Monthly Traffic Safety Analysis

73 CRASHES IN
AUBURN, MA
JANUARY 2026

All metrics benchmarked againstJanuary 2025

Total crashes in Auburn increased by 23.73%, from 59 crashes in January 2025 to 73 crashes in January 2026. Concurrently, total injuries rose from 17 to 24. The most notable shift was a significant decrease in hit-and-run crashes, which fell from 8 to 3.

73

23.7%was 59

Total Crash Events

0

Persons Killed

24

41.2%was 17

Persons Injured

3

-62.5%was 8

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 · 2026-01-01 to 2026-01-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crashes in Auburn are trending upwards year-over-year, with a 23.73% increase in total crashes from 59 in January 2025 to 73 in January 2026. This rise was accompanied by a 41.18% increase in total injuries, from 17 to 24, indicating a worsening safety trend for the month.

3

Hit-and-Run Crashes — January 2026

-62.5% vs prior (8)

Hit-and-run crashes decreased substantially, dropping from 8 incidents in January 2025 to 3 in January 2026. This resulted in a significant reduction in the hit-and-run rate, which fell from 13.6% of all crashes to 4.1%.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

2

Pedestrians Injured

Prior: 0%

22

Motorists Injured

Prior: 1729.4%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal distribution of crashes shifted year-over-year. In January 2025, the peak day for crashes was Thursday with 13 incidents, and the peak hour was 6 PM with 7 crashes. In January 2026, Wednesday became the peak day with 14 crashes, and the peak hour shifted to 11 AM with 13 crashes.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatalities remained at 0 in both January 2025 and January 2026. Total injuries increased from 17 in January 2025 to 24 in January 2026. The number of serious injuries rose from 0 to 1, while minor injuries increased from 11 to 17, and possible injuries remained stable at 6.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes1.4%
Minor Injury10minor injury crashes13.7%
66.7%prior 6
Possible Injury5possible injury crashes6.8%
25.0%prior 4
No Injury57no injury crashes78.1%
16.3%prior 49

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Most severe injury per crash record

Top Contributing Factors

Several contributing factors saw notable changes in crash counts. Crashes attributed to 'Followed too closely' increased from 3 to 9, a 200% rise. 'Driving too fast for conditions' also surged from 1 to 8 crashes, a 700% increase. Conversely, 'Inattention' as a factor decreased by 45.5%, from 11 crashes in January 2025 to 6 in January 2026.

Officer-Reported Primary Contributing Cause

No improper driving20 (27.4%)25.0%prior 16
Followed too closely9 (12.3%)
Failed to yield right of way9 (12.3%)12.5%prior 8
Driving too fast for conditions8 (11%)
Inattention6 (8.2%)-45.5%prior 11
Failure to keep in proper lane or running off road5 (6.8%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway5 (6.8%)
Other improper action3 (4.1%)
Glare1 (1.4%)
Fatigued/asleep1 (1.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Weather conditions during crashes shifted, with clear conditions decreasing from 43 crashes in January 2025 to 30 in January 2026, while snow-related crashes increased from 2 to 12. Correspondingly, crashes on dry road surfaces decreased from 50 to 42, but those on snowy surfaces significantly increased from 1 to 17. Daylight conditions saw more crashes, rising from 35 to 47 incidents year-over-year.

Weather

Clear30 (41.1%)
-30.2%prior 43
Snow12 (16.4%)
Clear/Clear8 (11.0%)
33.3%prior 6
Cloudy6 (8.2%)
Snow/Snow4 (5.5%)
Rain2 (2.7%)
Snow/Sleet, hail (freezing rain or drizzle)2 (2.7%)
Clear/Cloudy2 (2.7%)
Sleet, hail (freezing rain or drizzle)1 (1.4%)
Clear/Unknown1 (1.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Weather condition at time of crash

Lighting

Daylight47 (64.4%)
34.3%prior 35
Dark - lighted roadway16 (21.9%)
23.1%prior 13
Dark - roadway not lighted5 (6.8%)
-44.4%prior 9
Dusk3 (4.1%)
Dark - unknown roadway lighting1 (1.4%)
Dawn1 (1.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Lighting condition field

Road Surface

Dry42 (57.5%)
-16.0%prior 50
Snow17 (23.3%)
Wet9 (12.3%)
28.6%prior 7
Ice4 (5.5%)
Slush1 (1.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes increased from 119 to 128 year-over-year. Among top makes, Ford saw a substantial increase from 9 to 23 vehicles, and Chevrolet increased from 8 to 15. The 65+ age group experienced a decrease in involved persons from 21 to 13, while the 26-34 and 35-44 age groups each saw an increase of 5 persons involved.

Top Vehicle Makes (128 vehicles)

1
TOYOTA24 (18.8%)
33.3%prior 18
2
FORD23 (18%)
155.6%prior 9
3
CHEVROLET15 (11.7%)
87.5%prior 8
4
SUBARU13 (10.2%)
62.5%prior 8
5
HONDA12 (9.4%)
9.1%prior 11
6
JEEP6 (4.7%)
0.0%prior 6
7
MERCEDES-BENZ4 (3.1%)
8
GMC4 (3.1%)
9
ACURA4 (3.1%)
10
HYUNDAI2 (1.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Vehicle unit records

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

Sex Distribution (147 persons with recorded sex)

Male83 (56.5%)
22.1%prior 68
Female64 (43.5%)
4.9%prior 61

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in the 65 mph speed zones saw a significant increase, rising from 9 incidents in January 2025 to 20 in January 2026. Conversely, crashes in 30 mph zones decreased from 17 to 9. Crashes in 40 mph zones experienced a slight increase from 14 to 17.

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

Data Coverage

  • Reporting period: 2026-01-01 through 2026-01-31 (31 days)
  • Geographic scope: AUBURN, MA
  • Total crash records analyzed: 73
  • Total persons involved: 155
  • Total vehicles involved: 128

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: January 2026." Published June 21, 2026. Reporting period: 2026-01-01 to 2026-01-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/auburn/january-2026-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

Auburn, MA Crash Report — January 2026 | ThatCarHitMe.com