ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · MILTON, 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/milton/april-2023-report
Monthly Traffic Safety Analysis
65 CRASHES IN
MILTON, MA
APRIL 2023
In April 2023, Milton experienced 65 total crashes, an increase from the 55 crashes recorded in April 2022, representing an 18.2% rise. Concurrently, total injuries increased significantly by 72.7%, from 22 in the prior period to 38 in the current period. This marks a notable year-over-year increase in both crash frequency and injury severity.
65
▲ 18.2%was 55
Total Crash Events
0
Persons Killed
38
▲ 72.7%was 22
Persons Injured
3
▼ -50.0%was 6
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. 3 crashes with unreported severity are not shown in the severity breakdown.
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 an increase in crash activity year-over-year in Milton. Total crashes rose from 55 in April 2022 to 65 in April 2023, an increase of 10 crashes. Total injuries also saw a substantial rise, from 22 to 38, marking a 72.7% increase.
3
Hit-and-Run Crashes — April 2023
▼ -50.0% vs prior (6)
Hit-and-run crashes decreased from 6 in April 2022 to 3 in April 2023, representing a 50% reduction in count. The hit-and-run rate also trended downward, falling from 10.9% of total crashes in the prior period to 4.6% in the current period.
Vulnerable Road User Casualties
0
Cyclists Killed
0
Motorists Killed
1
Cyclists Injured
37
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 13 crashes in April 2022 to Sunday with 15 crashes in April 2023. Similarly, the peak hour for crashes changed from 2 PM with 8 crashes in the prior period to 9 AM with 8 crashes in the current period, indicating a shift in when crashes are most frequent.
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
There were no fatal crashes in either April 2022 or April 2023. However, injury crash proportions changed, with serious injuries (Severity A) increasing from 1 crash (1.8% of total) to 2 crashes (3.1% of total). Minor injuries (Severity B) also rose from 9 crashes (16.4%) to 16 crashes (24.6%), and possible injuries (Severity C) increased from 6 crashes (10.9%) to 8 crashes (12.3%).
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
The top contributing factors saw shifts in their rankings and counts. 'Followed too closely' decreased in count from 16 to 11 crashes, while 'No improper driving' decreased from 13 to 12 crashes. 'Inattention' increased from 4 crashes to 6 crashes, and 'Driving too fast for conditions' saw a notable increase from 1 crash to 4 crashes.
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 in clear weather conditions, including 'Clear' and 'Clear/Clear', decreased in share from 83.6% in April 2022 to 67.7% in April 2023. Conversely, rain-related crashes, including 'Rain', 'Cloudy/Rain', and 'Rain/Fog, smog, smoke', increased from 1 crash (1.8% share) to 9 crashes (13.8% share). Crashes on wet road surfaces also rose significantly from 3 crashes (5.5% share) to 11 crashes (16.9% share).
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
Among the top vehicle makes involved, HONDA saw a significant increase in representation, rising from 12 vehicles in April 2022 to 22 vehicles in April 2023. TOYOTA also increased from 21 to 22 vehicles, while FORD decreased from 10 to 9 vehicles. In terms of persons involved, the 35-44 age group experienced a notable increase from 14 persons to 32 persons, and the 65+ age group also saw a rise from 6 to 15 persons.
Top Vehicle Makes (126 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Vehicle unit records
15 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (152 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 in 55 mph speed zones decreased from 26 in April 2022 to 18 in April 2023. Conversely, crashes in 35 mph speed zones saw a substantial increase, rising from 2 crashes to 12 crashes year-over-year. No fatal crashes were reported in any speed zone during 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: MILTON, MA
- Total crash records analyzed: 65
- Total persons involved: 176
- Total vehicles involved: 126
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). "MILTON, 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/milton/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