ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · MILFORD, MA · AUGUST 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/milford/august-2025-report
Monthly Traffic Safety Analysis
100 CRASHES IN
MILFORD, MA
AUGUST 2025
Total crashes in Milford increased by 6.38%, rising from 94 in August 2024 to 100 in August 2025. This period saw a notable decrease in crash severity, with total fatalities dropping from 1 to 0 and total injuries decreasing significantly from 27 to 5. The most notable year-over-year shift was the substantial reduction in injuries and the absence of fatalities.
100
▲ 6.4%was 94
Total Crash Events
0
▼ -100.0%was 1
Persons Killed
5
▼ -81.5%was 27
Persons Injured
12
▲ 140.0%was 5
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. 5 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend shows a slight increase in total crashes year-over-year, rising by 6.38% from 94 to 100. However, this increase in crash count was accompanied by a significant positive shift in safety outcomes, as total fatalities decreased from 1 to 0, and total injuries fell by 81.48%, from 27 to 5.
12
Hit-and-Run Crashes — August 2025
▲ 140.0% vs prior (5)
Hit-and-run crashes increased significantly year-over-year, rising by 140% from 5 incidents in August 2024 to 12 in August 2025. Consequently, the hit-and-run rate also increased, from 5.3% of all crashes in the prior period to 12% in the current period.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Motorists Killed
1
Pedestrians Injured
4
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes shifted year-over-year. The peak day for crashes moved from Saturday (19 crashes) in August 2024 to Friday (23 crashes) in August 2025, representing a 64.29% increase in Friday crashes. The peak hour also shifted, from 12 p.m. (13 crashes) in the prior period to 4 p.m. (11 crashes) in the current period, which is a 120% increase for the 4 p.m. hour.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity of crashes significantly decreased year-over-year. Fatal crashes dropped from 1 in August 2024 to 0 in August 2025, eliminating the prior period's 1.06% fatal crash rate. Serious injuries (A) decreased from 5 to 1, minor injuries (B) from 13 to 3, and possible injuries (C) from 2 to 1, leading to a substantial reduction in overall injury crash proportions.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · Most severe injury per crash record
Top Contributing Factors
The top contributing factors saw notable shifts in count. Crashes attributed to 'Followed too closely' increased by 116.67%, rising from 6 to 13, and 'Failed to yield right of way' increased by 85.71%, from 7 to 13. While 'Inattention' remained the most frequent factor, its count slightly decreased from 35 to 34, a 2.86% reduction.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in clear weather conditions increased from 73 to 91, while those in cloudy weather decreased from 7 to 3. Similarly, crashes on dry road surfaces increased from 86 to 97, whereas crashes on wet roads decreased from 8 to 3. Crashes during daylight hours also increased from 75 to 81, indicating a general shift towards crashes occurring under more favorable environmental conditions.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes increased by 10.67%, from 178 to 197. Among vehicle makes, TOYOTA saw a significant increase in involvement, rising from 23 to 36 (+56.52%) and becoming the most frequently involved make, while FORD's involvement decreased from 29 to 24 (-17.24%). The age group '0-15' saw a 200% increase in persons involved, from 4 to 12, while the '65+' age group decreased by 20%, from 25 to 20.
Top Vehicle Makes (197 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · Vehicle unit records
36 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (191 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 25 mph zones increased by 20%, from 15 to 18, and crashes in 30 mph zones increased by 14%, from 50 to 57. Conversely, crashes in 65 mph zones decreased by 33.33%, from 6 to 4. There were no fatal crashes in any speed zone during August 2025, compared to one fatal crash in a 30 mph zone in August 2024.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · 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: 2025-08-01 through 2025-08-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2025-08-01 through 2025-08-31 (31 days)
- Geographic scope: MILFORD, MA
- Total crash records analyzed: 100
- Total persons involved: 229
- Total vehicles involved: 197
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). "MILFORD, MA Crash Intelligence Report: August 2025." Published June 21, 2026. Reporting period: 2025-08-01 to 2025-08-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/milford/august-2025-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: 2025-08-01 – 2025-08-31
Generated: June 21, 2026 · All rights reserved