ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · MILFORD, 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/milford/2024-annual-report
Yearly Traffic Safety Analysis
1,047 CRASHES IN
MILFORD, MA
2024
In 2024, Milford recorded 1,047 traffic crashes, an 8.0% increase from the 970 crashes reported in 2023. The most significant year-over-year change was in crash outcomes, as the number of fatalities rose from one to three, while the total number of injuries decreased from 212 to 200.
1,047
▲ 7.9%was 970
Total Crash Events
3
▲ 200.0%was 1
Persons Killed
200
▼ -5.7%was 212
Persons Injured
87
▲ 10.1%was 79
Hit-and-Run Crashes
Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 48 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, traffic crashes in Milford increased by 8.0% from 970 in 2023 to 1,047 in 2024. Despite the rise in total collisions, the number of people injured decreased by 5.7% from 212 to 200. Conversely, the number of fatalities increased from one to three year-over-year.
87
Hit-and-Run Crashes — 2024
▲ 10.1% vs prior (79)
The number of hit-and-run incidents increased from 79 in 2023 to 87 in 2024. The hit-and-run rate, which measures the percentage of total crashes that are hit-and-runs, also saw a slight increase from 8.1% to 8.3%. This indicates a small upward trend in both the absolute count and the proportion of hit-and-run crashes.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
3
Motorists Killed
12
Pedestrians Injured
9
Cyclists Injured
179
Motorists 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 showed some shifts between the two periods. The peak day for crashes moved from Wednesday (151 crashes) in 2023 to Monday (169 crashes) in 2024. While the 5 p.m. hour remained the peak time for collisions in both years, the number of crashes during that hour decreased from 96 to 83.
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 number of fatal crashes doubled from one in 2023 to two in 2024, increasing the fatal crash share from 0.1% to 0.2% of all incidents. The proportion of crashes resulting in serious injuries remained stable at approximately 1.2%. The share of crashes with no injuries increased from 77.7% in the prior year to 80.3% in the current year.
Severity is per crash event (most severe injury). 2 fatal crash events resulted in 3 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 contributing factors remained the same year-over-year: Inattention, No improper driving, and Failed to yield right of way. The count of crashes attributed to 'Inattention' grew by 39.3%, from 211 incidents in 2023 to 294 in 2024. In contrast, the counts for 'No improper driving' (from 175 to 176) and 'Failed to yield right of way' (from 142 to 147) remained relatively stable.
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
In both years, the majority of crashes occurred during daylight hours on dry roads under clear skies. The count of crashes on dark but lighted roadways increased from 188 to 225. Crashes on wet roads decreased in both count (from 149 to 127) and as a share of total crashes (from 15.4% to 12.1%).
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 top two vehicle makes involved in crashes remained consistent, with Toyota and Ford leading in both years; Chevrolet (204 vehicles) moved into the third position in 2024, displacing Honda. Regarding persons involved, the 26-34 age group remained the most represented, with its count increasing from 324 to 399. The 21-25 age group also saw a notable increase in involvement, rising from 222 to 264 individuals.
Top Vehicle Makes (1,992 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records
326 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (2,010 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
The 30 mph speed zone accounted for the highest number of crashes in both periods, with an identical count of 580 incidents each year. In 2024, fatal crashes occurred in the 30 mph and 65 mph zones, each recording one fatal crash. This contrasts with 2023, where the sole fatal crash was recorded in a 25 mph zone.
Fatal crashes by zone: 30 mph: 1 of 580 (0.172%) · 65 mph: 1 of 77 (1.299%)
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: MILFORD, MA
- Total crash records analyzed: 1,047
- Total persons involved: 2,356
- Total vehicles involved: 1,992
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: 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/milford/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