Monthly Traffic Safety Analysis

31 CRASHES IN
SALISBURY, MA
JUNE 2024

All metrics benchmarked againstJune 2023

In June 2024, Salisbury experienced 31 total crashes, a 3.3% increase compared to the 30 crashes recorded in June 2023. A notable shift includes a 100% increase in crashes attributed to operating a vehicle in an erratic, reckless, careless, negligent, or aggressive manner, rising from 2 incidents to 4.

31

3.3%was 30

Total Crash Events

0

Persons Killed

13

-18.8%was 16

Persons Injured

0

-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.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-06-01 to 2024-06-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash incidents in Salisbury remained relatively stable, with a slight increase of 3.3% from 30 crashes in June 2023 to 31 crashes in June 2024. Despite this, the total number of injuries decreased by 18.8%, from 16 injuries in June 2023 to 13 injuries in June 2024.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

1

Cyclists Injured

Prior: 0%

12

Motorists Injured

Prior: 15-20.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-06-01 to 2024-06-30 · 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 in June 2023, which saw 8 incidents, to Monday in June 2024, with 6 incidents. The peak hour for crashes remained consistent at 5 PM in both periods, though the count at this hour increased from 4 crashes in June 2023 to 5 crashes in June 2024.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-06-01 to 2024-06-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-06-01 to 2024-06-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There were no fatal crashes in either June 2023 or June 2024. However, the distribution of injury severities changed, with serious injury crashes appearing in June 2024 (1 crash, 3.2%) where none were reported in June 2023. Minor injury crashes decreased from 8 incidents (26.7% share) in June 2023 to 3 incidents (9.7% share) in June 2024, while possible injury crashes increased from 2 incidents (6.7% share) to 4 incidents (12.9% share).

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes3.2%
Minor Injury3minor injury crashes9.7%
-62.5%prior 8
Possible Injury4possible injury crashes12.9%
100.0%prior 2
No Injury23no injury crashes74.2%
21.1%prior 19

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-06-01 to 2024-06-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-06-01 to 2024-06-30 · Most severe injury per crash record

Top Contributing Factors

Crashes where 'No improper driving' was a factor decreased from 12 incidents (40% share) in June 2023 to 9 incidents (29% share) in June 2024. Conversely, crashes attributed to 'Operating vehicle in erratic, reckless, careless, negligent or aggressive manner' doubled from 2 incidents (6.7% share) to 4 incidents (12.9% share) year-over-year. Additionally, 'Fatigued/asleep' emerged as a factor in 2 crashes in June 2024, compared to none in the prior period.

Officer-Reported Primary Contributing Cause

No improper driving9 (29%)-25.0%prior 12
Inattention5 (16.1%)-16.7%prior 6
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner4 (12.9%)
Failed to yield right of way3 (9.7%)
Fatigued/asleep2 (6.5%)
Followed too closely1 (3.2%)
Driving too fast for conditions1 (3.2%)
Failure to keep in proper lane or running off road1 (3.2%)
Distracted1 (3.2%)
Made an improper turn1 (3.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-06-01 to 2024-06-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The number of crashes occurring in 'Clear' weather conditions slightly decreased from 23 in June 2023 to 21 in June 2024. Crashes on 'Wet' road surfaces increased from 3 incidents in June 2023 to 4 incidents in June 2024. Furthermore, crashes occurring in 'Dark - lighted roadway' conditions increased from 4 to 6, and in 'Dark - roadway not lighted' conditions increased from 1 to 3.

Weather

Clear21 (67.7%)
-8.7%prior 23
Rain4 (12.9%)
Clear/Other3 (9.7%)
Cloudy2 (6.5%)
Cloudy/Other1 (3.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-06-01 to 2024-06-30 · Weather condition at time of crash

Lighting

Daylight21 (67.7%)
-12.5%prior 24
Dark - lighted roadway6 (19.4%)
Dark - roadway not lighted3 (9.7%)
Dawn1 (3.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-06-01 to 2024-06-30 · Lighting condition field

Road Surface

Dry26 (83.9%)
0.0%prior 26
Wet4 (12.9%)
Sand, mud, dirt, oil, gravel1 (3.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-06-01 to 2024-06-30 · Road surface condition field

Vehicles & Demographics

The leading vehicle makes involved in crashes saw shifts year-over-year. Toyota, which was the most frequently involved make with 11 incidents in June 2023, saw its involvement decrease to 4 incidents in June 2024. Conversely, Chevrolet increased its involvement from 6 to 9 incidents, and Ford from 4 to 8 incidents.

Top Vehicle Makes (53 vehicles)

1
CHEVROLET9 (17%)
50.0%prior 6
2
FORD8 (15.1%)
3
JEEP6 (11.3%)
4
HYUNDAI4 (7.5%)
5
TOYOTA4 (7.5%)
-63.6%prior 11
6
VOLKSWAGEN3 (5.7%)
7
HONDA3 (5.7%)
-50.0%prior 6
8
INFI3 (5.7%)
9
KIA2 (3.8%)
10
NISSAN2 (3.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-06-01 to 2024-06-30 · Vehicle unit records

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

Sex Distribution (76 persons with recorded sex)

Male39 (51.3%)
-11.4%prior 44
Female37 (48.7%)
60.9%prior 23

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-06-01 to 2024-06-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes occurring in 40 mph speed zones increased from 9 incidents in June 2023 to 13 incidents in June 2024. Concurrently, crashes in 30 mph zones decreased from 9 incidents to 7 incidents year-over-year. There were no fatal crashes reported in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-06-01 to 2024-06-30 · 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: 2024-06-01 through 2024-06-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2024-06-01 through 2024-06-30 (30 days)
  • Geographic scope: SALISBURY, MA
  • Total crash records analyzed: 31
  • Total persons involved: 79
  • Total vehicles involved: 53

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: June 2024." Published June 21, 2026. Reporting period: 2024-06-01 to 2024-06-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/salisbury/june-2024-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

Salisbury, MA Crash Report — June 2024 | ThatCarHitMe.com