Monthly Traffic Safety Analysis

83 CRASHES IN
ANDOVER, MA
JANUARY 2025

All metrics benchmarked againstJanuary 2024

ANDOVER experienced a notable decrease in total crashes in January 2025 compared to January 2024, with 83 crashes recorded this period versus 110 crashes in the prior period, representing a 24.6% reduction. Despite this overall decline, the number of injured persons increased by 35.7%, from 14 to 19, indicating a shift in crash outcomes.

83

▼ -24.5%was 110

Total Crash Events

0

Persons Killed

19

▲ 35.7%was 14

Persons Injured

5

▼ -61.5%was 13

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. 2 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-01-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crashes in ANDOVER decreased year-over-year, falling from 110 in January 2024 to 83 in January 2025, a reduction of 27 crashes. While total crashes declined, the number of injuries rose from 14 to 19, an increase of 35.7%. Fatalities remained at zero for both periods.

5

Hit-and-Run Crashes — January 2025

▼ -61.5% vs prior (13)

Hit-and-run crashes decreased significantly year-over-year, dropping from 13 incidents in January 2024 to 5 in January 2025. This resulted in the hit-and-run rate falling from 11.8% of all crashes to 6%.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 0→0.0%

1

Cyclists Injured

Prior: 0→%

18

Motorists Injured

Prior: 14↑28.6%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-01-31 · 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 Tuesday in both periods, though the count decreased from 24 crashes in January 2024 to 17 crashes in January 2025. The peak crash hour shifted from 6 p.m. with 15 crashes in January 2024 to 2 p.m. with 10 crashes in January 2025.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-01-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-01-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While there were no fatal crashes or fatalities in either period, the proportion of crashes resulting in injury increased. The total number of injured persons rose by 35.7%, from 14 in January 2024 to 19 in January 2025. This period saw 12 minor injuries and 5 possible injuries, compared to 2 serious, 7 minor, and 3 possible injuries in the prior period.

Outcome by Severity (Crash Events)

Minor Injury12minor injury crashes14.5%
↑71.4%prior 7
Possible Injury5possible injury crashes6%
↑66.7%prior 3
No Injury64no injury crashes77.1%
↓-34.0%prior 97

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

Severity Distribution (Crash Events)

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

Top Contributing Factors

The top contributing factors saw significant shifts in counts year-over-year. Crashes attributed to 'No improper driving' increased from 14 to 19, a 35.7% rise. Conversely, 'Followed too closely' decreased by 8 crashes (from 19 to 11), and 'Driving too fast for conditions' decreased by 6 crashes (from 15 to 9).

Officer-Reported Primary Contributing Cause

No improper driving19 (22.9%)↑35.7%prior 14
Failed to yield right of way12 (14.5%)↓-7.7%prior 13
Followed too closely11 (13.3%)↓-42.1%prior 19
Driving too fast for conditions9 (10.8%)↓-40.0%prior 15
Inattention8 (9.6%)↓-20.0%prior 10
Failure to keep in proper lane or running off road4 (4.8%)↓-42.9%prior 7
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway3 (3.6%)
Other improper action3 (3.6%)
Disregarded traffic signs, signals, road markings3 (3.6%)↓-40.0%prior 5
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner2 (2.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-01-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in snowy conditions significantly decreased, falling from 24 in January 2024 to 7 in January 2025. Similarly, crashes on wet road surfaces declined from 18 to 10, and those on icy roads dropped from 6 to 2. Crashes occurring in daylight decreased from 64 to 53, while those in 'Dark - roadway not lighted' conditions saw a substantial reduction from 20 to 8.

Weather

Clear/Clear32 (39.5%)
↑113.3%prior 15
Clear27 (33.3%)
↓-34.1%prior 41
Snow/Snow4 (4.9%)
Snow3 (3.7%)
↓-80.0%prior 15
Cloudy3 (3.7%)
↓-70.0%prior 10
Cloudy/Cloudy3 (3.7%)
Snow/Sleet, hail (freezing rain or drizzle)2 (2.5%)
↓-66.7%prior 6
Sleet, hail (freezing rain or drizzle)1 (1.2%)
Blowing sand, snow1 (1.2%)
Cloudy/Clear1 (1.2%)

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

Lighting

Daylight53 (64.6%)
↓-17.2%prior 64
Dark - lighted roadway15 (18.3%)
↓-21.1%prior 19
Dark - roadway not lighted8 (9.8%)
↓-60.0%prior 20
Dusk4 (4.9%)
Dawn1 (1.2%)
Dark - unknown roadway lighting1 (1.2%)

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

Road Surface

Dry58 (70.7%)
↑7.4%prior 54
Snow12 (14.6%)
↓-57.1%prior 28
Wet10 (12.2%)
↓-44.4%prior 18
Ice2 (2.4%)
↓-66.7%prior 6

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

Vehicles & Demographics

The total number of vehicles involved in crashes decreased from 191 to 150 year-over-year. Among vehicle makes, Honda saw a decrease from 40 vehicles involved to 23, and Toyota from 29 to 18. In terms of persons involved, the 16-20 age group saw a decrease from 26 to 15, and the 21-25 age group from 30 to 13, while the 65+ age group increased from 17 to 22.

Top Vehicle Makes (150 vehicles)

1
HONDA23 (15.3%)
↓-42.5%prior 40
2
TOYOTA18 (12%)
↓-37.9%prior 29
3
CHEVROLET13 (8.7%)
↑8.3%prior 12
4
BMW9 (6%)
5
FORD9 (6%)
↓-10.0%prior 10
6
NISSAN8 (5.3%)
↑14.3%prior 7
7
JEEP6 (4%)
↓-14.3%prior 7
8
SUBARU5 (3.3%)
↓-44.4%prior 9
9
DODGE5 (3.3%)
10
FREIGHTLINER CO4 (2.7%)

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

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

Sex Distribution (171 persons with recorded sex)

Male97 (56.7%)
↓-19.8%prior 121
Female74 (43.3%)
↓-6.3%prior 79

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-01-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes occurring in 65 mph zones decreased from 42 in January 2024 to 26 in January 2025, a reduction of 16 crashes. Crashes in 30 mph zones also saw a decrease, falling from 24 to 9. Conversely, crashes in 25 mph zones increased from 16 to 19.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-01-31 · 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: 2025-01-01 through 2025-01-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2025-01-01 through 2025-01-31 (31 days)
  • Geographic scope: ANDOVER, MA
  • Total crash records analyzed: 83
  • Total persons involved: 181
  • Total vehicles involved: 150

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). "ANDOVER, MA Crash Intelligence Report: January 2025." Published June 21, 2026. Reporting period: 2025-01-01 to 2025-01-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/andover/january-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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Andover, MA Crash Report — January 2025 | ThatCarHitMe.com