Monthly Traffic Safety Analysis

27 CRASHES IN
LANCASTER, MA
JANUARY 2026

All metrics benchmarked againstJanuary 2025

In January 2026, Lancaster experienced 27 crashes, a 12.5% increase from the 24 crashes reported in January 2025. A notable year-over-year shift was observed in total injuries, which doubled from 3 in the prior period to 6 in the current period.

27

12.5%was 24

Total Crash Events

0

Persons Killed

6

100.0%was 3

Persons Injured

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

Trend Summary

Overall, crashes in Lancaster showed an upward trend, increasing by 3 incidents, or 12.5%, from 24 crashes in January 2025 to 27 crashes in January 2026. Total injuries also saw a significant rise, doubling from 3 to 6 over the same period, while fatalities remained at zero in both months.

2

Hit-and-Run Crashes — January 2026

0.0% vs prior (2)

The number of hit-and-run crashes remained consistent at 2 for both January 2025 and January 2026. However, the hit-and-run crash rate slightly decreased from 8.3% in the prior period to 7.4% in the current period, reflecting the overall increase in total crashes.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

6

Motorists Injured

Prior: 3100.0%

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 peak day for crashes remained Saturday, though the count decreased from 7 in January 2025 to 6 in January 2026. A shift in peak crash hour was observed, moving from 5 PM (4 crashes) in the prior year to 11 PM (4 crashes) in the current year. Additionally, crashes on Sunday and Wednesday increased from 1 to 4 and 2 to 5 respectively, while crashes on Tuesday and Friday decreased from 4 to 1 and 5 to 3.

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

Fatal crashes remained at zero for both January 2025 and January 2026. However, total injuries increased by 100%, from 3 in the prior period to 6 in the current period. The number of crashes resulting in possible injuries doubled from 1 to 2, contributing to a slight increase in the share of injury-involved crashes from 12.5% to 14.8%.

Outcome by Severity (Crash Events)

Minor Injury2minor injury crashes7.4%
0.0%prior 2
Possible Injury2possible injury crashes7.4%
100.0%prior 1
No Injury23no injury crashes85.2%
15.0%prior 20

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

The number of crashes attributed to "No improper driving" increased from 6 to 8, and "Driving too fast for conditions" also saw an increase from 2 to 4 crashes. Conversely, crashes where "Failed to yield right of way" was a factor decreased from 3 to 1. New contributing factors appearing in January 2026 included "Operating vehicle in erratic, reckless, careless, negligent or aggressive manner" (3 crashes) and "Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway" (2 crashes).

Officer-Reported Primary Contributing Cause

No improper driving8 (29.6%)33.3%prior 6
Driving too fast for conditions4 (14.8%)
Inattention3 (11.1%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner3 (11.1%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway2 (7.4%)
Disregarded traffic signs, signals, road markings2 (7.4%)
Exceeded authorized speed limit1 (3.7%)
Fatigued/asleep1 (3.7%)
Failed to yield right of way1 (3.7%)

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

Regarding road surface conditions, crashes on dry roads decreased from 17 to 14, while those on snowy roads increased from 3 to 6, and icy roads from 1 to 3. In terms of lighting, daylight crashes decreased from 15 to 11, with a notable increase in crashes occurring in "Dark - lighted roadway" conditions, rising from 0 to 7. The number of crashes during clear weather conditions remained stable at 17 for both periods, while crashes during snowy conditions increased from 5 to 7.

Weather

Clear15 (55.6%)
7.1%prior 14
Snow3 (11.1%)
Snow/Snow2 (7.4%)
Clear/Clear2 (7.4%)
Rain1 (3.7%)
Sleet, hail (freezing rain or drizzle)1 (3.7%)
Cloudy/Cloudy1 (3.7%)
Snow/Sleet, hail (freezing rain or drizzle)1 (3.7%)
Clear/Snow1 (3.7%)

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

Lighting

Daylight11 (40.7%)
-26.7%prior 15
Dark - lighted roadway7 (25.9%)
Dark - roadway not lighted6 (22.2%)
-14.3%prior 7
Dawn2 (7.4%)
Dusk1 (3.7%)

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

Road Surface

Dry14 (51.9%)
-17.6%prior 17
Snow6 (22.2%)
Wet4 (14.8%)
Ice3 (11.1%)

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

Vehicles & Demographics

Top Vehicle Makes (41 vehicles)

1
TOYOTA7 (17.1%)
40.0%prior 5
2
CHEVROLET5 (12.2%)
3
JEEP4 (9.8%)
4
HONDA4 (9.8%)
-20.0%prior 5
5
SUBARU2 (4.9%)
6
HYUNDAI2 (4.9%)
7
NISSAN2 (4.9%)
8
PONT1 (2.4%)
9
PTRB1 (2.4%)
10
STRN1 (2.4%)

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

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

Sex Distribution (52 persons with recorded sex)

Male30 (57.7%)
30.4%prior 23
Female22 (42.3%)
100.0%prior 11

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 30 mph zones increased from 8 to 9, and 50 mph zones increased from 1 to 2. Conversely, crashes in 40 mph zones decreased from 5 to 3, and 45 mph zones decreased from 2 to 1. A crash in a 10 mph zone was recorded in January 2026, where none was recorded in the prior year, with no fatal crashes occurring in any speed zone during either period.

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: LANCASTER, MA
  • Total crash records analyzed: 27
  • Total persons involved: 60
  • Total vehicles involved: 41

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). "LANCASTER, 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/lancaster/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

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