Monthly Traffic Safety Analysis

10 CRASHES IN
ASHLAND, MA
MAY 2025

All metrics benchmarked againstMay 2024

In May 2025, ASHLAND recorded 10 total crashes, a decrease of 28.6% compared to the 14 crashes reported in May 2024. Total injuries also saw a notable reduction, dropping by 33.3% from 3 injuries in May 2024 to 2 injuries in May 2025.

10

-28.6%was 14

Total Crash Events

0

Persons Killed

2

-33.3%was 3

Persons Injured

0

Fatal Crash Events

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 · 2025-05-01 to 2025-05-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend for crashes in ASHLAND during May shows a decrease year-over-year, with total crashes falling by 28.6% from 14 in May 2024 to 10 in May 2025. This reduction indicates a positive shift in crash frequency for the period.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

2

Motorists Injured

Prior: 3-33.3%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes remained Friday in both May 2025 and May 2024, with 4 crashes each period. However, the peak hour shifted from 5p with 3 crashes in May 2024 to 2p with 3 crashes in May 2025. Crashes on Wednesdays decreased from 3 to 0, while crashes on Mondays increased from 2 to 3 and Tuesdays from 1 to 3.

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)

Crash Severity Breakdown

There were no fatalities reported in either May 2025 or May 2024. Total injuries decreased by 33.3% from 3 in May 2024 to 2 in May 2025. Specifically, May 2024 reported 1 serious injury and 1 minor injury, while May 2025 reported 2 minor injuries and no serious injuries.

Outcome by Severity (Crash Events)

Minor Injury1minor injury crashes10%
0.0%prior 1
No Injury9no injury crashes90%
-25.0%prior 12

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Most severe injury per crash record

Top Contributing Factors

The number of crashes attributed to 'Inattention' increased by 50%, from 2 crashes in May 2024 to 3 crashes in May 2025. Conversely, crashes where 'No improper driving' was cited decreased by 25%, from 4 crashes in May 2024 to 3 crashes in May 2025. 'Failed to yield right of way' remained a contributing factor in 2 crashes for both periods.

Officer-Reported Primary Contributing Cause

Inattention3 (30%)
No improper driving3 (30%)
Failed to yield right of way2 (20%)
Followed too closely1 (10%)

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

The proportion of crashes occurring on wet road surfaces significantly increased, from 2 crashes (14.3% of total) in May 2024 to 5 crashes (50% of total) in May 2025. In May 2024, 11 crashes occurred in 'Clear' weather, while in May 2025, 'Clear' weather accounted for 4 crashes, and various 'Rain' conditions were present in 3 crashes.

Weather

Clear4 (40.0%)
-63.6%prior 11
Cloudy3 (30.0%)
Cloudy/Rain1 (10.0%)
Rain/Cloudy1 (10.0%)
Rain/Fog, smog, smoke1 (10.0%)

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

Lighting

Daylight9 (90.0%)
Dawn1 (10.0%)

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

Road Surface

Dry5 (50.0%)
-58.3%prior 12
Wet5 (50.0%)

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 (19 vehicles)

1
TOYOTA3 (15.8%)
2
SUBARU2 (10.5%)
3
VOLKSWAGEN2 (10.5%)
4
HONDA2 (10.5%)
5
MERCEDES-BENZ2 (10.5%)
6
MACK1 (5.3%)
7
VOLVO1 (5.3%)
8
ACURA1 (5.3%)
9
WHCO1 (5.3%)
10
FORD1 (5.3%)

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

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

Sex Distribution (23 persons with recorded sex)

Male15 (65.2%)
36.4%prior 11
Female8 (34.8%)
-50.0%prior 16

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 in the 30 mph speed zone decreased from 6 in May 2024 to 4 in May 2025, while crashes in the 35 mph zone decreased from 4 to 1. The 25 mph speed zone maintained 3 crashes in both periods. A crash occurred in a 40 mph zone in May 2025, a speed zone not present in the May 2024 data.

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: ASHLAND, MA
  • Total crash records analyzed: 10
  • Total persons involved: 24
  • Total vehicles involved: 19

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). "ASHLAND, 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/ashland/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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Ashland, MA Crash Report — May 2025 | ThatCarHitMe.com