Monthly Traffic Safety Analysis

62 CRASHES IN
MILTON, MA
MARCH 2023

All metrics benchmarked againstMarch 2022

Total crashes in Milton increased by 21.6% from 51 in March 2022 to 62 in March 2023. Concurrently, total injuries rose from 27 to 33, representing a 22.2% increase. The most notable shift was the reduction in total fatalities from 1 in March 2022 to 0 in March 2023.

62

21.6%was 51

Total Crash Events

0

-100.0%was 1

Persons Killed

33

22.2%was 27

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash trends indicate an increase in both total crashes and total injuries year-over-year. Total crashes rose by 11 incidents, from 51 to 62, marking a 21.6% increase. Despite this rise, fatal crashes decreased from 1 to 0 in the current period.

4

Hit-and-Run Crashes — March 2023

100.0% vs prior (2)

The number of hit-and-run crashes doubled, increasing from 2 incidents in March 2022 to 4 incidents in March 2023. This resulted in an increase in the hit-and-run rate from 3.9% of all crashes to 6.5%.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Motorists Killed

Prior: 1-100.0%

1

Pedestrians Injured

Prior: 0%

32

Motorists Injured

Prior: 2718.5%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · 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 Tuesday with 12 crashes in March 2022 to Friday with 13 crashes in March 2023. The peak hour also changed, moving from 4 PM with 6 crashes in March 2022 to 3 PM with 8 crashes in March 2023, indicating a shift in daily crash patterns.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The fatal crash rate decreased significantly from 1.96% in March 2022 (1 fatal crash out of 51 total) to 0% in March 2023 (0 fatal crashes out of 62 total). While minor injuries remained stable at 10-11 incidents, possible injuries increased from 5 to 11, and serious injuries increased from 0 to 1.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes1.6%
Minor Injury10minor injury crashes16.1%
-9.1%prior 11
Possible Injury11possible injury crashes17.7%
120.0%prior 5
No Injury34no injury crashes54.8%
13.3%prior 30

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Most severe injury per crash record

Top Contributing Factors

Contributing factors saw notable changes: 'Inattention' increased from 5 incidents to 10, and 'Followed too closely' rose from 7 to 9 incidents. 'Failed to yield right of way' also increased from 1 incident to 6. Conversely, 'Operating vehicle in erratic, reckless, careless, negligent or aggressive manner' decreased from 3 incidents to 1.

Officer-Reported Primary Contributing Cause

No improper driving12 (19.4%)9.1%prior 11
Inattention10 (16.1%)100.0%prior 5
Followed too closely9 (14.5%)28.6%prior 7
Failed to yield right of way6 (9.7%)
Disregarded traffic signs, signals, road markings4 (6.5%)
Driving too fast for conditions3 (4.8%)
Other improper action2 (3.2%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway2 (3.2%)
Made an improper turn1 (1.6%)
Over-correcting/over-steering1 (1.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring under adverse weather conditions, including rain, sleet, or snow, increased from 4 incidents in March 2022 to 12 incidents in March 2023. Similarly, crashes on adverse road surfaces (wet, snow, ice, slush) increased from 10 incidents to 14. Daylight conditions remained the dominant lighting factor for crashes in both periods.

Weather

Clear23 (37.1%)
0.0%prior 23
Clear/Clear22 (35.5%)
57.1%prior 14
Rain6 (9.7%)
Cloudy3 (4.8%)
Sleet, hail (freezing rain or drizzle)2 (3.2%)
Rain/Snow1 (1.6%)
Sleet, hail (freezing rain or drizzle)/Rain1 (1.6%)
Snow1 (1.6%)
Cloudy/Clear1 (1.6%)
Snow/Sleet, hail (freezing rain or drizzle)1 (1.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Weather condition at time of crash

Lighting

Daylight45 (72.6%)
21.6%prior 37
Dark - lighted roadway15 (24.2%)
50.0%prior 10
Dark - roadway not lighted2 (3.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Lighting condition field

Road Surface

Dry48 (77.4%)
20.0%prior 40
Wet10 (16.1%)
0.0%prior 10
Snow2 (3.2%)
Ice1 (1.6%)
Slush1 (1.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes increased from 105 in March 2022 to 109 in March 2023. Toyota and Honda remained the top two vehicle makes involved, with Toyota increasing from 19 to 23 vehicles and Honda from 13 to 19 vehicles. Ford also saw an increase from 8 to 12 vehicles involved.

Top Vehicle Makes (109 vehicles)

1
TOYOTA23 (21.1%)
21.1%prior 19
2
HONDA19 (17.4%)
46.2%prior 13
3
FORD12 (11%)
50.0%prior 8
4
NISSAN7 (6.4%)
-22.2%prior 9
5
ACURA6 (5.5%)
6
AUDI5 (4.6%)
7
JEEP4 (3.7%)
-20.0%prior 5
8
MAZDA4 (3.7%)
9
SUBARU4 (3.7%)
-20.0%prior 5
10
MERCEDES-BENZ3 (2.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Vehicle unit records

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

Sex Distribution (133 persons with recorded sex)

Male86 (64.7%)
16.2%prior 74
Female47 (35.3%)
-14.5%prior 55

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in the 35 mph speed limit zone increased from 4 in March 2022 to 9 in March 2023. Crashes in the 55 mph zone also rose from 13 to 16. The 25 mph and 45 mph zones maintained a consistent number of crashes year-over-year.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · 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: 2023-03-01 through 2023-03-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-03-01 through 2023-03-31 (31 days)
  • Geographic scope: MILTON, MA
  • Total crash records analyzed: 62
  • Total persons involved: 151
  • Total vehicles involved: 109

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: March 2023." Published June 21, 2026. Reporting period: 2023-03-01 to 2023-03-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/milton/march-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

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