Monthly Traffic Safety Analysis

59 CRASHES IN
MILTON, MA
NOVEMBER 2023

All metrics benchmarked againstNovember 2022

In November 2023, Milton experienced 59 total crashes, a decrease of 3.3% from the 61 crashes reported in November 2022. The most significant year-over-year shift was the elimination of serious injuries, dropping from 2 in the prior period to 0 in the current period.

59

▼ -3.3%was 61

Total Crash Events

0

Persons Killed

22

▼ -24.1%was 29

Persons Injured

6

▼ -14.3%was 7

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. 2 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates a slight decrease in total crashes, from 61 in November 2022 to 59 in November 2023, representing a 3.3% reduction. Total injuries saw a more substantial decline, decreasing by 24.1% from 29 to 22 over the same period.

6

Hit-and-Run Crashes — November 2023

▼ -14.3% vs prior (7)

Hit-and-run crashes decreased from 7 in November 2022 to 6 in November 2023. The hit-and-run rate also saw a slight decrease, moving from 11.5% to 10.2% of total crashes.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 0→0.0%

22

Motorists Injured

Prior: 28↓-21.4%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · 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 Saturday with 12 crashes in November 2022 to Monday with 13 crashes in November 2023. The peak hour also changed, moving from 11 AM with 9 crashes in the prior period to 4 PM with 6 crashes in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Total injuries decreased from 29 in November 2022 to 22 in November 2023, a 24.1% reduction. Notably, serious injuries (Severity A) were eliminated, dropping from 2 in the prior period to 0 in the current period, while minor injuries (Severity B) decreased by 53.3%, from 15 to 7.

Outcome by Severity (Crash Events)

Minor Injury7minor injury crashes11.9%
↓-53.3%prior 15
Possible Injury8possible injury crashes13.6%
↑14.3%prior 7
No Injury42no injury crashes71.2%
↑13.5%prior 37

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Most severe injury per crash record

Top Contributing Factors

Crashes where 'No improper driving' was cited decreased by 4 incidents, from 17 in November 2022 to 13 in November 2023. Conversely, crashes attributed to 'Followed too closely' increased by 3 incidents, rising from 8 to 11, with its share of contributing factors increasing from 13.1% to 18.6%. Crashes due to 'Inattention' decreased by 5 incidents, from 8 to 3.

Officer-Reported Primary Contributing Cause

No improper driving13 (22%)↓-23.5%prior 17
Followed too closely11 (18.6%)↑37.5%prior 8
Failed to yield right of way9 (15.3%)↑80.0%prior 5
Failure to keep in proper lane or running off road5 (8.5%)
Disregarded traffic signs, signals, road markings4 (6.8%)
Inattention3 (5.1%)↓-62.5%prior 8
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner3 (5.1%)
Exceeded authorized speed limit2 (3.4%)
Fatigued/asleep2 (3.4%)
Over-correcting/over-steering2 (3.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The number of crashes occurring in 'Daylight' conditions decreased from 35 in November 2022 to 27 in November 2023. Crashes on 'Wet' road surfaces decreased by 5 incidents, from 9 to 4, while crashes in 'Clear' weather conditions increased from 32 to 36.

Weather

Clear36 (61.0%)
↑12.5%prior 32
Clear/Clear14 (23.7%)
↓-30.0%prior 20
Cloudy4 (6.8%)
Rain3 (5.1%)
Clear/Cloudy1 (1.7%)
Cloudy/Cloudy1 (1.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Weather condition at time of crash

Lighting

Daylight27 (45.8%)
↓-22.9%prior 35
Dark - lighted roadway23 (39.0%)
↑21.1%prior 19
Dark - roadway not lighted4 (6.8%)
Dusk4 (6.8%)
↓-33.3%prior 6
Dawn1 (1.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Lighting condition field

Road Surface

Dry54 (93.1%)
↑3.8%prior 52
Wet4 (6.9%)
↓-55.6%prior 9

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes decreased from 122 in November 2022 to 118 in November 2023. While Toyota, Ford, and Honda remained among the top makes, Toyota saw a slight decrease from 19 to 18 vehicles, Ford from 19 to 15, and Honda from 19 to 17.

Top Vehicle Makes (118 vehicles)

1
TOYOTA18 (15.3%)
↓-5.3%prior 19
2
HONDA17 (14.4%)
↓-10.5%prior 19
3
FORD15 (12.7%)
↓-21.1%prior 19
4
LEXUS6 (5.1%)
5
BMW6 (5.1%)
6
MITS6 (5.1%)
7
JEEP5 (4.2%)
↓-16.7%prior 6
8
NISSAN5 (4.2%)
9
MERCEDES-BENZ4 (3.4%)
10
CHEVROLET4 (3.4%)
↓-42.9%prior 7

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Vehicle unit records

6 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (129 persons with recorded sex)

Male86 (66.7%)
↑6.2%prior 81
Female43 (33.3%)
↓-27.1%prior 59

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes occurring in 55 mph speed limit zones decreased from 18 in November 2022 to 14 in November 2023. In contrast, crashes in 45 mph speed limit zones increased from 1 to 5, and crashes in 30 mph zones decreased from 12 to 7.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · 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: 2023-11-01 through 2023-11-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-11-01 through 2023-11-30 (30 days)
  • Geographic scope: MILTON, MA
  • Total crash records analyzed: 59
  • Total persons involved: 140
  • Total vehicles involved: 118

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: November 2023." Published June 21, 2026. Reporting period: 2023-11-01 to 2023-11-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/milton/november-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

Milton, MA Crash Report — November 2023 | ThatCarHitMe.com