Monthly Traffic Safety Analysis

78 CRASHES IN
SHREWSBURY, MA
NOVEMBER 2025

All metrics benchmarked againstNovember 2024

In November 2025, SHREWSBURY experienced 78 total crashes, a 9.3% decrease from the 86 crashes recorded in November 2024. Despite this reduction in overall incidents, total injuries rose by 24%, from 25 to 31. Fatalities remained at zero for both periods, indicating no change in the most severe outcome.

78

-9.3%was 86

Total Crash Events

0

Persons Killed

31

24.0%was 25

Persons Injured

6

-25.0%was 8

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend shows a decrease in total crashes, with 78 crashes in the current period compared to 86 in the prior period, representing a 9.3% reduction. However, total injuries increased from 25 to 31, marking a 24% rise year-over-year. Fatalities remained stable at zero in both November 2025 and November 2024.

6

Hit-and-Run Crashes — November 2025

-25.0% vs prior (8)

Hit-and-run crashes decreased from 8 incidents in November 2024 to 6 incidents in November 2025. The hit-and-run rate also saw a decline, moving from 9.3% of total crashes in the prior period to 7.7% in the current period. This indicates a downward trend in hit-and-run incidents.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

1

Pedestrians Injured

Prior: 0%

30

Motorists Injured

Prior: 2520.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · 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 Friday with 21 incidents in the prior period to Monday with 19 incidents in the current period. Similarly, the peak hour for crashes moved from 3 PM with 10 incidents in the prior period to 4 PM with 11 incidents in the current period. This indicates a shift in the most crash-prone times of the week and day.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There were no fatalities in either November 2025 or November 2024. Total injuries increased from 25 in the prior period to 31 in the current period, a 24% rise. Serious injuries (Severity A) decreased from 4 to 2, while minor injuries (Severity B) increased from 6 to 9, and possible injuries (Severity C) remained at 9.

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes2.6%
-50.0%prior 4
Minor Injury9minor injury crashes11.5%
50.0%prior 6
Possible Injury9possible injury crashes11.5%
0.0%prior 9
No Injury54no injury crashes69.2%
-16.9%prior 65

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factor, 'Failed to yield right of way,' increased significantly from 10 crashes in the prior period to 23 crashes in the current period, a 130% increase in count. Conversely, 'No improper driving' decreased from 20 crashes to 12 crashes, a 40% reduction in count. 'Followed too closely' also saw a decrease, falling from 15 crashes to 7 crashes, a 53.3% decrease in count.

Officer-Reported Primary Contributing Cause

Failed to yield right of way23 (29.5%)130.0%prior 10
No improper driving12 (15.4%)-40.0%prior 20
Failure to keep in proper lane or running off road7 (9%)
Followed too closely7 (9%)-53.3%prior 15
Inattention7 (9%)16.7%prior 6
Made an improper turn3 (3.8%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner2 (2.6%)
Disregarded traffic signs, signals, road markings1 (1.3%)-80.0%prior 5
Operating defective equipment1 (1.3%)
Over-correcting/over-steering1 (1.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear' weather conditions decreased from 45 to 40, while 'Clear/Clear' conditions saw a slight decrease from 18 to 17 incidents. Incidents during 'Cloudy/Rain' conditions significantly decreased from 9 to 2 crashes. Crashes on wet road surfaces decreased from 16 in the prior period to 8 in the current period, and incidents on icy roads (1) were observed in the prior period but not the current.

Weather

Clear40 (51.9%)
-11.1%prior 45
Clear/Clear17 (22.1%)
-5.6%prior 18
Cloudy10 (13.0%)
Rain4 (5.2%)
Cloudy/Cloudy2 (2.6%)
Cloudy/Rain2 (2.6%)
-77.8%prior 9
Clear/Rain1 (1.3%)
Clear/Cloudy1 (1.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Weather condition at time of crash

Lighting

Daylight40 (51.3%)
-16.7%prior 48
Dark - lighted roadway28 (35.9%)
-3.4%prior 29
Dark - roadway not lighted4 (5.1%)
Dusk3 (3.8%)
Dawn2 (2.6%)
Dark - unknown roadway lighting1 (1.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Lighting condition field

Road Surface

Dry68 (89.5%)
3.0%prior 66
Wet8 (10.5%)
-50.0%prior 16

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes decreased from 169 to 153 year-over-year. The 26-34 age group saw a notable decrease in persons involved, from 51 to 23. Toyota remained the most involved vehicle make, though its count decreased from 38 to 31, while Subaru increased its representation from 10 to 12.

Top Vehicle Makes (153 vehicles)

1
TOYOTA31 (20.3%)
-18.4%prior 38
2
SUBARU12 (7.8%)
20.0%prior 10
3
FORD11 (7.2%)
-21.4%prior 14
4
NISSAN10 (6.5%)
25.0%prior 8
5
HYUNDAI9 (5.9%)
28.6%prior 7
6
CHEVROLET9 (5.9%)
-25.0%prior 12
7
HONDA9 (5.9%)
-35.7%prior 14
8
JEEP6 (3.9%)
20.0%prior 5
9
KIA6 (3.9%)
10
MAZDA5 (3.3%)
-16.7%prior 6

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Vehicle unit records

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

Sex Distribution (177 persons with recorded sex)

Male101 (57.1%)
-9.8%prior 112
Female76 (42.9%)
-1.3%prior 77

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · Person-level records linked to crash events

Speed Limit Zones

The highest number of crashes in the current period occurred in 40 mph zones with 16 incidents, an increase from 6 crashes in the prior period. Conversely, crashes in 45 mph zones decreased from 10 to 5. There were no fatal crashes reported across any speed limit zone in either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-11-01 to 2025-11-30 · 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: 2025-11-01 through 2025-11-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2025-11-01 through 2025-11-30 (30 days)
  • Geographic scope: SHREWSBURY, MA
  • Total crash records analyzed: 78
  • Total persons involved: 184
  • Total vehicles involved: 153

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). "SHREWSBURY, MA Crash Intelligence Report: November 2025." Published June 21, 2026. Reporting period: 2025-11-01 to 2025-11-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/shrewsbury/november-2025-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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Shrewsbury, MA Crash Report — November 2025 | ThatCarHitMe.com