ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · BOXBOROUGH, MA · 2025
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/boxborough/2025-annual-report
Yearly Traffic Safety Analysis
73 CRASHES IN
BOXBOROUGH, MA
2025
In 2025, Boxborough recorded 73 total vehicle crashes, a 9.9% decrease from the 81 crashes reported in 2024. While overall crashes declined, the number of hit-and-run incidents doubled from 3 to 6. Notably, the total number of injuries fell by 51.9%, from 27 in the prior year to 13 in the current year, and crashes resulting in serious injuries dropped from 3 to 0.
73
▼ -9.9%was 81
Total Crash Events
0
Persons Killed
13
▼ -51.9%was 27
Persons Injured
6
▲ 100.0%was 3
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. 2 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend in traffic crashes in Boxborough is downward year-over-year. Total crashes decreased by 9.9%, from 81 in 2024 to 73 in 2025. This decline was accompanied by a significant reduction in crash severity, as total injuries dropped from 27 to 13, and no fatal crashes were recorded in either period.
6
Hit-and-Run Crashes — 2025
▲ 100.0% vs prior (3)
Hit-and-run incidents increased significantly year-over-year. The number of hit-and-run crashes doubled from 3 in 2024 to 6 in 2025. Correspondingly, the hit-and-run rate, as a percentage of all crashes, more than doubled from 3.7% to 8.2%, indicating an upward trend for this crash type.
Vulnerable Road User Casualties
0
Motorists Killed
13
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The timing of crashes shifted between the two periods. In 2024, the peak day for crashes was Tuesday with 15 incidents, and the peak hour was 4 p.m. with 14 crashes. In 2025, the peak day shifted to Friday (13 crashes), and the peak hour moved later into the evening, with 5 p.m., 8 p.m., and 10 p.m. each recording 7 crashes.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Crash severity decreased notably from the prior year. While both periods recorded zero fatal crashes, the number of crashes involving any injury declined from 16 in 2024 to 12 in 2025. Critically, there were no crashes with serious injuries in 2025, compared to 3 such incidents in 2024. Consequently, the share of crashes with no reported injuries increased from 76.5% in 2024 to 80.8% in 2025.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factors showed some changes year-over-year. While 'No improper driving' remained the most common circumstance in both periods, its count decreased from 31 to 25. The count for crashes involving 'Followed too closely' remained stable, increasing from 8 to 9. Notably, crashes attributed to 'Driving too fast for conditions' increased from a lower count in the prior year to 8 in the current year, and 'Failure to keep in proper lane' incidents increased from 2 to 7.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes in 2025 occurred more frequently in adverse conditions compared to 2024. The proportion of crashes in daylight decreased from 64.2% (52 crashes) to 49.3% (36 crashes), while incidents in 'Dark - roadway not lighted' conditions increased from 17 to 24. Similarly, crashes on dry roads fell from 85.2% to 79.5% of the total, while crashes on snow-covered roads increased from 1 to 8.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field
Vehicles & Demographics
The makes of vehicles involved in crashes remained consistent, with Toyota and Honda being the top two in both 2025 (20 and 17 vehicles, respectively) and 2024 (19 and 18 vehicles, respectively). The age distribution of persons involved in crashes also showed little change. In both years, individuals in the 45-54 age group were among the most frequently involved, accounting for 23 persons in 2025 and 24 persons in 2024.
Top Vehicle Makes (120 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records
16 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (127 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in both years were most prevalent in 65 mph zones, with the count in this zone increasing from 27 in 2024 to 31 in 2025. Conversely, crashes in 40 mph zones decreased from 14 to 10, and incidents in 25 mph zones fell from 9 to 5. No fatalities were recorded in any speed zone during either period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · 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: 2025-01-01 through 2025-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2025-01-01 through 2025-12-31 (365 days)
- Geographic scope: BOXBOROUGH, MA
- Total crash records analyzed: 73
- Total persons involved: 143
- Total vehicles involved: 120
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). "BOXBOROUGH, MA Crash Intelligence Report: 2025." Published June 21, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/boxborough/2025-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: 2025-01-01 – 2025-12-31
Generated: June 21, 2026 · All rights reserved