Monthly Traffic Safety Analysis

58 CRASHES IN
AUBURN, MA
JUNE 2025

All metrics benchmarked againstJune 2024

In June 2025, AUBURN recorded 58 total crashes, a decrease from 84 crashes in June 2024, representing a 30.95% reduction. The most notable year-over-year shift was a substantial 71.43% decrease in total injuries, falling from 35 to 10.

58

▼ -31.0%was 84

Total Crash Events

0

Persons Killed

10

▼ -71.4%was 35

Persons Injured

4

▼ -20.0%was 5

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. 1 crash with unreported severity is not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash data for AUBURN shows a significant downward trend year-over-year. Total crashes decreased by 30.95%, from 84 in June 2024 to 58 in June 2025. This was accompanied by a substantial 71.43% reduction in total injuries, which fell from 35 to 10.

4

Hit-and-Run Crashes — June 2025

▼ -20.0% vs prior (5)

The number of hit-and-run crashes decreased from 5 in June 2024 to 4 in June 2025. However, the hit-and-run rate, as a proportion of total crashes, slightly increased from 6% to 6.9% year-over-year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 0→0.0%

1

Pedestrians Injured

Prior: 1→0.0%

2

Cyclists Injured

Prior: 0→%

7

Motorists Injured

Prior: 34↓-79.4%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · 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 Wednesday in June 2024 (18 crashes) to Monday in June 2025 (14 crashes). The peak hour for crashes remained 2 PM in both periods, though the number of crashes at this hour decreased from 13 in June 2024 to 8 in June 2025.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There were no fatal crashes reported in either June 2024 or June 2025. Crashes resulting in serious injuries (Severity A) decreased from 1 in June 2024 to 0 in June 2025. Minor injury crashes (Severity B) decreased from 19 to 8, and possible injury crashes (Severity C) decreased from 6 to 1, contributing to a lower overall proportion of injury-involved crashes in June 2025.

Outcome by Severity (Crash Events)

Minor Injury8minor injury crashes13.8%
↓-57.9%prior 19
Possible Injury1possible injury crashes1.7%
↓-83.3%prior 6
No Injury48no injury crashes82.8%
↓-17.2%prior 58

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factor in June 2025 was 'Followed too closely' with 14 crashes, an increase of 3 crashes (27.27%) from 11 in June 2024. Conversely, 'No improper driving' decreased by 8 crashes from 18 to 10, and 'Failed to yield right of way' saw a significant reduction of 10 crashes, from 14 to 4.

Officer-Reported Primary Contributing Cause

Followed too closely14 (24.1%)↑27.3%prior 11
No improper driving10 (17.2%)↓-44.4%prior 18
Inattention9 (15.5%)↓-35.7%prior 14
Failed to yield right of way4 (6.9%)↓-71.4%prior 14
Failure to keep in proper lane or running off road4 (6.9%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway3 (5.2%)
Disregarded traffic signs, signals, road markings2 (3.4%)
Made an improper turn2 (3.4%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner2 (3.4%)
Driving too fast for conditions1 (1.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in clear weather conditions decreased from 63 in June 2024 to 36 in June 2025. Similarly, crashes on wet road surfaces saw a reduction from 6 to 2. The number of crashes during daylight hours also decreased, from 76 to 50.

Weather

Clear36 (62.1%)
↓-42.9%prior 63
Clear/Clear12 (20.7%)
Clear/Unknown4 (6.9%)
Cloudy3 (5.2%)
↓-57.1%prior 7
Cloudy/Cloudy1 (1.7%)
Rain1 (1.7%)
Unknown/Clear1 (1.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Weather condition at time of crash

Lighting

Daylight50 (86.2%)
↓-34.2%prior 76
Dark - roadway not lighted4 (6.9%)
Dark - lighted roadway3 (5.2%)
Dawn1 (1.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Lighting condition field

Road Surface

Dry55 (94.8%)
↓-27.6%prior 76
Wet2 (3.4%)
↓-66.7%prior 6
Sand, mud, dirt, oil, gravel1 (1.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes decreased from 158 in June 2024 to 110 in June 2025. Toyota remained the most frequently involved vehicle make, though its count decreased from 18 to 13. All age groups saw a reduction in the number of persons involved, with the 0-15 age group decreasing from 9 to 2 and the 16-20 age group from 26 to 15.

Top Vehicle Makes (110 vehicles)

1
TOYOTA13 (11.8%)
↓-27.8%prior 18
2
FORD12 (10.9%)
↓-29.4%prior 17
3
HONDA9 (8.2%)
↓-47.1%prior 17
4
KIA8 (7.3%)
5
SUBARU7 (6.4%)
↓-30.0%prior 10
6
NISSAN7 (6.4%)
↓-50.0%prior 14
7
CHEVROLET6 (5.5%)
↓-53.8%prior 13
8
LEXUS5 (4.5%)
9
ACURA3 (2.7%)
10
GMC3 (2.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Vehicle unit records

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

Sex Distribution (118 persons with recorded sex)

Male67 (56.8%)
↓-39.1%prior 110
Female51 (43.2%)
↓-12.1%prior 58

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes in the 65 mph speed zone decreased slightly from 24 in June 2024 to 21 in June 2025. A more significant reduction was observed in the 30 mph zone, where crashes fell from 23 to 11. Conversely, crashes in the 25 mph zone increased from 2 to 4.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-06-01 to 2025-06-30 · 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: 2025-06-01 through 2025-06-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2025-06-01 through 2025-06-30 (30 days)
  • Geographic scope: AUBURN, MA
  • Total crash records analyzed: 58
  • Total persons involved: 129
  • Total vehicles involved: 110

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). "AUBURN, MA Crash Intelligence Report: June 2025." Published June 21, 2026. Reporting period: 2025-06-01 to 2025-06-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/auburn/june-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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Auburn, MA Crash Report — June 2025 | ThatCarHitMe.com