vehicles & Demographics
The vehicle makes most frequently involved in crashes were unchanged, with Toyota, Honda, and Ford ranking as the top three in both 2022 and 2023 with very similar involvement counts. The age demographics of persons involved in crashes also remained consistent. For example, individuals in the 16-20 age group accounted for 9.5% of persons in 2022 and 10.5% in 2023, showing minimal change in representation.
Top Vehicle Makes (1,046 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records
74 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (1,229 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes increased in the highest posted speed zones, rising from 164 to 183 in 65 mph zones. However, fatalities in this zone decreased from 3 in 2022 to 1 in 2023. Collisions in lower speed zones also saw an increase, with the combined count for 30 mph and 35 mph zones growing from 141 to 159. The single fatal crash in 2023 occurred in a 65 mph zone.
Fatal crashes by zone: 65 mph: 1 of 183 (0.546%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · 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: 2023-01-01 through 2023-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-01-01 through 2023-12-31 (365 days)
- Geographic scope: FOXBOROUGH, MA
- Total crash records analyzed: 541
- Total persons involved: 1,331
- Total vehicles involved: 1,046
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: 2023." Published June 21, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/foxborough/2023-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: 2023-01-01 – 2023-12-31
Generated: June 21, 2026 · All rights reserved