Monthly Traffic Safety Analysis

15 CRASHES IN
ASHLAND, MA
SEPTEMBER 2025

All metrics benchmarked againstSeptember 2024

In September 2025, Ashland experienced 15 total crashes, a decrease of 6.25% compared to the 16 crashes recorded in September 2024. Fatalities remained at zero in both periods, while total injuries held steady at 3. A notable shift was observed in hit-and-run incidents, which doubled from 1 in the prior period to 2 in the current period.

15

-6.3%was 16

Total Crash Events

0

Persons Killed

3

Persons Injured

2

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 · 2025-09-01 to 2025-09-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates a slight decrease in total crashes, falling from 16 in September 2024 to 15 in September 2025, representing a 6.25% reduction. Despite this, the number of total injuries remained consistent at 3 in both periods. Fatalities were not reported in either the current or prior September.

2

Hit-and-Run Crashes — September 2025

100.0% vs prior (1)

Hit-and-run crashes increased from 1 incident in September 2024 to 2 incidents in September 2025. This resulted in the hit-and-run rate rising from 6.3% of total crashes in the prior period to 13.3% in the current period.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

1

Pedestrians Injured

Prior: 0%

2

Motorists Injured

Prior: 3-33.3%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-09-01 to 2025-09-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns for crashes shifted year-over-year. In September 2025, the peak day for crashes was Friday with 3 incidents, and the peak hour was 3 PM with 4 incidents. This contrasts with September 2024, where Wednesday saw the highest number of crashes at 4, and 1 PM was the peak hour with 3 incidents.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-09-01 to 2025-09-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-09-01 to 2025-09-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatal crashes remained at zero in both September 2024 and September 2025. The distribution of injury severity changed, with 2 serious injury (A) crashes and 1 possible injury (C) crash reported in the current period. In the prior period, there was 1 minor injury (B) crash and 1 possible injury (C) crash, indicating a shift from minor to serious injuries.

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes13.3%
Possible Injury1possible injury crashes6.7%
0.0%prior 1
No Injury12no injury crashes80%
-14.3%prior 14

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-09-01 to 2025-09-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-09-01 to 2025-09-30 · Most severe injury per crash record

Top Contributing Factors

The top contributing factor, 'No improper driving,' saw a significant increase, rising from 2 crashes in the prior period to 7 crashes in the current period. Conversely, 'Inattention' decreased from 3 crashes to 1 crash, and 'Failed to yield right of way' also decreased from 3 crashes to 1 crash. 'Disregarded traffic signs, signals, road markings' remained consistent with 1 crash in both periods.

Officer-Reported Primary Contributing Cause

No improper driving7 (46.7%)
Disregarded traffic signs, signals, road markings1 (6.7%)
Failed to yield right of way1 (6.7%)
Inattention1 (6.7%)
Wrong side or wrong way1 (6.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-09-01 to 2025-09-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Clear weather conditions were associated with 14 crashes in September 2025, a slight decrease from 15 crashes in September 2024. Crashes occurring in rain or cloudy conditions remained at 1 incident for both periods. Similarly, dry road surface conditions accounted for 14 crashes in the current period, down from 15 in the prior period, while wet road conditions were associated with 1 crash in both periods.

Weather

Clear14 (93.3%)
-6.7%prior 15
Rain1 (6.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-09-01 to 2025-09-30 · Weather condition at time of crash

Lighting

Daylight12 (80.0%)
Dark - lighted roadway1 (6.7%)
Dark - roadway not lighted1 (6.7%)
Dusk1 (6.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-09-01 to 2025-09-30 · Lighting condition field

Road Surface

Dry14 (93.3%)
-6.7%prior 15
Wet1 (6.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-09-01 to 2025-09-30 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (25 vehicles)

1
FORD3 (12%)
-40.0%prior 5
2
TOYOTA3 (12%)
-50.0%prior 6
3
HONDA3 (12%)
-50.0%prior 6
4
GMC2 (8%)
5
LNDR1 (4%)
6
MAZDA1 (4%)
7
PTRB1 (4%)
8
RAM1 (4%)
9
SUBARU1 (4%)
10
TESL1 (4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-09-01 to 2025-09-30 · Vehicle unit records

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

Sex Distribution (27 persons with recorded sex)

Male15 (55.6%)
-11.8%prior 17
Female12 (44.4%)
-14.3%prior 14

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-09-01 to 2025-09-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes occurring in 25 mph zones increased significantly from 3 in the prior period to 10 in the current period. Conversely, crashes in 30 mph zones decreased from 3 to 1, and those in 35 mph zones fell from 7 to 4. No fatal crashes were recorded in any speed limit zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-09-01 to 2025-09-30 · 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: 2025-09-01 through 2025-09-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2025-09-01 through 2025-09-30 (30 days)
  • Geographic scope: ASHLAND, MA
  • Total crash records analyzed: 15
  • Total persons involved: 31
  • Total vehicles involved: 25

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: September 2025." Published June 21, 2026. Reporting period: 2025-09-01 to 2025-09-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/ashland/september-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 — September 2025 | ThatCarHitMe.com