ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · MILTON, MA · JANUARY 2022
Purpose: Machine-readable JSON endpoint for AI agents, LLMs, researchers, and programmatic consumers. Returns all underlying crash data and AI-generated commentary without HTML.
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GET: https://thatcarhitme.com/api/crash-data/reports/data/massachusetts/milton/january-2022-report
Monthly Traffic Safety Analysis
58 CRASHES IN
MILTON, MA
JANUARY 2022
In January 2022, MILTON, MA experienced 58 total crashes, an increase of 20.83% compared to the 48 crashes recorded in January 2021. The most notable shift was a substantial 300% increase in total injuries, rising from 5 in the prior period to 20 in the current period. This indicates a significant worsening of crash outcomes year-over-year.
58
▲ 20.8%was 48
Total Crash Events
0
Persons Killed
20
▲ 300.0%was 5
Persons Injured
4
▲ 100.0%was 2
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.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend shows an increase in crash activity year-over-year, with total crashes rising by 20.83% from 48 to 58. This upward trend is further emphasized by a dramatic 300% increase in total injuries, climbing from 5 to 20 over the same period. Fatalities remained at zero for both January 2021 and January 2022.
4
Hit-and-Run Crashes — January 2022
▲ 100.0% vs prior (2)
Hit-and-run crashes increased by 100% year-over-year, rising from 2 incidents in the prior period to 4 in the current period. The rate of hit-and-run crashes also trended upward, increasing from 4.2% of total crashes in January 2021 to 6.9% in January 2022. This indicates a clear increase in both the count and proportion of hit-and-run incidents.
Vulnerable Road User Casualties
0
Motorists Killed
20
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · 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 Wednesday with 10 crashes in the prior period to Monday with 13 crashes in the current period. The peak hour remained consistent in the afternoon, shifting slightly from 4 PM with 6 crashes in the prior period to 3 PM with 6 crashes in the current period. Overall, the distribution of crashes across days of the week and hours of the day showed some changes in peak times.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While both periods recorded zero fatalities, there was a significant increase in injury crashes in the current period. Serious injury crashes rose from 0 to 1, while minor injury crashes increased from 2 to 10. Consequently, the proportion of crashes resulting in no injury decreased from 89.6% in the prior period to 69% in the current period, indicating a higher severity distribution.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Most severe injury per crash record
Top Contributing Factors
The number of crashes where 'No improper driving' was cited increased from 21 to 28, remaining the most frequent factor. Crashes involving 'Failed to yield right of way' saw a count increase from 1 to 4, while 'Inattention' crashes decreased from 6 to 2. Notably, 'Exceeded authorized speed limit' and 'Driving too fast for conditions' both appeared in the current period with 2 crashes each, after having 0 crashes in the prior period.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring on wet road surfaces saw a significant increase, rising from 5 in the prior period to 17 in the current period. Similarly, crashes on icy road surfaces increased from 0 to 5, and crashes during rainy weather increased from 0 to 4. This indicates a notable shift towards a higher proportion of crashes occurring under adverse weather and road conditions in the current period.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes increased from 90 in the prior period to 97 in the current period. Toyota remained the most frequently involved vehicle make, increasing from 15 to 20 vehicles. Honda moved up in ranking, with its involvement increasing from 9 to 11 vehicles, while Ford's involvement decreased from 11 to 8 vehicles.
Top Vehicle Makes (97 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Vehicle unit records
9 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (120 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in the 35 mph speed zone saw a substantial increase, rising from 5 in the prior period to 17 in the current period. Conversely, crashes in the 30 mph zone decreased from 10 to 7. The 55 mph zone also experienced an increase in crashes, going from 10 to 12, and a crash in an 85 mph zone was reported in the current period, which was not present in the prior period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · 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: 2022-01-01 through 2022-01-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-01-01 through 2022-01-31 (31 days)
- Geographic scope: MILTON, MA
- Total crash records analyzed: 58
- Total persons involved: 132
- Total vehicles involved: 97
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 2022." Published June 21, 2026. Reporting period: 2022-01-01 to 2022-01-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/milton/january-2022-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: 2022-01-01 – 2022-01-31
Generated: June 21, 2026 · All rights reserved