Monthly Traffic Safety Analysis

21 CRASHES IN
MILLBURY, MA
NOVEMBER 2023

All metrics benchmarked againstNovember 2022

In November 2023, the city of MILLBURY experienced 21 crashes, a significant decrease of 44.7% compared to the 38 crashes recorded in November 2022. This period saw a notable shift in contributing factors, with speeding-related crashes decreasing from 4 to 1, while DUI-related crashes increased from 0 to 1.

21

▼ -44.7%was 38

Total Crash Events

0

Persons Killed

3

▼ -25.0%was 4

Persons Injured

0

▼ -100.0%was 1

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 · 2023-11-01 to 2023-11-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, the trend for crashes in MILLBURY shows a significant decrease year-over-year, with total crashes falling by 44.7% from 38 in November 2022 to 21 in November 2023. Total injuries also saw a decrease, dropping from 4 to 3, representing a 25% reduction.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 0→0.0%

3

Motorists Injured

Prior: 4↓-25.0%

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 temporal patterns of crashes shifted notably year-over-year. In November 2022, the peak day for crashes was Saturday with 9 incidents, and the peak hour was 5 p.m. with 9 crashes. In contrast, November 2023 saw Thursday as the peak day with 5 crashes, and 7 a.m. as the peak hour with 3 crashes, indicating a shift in both the busiest day and time for incidents.

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

Fatalities remained at zero in both November 2022 and November 2023, with no fatal crashes recorded in either period. Total injuries decreased from 4 in November 2022 to 3 in November 2023. However, the proportion of crashes resulting in any injury (Minor or Possible) increased from 10.5% (4 out of 38) in the prior period to 14.3% (3 out of 21) in the current period.

Outcome by Severity (Crash Events)

Minor Injury2minor injury crashes9.5%
↓-33.3%prior 3
Possible Injury1possible injury crashes4.8%
→0.0%prior 1
No Injury18no injury crashes85.7%
↓-45.5%prior 33

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

The top contributing factors showed significant shifts year-over-year. 'Failed to yield right of way' saw a substantial decrease in count, dropping from 8 crashes in November 2022 to 1 crash in November 2023, an 87.5% reduction. 'Followed too closely' also decreased from 6 crashes to 5 crashes, a 16.7% reduction in count. 'No improper driving' remained constant at 6 crashes in both periods, becoming the most frequent factor in November 2023.

Officer-Reported Primary Contributing Cause

No improper driving6 (28.6%)→0.0%prior 6
Followed too closely5 (23.8%)↓-16.7%prior 6
Other improper action3 (14.3%)
Failure to keep in proper lane or running off road2 (9.5%)↓-60.0%prior 5
Driving too fast for conditions1 (4.8%)
Failed to yield right of way1 (4.8%)↓-87.5%prior 8
Inattention1 (4.8%)
Physical impairment1 (4.8%)

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

Crashes occurring under adverse conditions decreased year-over-year. The proportion of crashes in rain or snow conditions dropped from 13.2% (5 out of 38) in November 2022 to 4.8% (1 out of 21) in November 2023. Similarly, crashes on wet road surfaces decreased from 18.4% (7 out of 38) to 4.8% (1 out of 21). Incidents during dark or low-light conditions also saw a significant reduction in proportion, from 52.6% (20 out of 38) to 28.6% (6 out of 21).

Weather

Clear15 (88.2%)
↓-50.0%prior 30
Cloudy1 (5.9%)
Rain1 (5.9%)

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

Lighting

Daylight14 (66.7%)
↓-22.2%prior 18
Dark - lighted roadway4 (19.0%)
↓-63.6%prior 11
Dark - roadway not lighted2 (9.5%)
↓-71.4%prior 7
Other1 (4.8%)

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

Road Surface

Dry20 (95.2%)
↓-35.5%prior 31
Wet1 (4.8%)
↓-85.7%prior 7

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

Vehicles & Demographics

Top Vehicle Makes (35 vehicles)

1
TOYOTA6 (17.1%)
↓-57.1%prior 14
2
CHEVROLET3 (8.6%)
↓-50.0%prior 6
3
SUBARU3 (8.6%)
4
FORD3 (8.6%)
↓-62.5%prior 8
5
HONDA3 (8.6%)
↓-57.1%prior 7
6
LEXUS2 (5.7%)
7
DODGE2 (5.7%)
8
NISSAN2 (5.7%)
9
PETERBILT2 (5.7%)
10
HYUNDAI2 (5.7%)

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

Sex Distribution (44 persons with recorded sex)

Male23 (52.3%)
↓-53.1%prior 49
Female21 (47.7%)
↓-30.0%prior 30

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

Crash distribution across speed zones shifted, with a general decrease in crash counts across most categories. Crashes in the 65 mph zone decreased from 14 in November 2022 to 8 in November 2023, while crashes in the 30 mph zone dropped from 9 to 3. There were no fatal crashes recorded in any speed zone during either period.

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: MILLBURY, MA
  • Total crash records analyzed: 21
  • Total persons involved: 44
  • Total vehicles involved: 35

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). "MILLBURY, 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/millbury/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

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