Monthly Traffic Safety Analysis

18 CRASHES IN
SALISBURY, MA
MAY 2025

All metrics benchmarked againstMay 2024

In May 2025, SALISBURY experienced 18 total crashes, a decrease of 5.26% compared to the 19 crashes recorded in May 2024. The most significant year-over-year shift was the complete absence of injuries in May 2025, down from 10 injuries reported in the prior year.

18

-5.3%was 19

Total Crash Events

0

Persons Killed

0

-100.0%was 10

Persons Injured

3

50.0%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. 18 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, total crashes in SALISBURY decreased by 5.26%, from 19 crashes in May 2024 to 18 crashes in May 2025. This period also saw a notable reduction in total injuries, falling from 10 in May 2024 to 0 in May 2025.

3

Hit-and-Run Crashes — May 2025

50.0% vs prior (2)

Hit-and-run crashes increased from 2 in May 2024 to 3 in May 2025. This change resulted in an increase in the hit-and-run rate from 10.5% of total crashes in May 2024 to 16.7% in May 2025.

When Crashes Happen

The peak day for crashes shifted from Sunday, which had 6 crashes in May 2024, to Friday and Saturday, both recording 4 crashes in May 2025. The peak hour for crashes also changed, moving from 5 PM with 6 crashes in May 2024 to 1 PM with 3 crashes in May 2025.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Crash time field aggregated by hour (0-23)

Top Contributing Factors

Crashes attributed to 'No improper driving' remained consistent with 8 incidents in both May 2024 and May 2025. 'Failed to yield right of way' crashes decreased from 4 in May 2024 to 3 in May 2025, and 'Inattention' crashes saw a reduction from 4 to 1 over the same period. The factor 'Distracted' appeared in May 2025 with 1 crash, not being present in the prior period's data.

Officer-Reported Primary Contributing Cause

No improper driving8 (44.4%)0.0%prior 8
Failed to yield right of way3 (16.7%)
Inattention1 (5.6%)
Failure to keep in proper lane or running off road1 (5.6%)
Distracted1 (5.6%)
Disregarded traffic signs, signals, road markings1 (5.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Weather conditions in May 2025 were more varied, with 8 crashes in 'Clear' conditions compared to 18 'Clear' crashes in May 2024, and 3 crashes occurring in 'Rain' conditions in May 2025. Crashes during 'Daylight' hours decreased from 17 in May 2024 to 13 in May 2025. Data for road surface conditions in May 2024 was not available for comparison.

Weather

Clear8 (50.0%)
-55.6%prior 18
Rain3 (18.8%)
Clear/Clear2 (12.5%)
Cloudy/Rain1 (6.3%)
Clear/Cloudy1 (6.3%)
Clear/Other1 (6.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Weather condition at time of crash

Lighting

Daylight13 (81.3%)
-23.5%prior 17
Dark - lighted roadway2 (12.5%)
Dark - roadway not lighted1 (6.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Lighting condition field

Road Surface

Dry12 (70.6%)
Wet5 (29.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (35 vehicles)

1
HONDA6 (17.1%)
2
TOYOTA5 (14.3%)
3
JEEP2 (5.7%)
4
GMC2 (5.7%)
5
LEXUS2 (5.7%)
6
NISSAN2 (5.7%)
7
HYUNDAI1 (2.9%)
8
INFI1 (2.9%)
9
BMW1 (2.9%)
10
KIA1 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Vehicle unit records

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

Sex Distribution (38 persons with recorded sex)

Female20 (52.6%)
5.3%prior 19
Male18 (47.4%)
-45.5%prior 33

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes occurring in 30 mph zones decreased from 7 in May 2024 to 4 in May 2025, a reduction of 3 crashes. Conversely, crashes in 40 mph zones increased by 1, from 6 in May 2024 to 7 in May 2025. No fatalities were reported in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · 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: 2025-05-01 through 2025-05-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2025-05-01 through 2025-05-31 (31 days)
  • Geographic scope: SALISBURY, MA
  • Total crash records analyzed: 18
  • Total persons involved: 45
  • 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). "SALISBURY, MA Crash Intelligence Report: May 2025." Published June 21, 2026. Reporting period: 2025-05-01 to 2025-05-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/salisbury/may-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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Salisbury, MA Crash Report — May 2025 | ThatCarHitMe.com