ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · MILTON, MA · JANUARY 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/january-2023-report
Monthly Traffic Safety Analysis
62 CRASHES IN
MILTON, MA
JANUARY 2023
In January 2023, Milton experienced 62 crashes, an increase from the 58 crashes reported in January 2022, representing a 6.9% rise year-over-year. This period also saw a notable increase in total injuries, which rose by 45% from 20 to 29.
62
▲ 6.9%was 58
Total Crash Events
0
Persons Killed
29
▲ 45.0%was 20
Persons Injured
1
▼ -75.0%was 4
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. 1 crash with unreported severity is not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall crash incidents in Milton showed an upward trend year-over-year, increasing by 6.9% from 58 crashes in January 2022 to 62 crashes in January 2023. Concurrently, total injuries saw a substantial rise of 45%, from 20 in the prior period to 29 in the current period.
1
Hit-and-Run Crashes — January 2023
▼ -75.0% vs prior (4)
Hit-and-run incidents significantly decreased year-over-year, falling by 75% from 4 crashes in January 2022 to 1 crash in January 2023. This resulted in the hit-and-run crash rate dropping from 6.9% in the prior period to 1.6% in the current period, indicating a downward trend.
Vulnerable Road User Casualties
0
Motorists Killed
29
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal distribution of crashes shifted year-over-year, with Wednesday becoming the peak day in January 2023 with 14 crashes, up from 7 crashes on Wednesday in January 2022. The peak crash hour remained 3p in both periods, experiencing an increase from 6 crashes in January 2022 to 8 crashes in January 2023. Conversely, crashes on Monday decreased from 13 to 11, and on Sunday from 11 to 7.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
There were no fatal crashes reported in either January 2022 or January 2023. However, total injuries increased by 45%, rising from 20 in the prior period to 29 in the current period. While minor injury crashes remained at 10 for both periods, serious injury crashes decreased from 1 in January 2022 to 0 in January 2023, and possible injury crashes increased from 7 to 8.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Most severe injury per crash record
Top Contributing Factors
The distribution of contributing factors saw significant shifts year-over-year. The count of crashes attributed to 'No improper driving' decreased by 35.7%, from 28 in January 2022 to 18 in January 2023. Conversely, 'Inattention' crashes increased by 250%, rising from 2 to 7, and 'Followed too closely' incidents doubled from 3 to 6. 'Failed to yield right of way' also saw a 50% increase in count, from 4 to 6.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in clear weather conditions remained relatively stable year-over-year, with 33 in January 2023 compared to 30 in January 2022. There was a notable increase in crashes on wet road surfaces, rising from 17 in January 2022 to 26 in January 2023. Conversely, crashes on icy roads significantly decreased from 5 to 1, and those in dark, unlighted conditions decreased from 7 to 2.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes increased by 31.9%, from 97 in January 2022 to 128 in January 2023. Toyota remained the most frequently involved vehicle make, with its count increasing from 20 to 27. Ford and Chevrolet also saw increases in their involvement, rising from 8 to 12 and 7 to 12 respectively.
Top Vehicle Makes (128 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Vehicle unit records
10 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (146 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes at the 30 mph speed limit zone saw a substantial increase, rising from 7 in January 2022 to 21 in January 2023. Conversely, crashes in the 35 mph zone decreased from 17 to 7 year-over-year. Crashes in the 55 mph zone also increased from 12 to 15, while there were no fatal crashes reported in any speed zone during either period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · 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 2023-01-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-01-01 through 2023-01-31 (31 days)
- Geographic scope: MILTON, MA
- Total crash records analyzed: 62
- Total persons involved: 167
- Total vehicles involved: 128
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: January 2023." Published June 21, 2026. Reporting period: 2023-01-01 to 2023-01-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/milton/january-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: 2023-01-01 – 2023-01-31
Generated: June 21, 2026 · All rights reserved