Monthly Traffic Safety Analysis

35 CRASHES IN
SOMERVILLE, MA
FEBRUARY 2025

All metrics benchmarked againstFebruary 2024

In February 2025, Somerville experienced 35 total crashes, marking an 18.6% decrease compared to the 43 crashes recorded in February 2024. Total injuries also saw a slight decrease, falling from 14 to 13, a 7.1% reduction. A notable shift was observed in hit-and-run incidents, which increased by 100% from 1 crash to 2 crashes year-over-year.

35

-18.6%was 43

Total Crash Events

0

Persons Killed

13

-7.1%was 14

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 · 2025-02-01 to 2025-02-28 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash data for Somerville indicates a downward trend year-over-year, with total crashes decreasing by 18.6% from 43 in the prior period to 35 in the current period. Similarly, the number of total injuries declined by 7.1%, from 14 to 13. Fatalities remained at zero for both periods, indicating no change in the most severe crash outcomes.

2

Hit-and-Run Crashes — February 2025

100.0% vs prior (1)

Hit-and-run crashes increased by 100% year-over-year, rising from 1 incident in February 2024 to 2 incidents in February 2025. This change also resulted in the hit-and-run rate more than doubling, increasing from 2.3% to 5.7% of all crashes.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

2

Pedestrians Injured

Prior: 0%

2

Cyclists Injured

Prior: 20.0%

9

Motorists Injured

Prior: 12-25.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

Wednesday remained the peak day for crashes in both periods, though the count decreased from 9 crashes in the prior period to 8 crashes in the current period. The peak crash hour shifted from 8 AM (5 crashes) in February 2024 to 9 AM (6 crashes) in February 2025. Crashes on Mondays also saw a decrease, falling from 9 to 6 year-over-year.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The distribution of crash severity showed some shifts, with minor injury crashes increasing their share from 20.9% to 28.6% of all crashes. Conversely, possible injury crashes decreased their share from 7% to 5.7%. There were no fatal crashes in either February 2024 or February 2025, maintaining a 0% fatal crash rate.

Outcome by Severity (Crash Events)

Minor Injury10minor injury crashes28.6%
11.1%prior 9
Possible Injury2possible injury crashes5.7%
-33.3%prior 3
No Injury23no injury crashes65.7%
-23.3%prior 30

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Most severe injury per crash record

Top Contributing Factors

The top contributing factor, 'Followed too closely,' decreased in count from 9 crashes in the prior period to 7 crashes in the current period. 'No improper driving' increased slightly from 7 crashes to 8 crashes. 'Failure to keep in proper lane or running off road' also saw an increase in count, rising from 1 crash to 3 crashes year-over-year.

Officer-Reported Primary Contributing Cause

No improper driving8 (22.9%)14.3%prior 7
Followed too closely7 (20%)-22.2%prior 9
Failure to keep in proper lane or running off road3 (8.6%)
Disregarded traffic signs, signals, road markings2 (5.7%)
Inattention2 (5.7%)
Failed to yield right of way2 (5.7%)
Exceeded authorized speed limit1 (2.9%)
Driving too fast for conditions1 (2.9%)
Other improper action1 (2.9%)
Physical impairment1 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Road surface conditions showed a significant shift, with crashes on dry roads decreasing from 38 to 14. In contrast, crashes on wet roads doubled from 5 to 10, and 8 crashes occurred on ice in the current period, a condition not reported in the prior period. The number of crashes occurring in clear weather conditions also decreased notably, from 28 to 16.

Weather

Clear16 (45.7%)
-42.9%prior 28
Clear/Clear7 (20.0%)
40.0%prior 5
Cloudy/Cloudy3 (8.6%)
Cloudy3 (8.6%)
Rain/Cloudy2 (5.7%)
Clear/Other1 (2.9%)
Severe crosswinds1 (2.9%)
Sleet, hail (freezing rain or drizzle)1 (2.9%)
Snow/Cloudy1 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Weather condition at time of crash

Lighting

Daylight18 (51.4%)
-21.7%prior 23
Dark - lighted roadway13 (37.1%)
-23.5%prior 17
Dark - roadway not lighted1 (2.9%)
Dark - unknown roadway lighting1 (2.9%)
Dawn1 (2.9%)
Dusk1 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Lighting condition field

Road Surface

Dry14 (42.4%)
-63.2%prior 38
Wet10 (30.3%)
100.0%prior 5
Ice8 (24.2%)
Snow1 (3.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Road surface condition field

Vehicles & Demographics

While Honda vehicles remained prominent, their crash involvement decreased slightly from 14 to 13, while Toyota's increased from 11 to 13, making them equally represented in the current period's top makes. The 55-64 age group saw a significant reduction in representation, falling from 11 persons to 3 persons involved in crashes. Overall, the number of males involved in crashes decreased from 59 to 40, and females from 28 to 24.

Top Vehicle Makes (66 vehicles)

1
HONDA13 (19.7%)
-7.1%prior 14
2
TOYOTA13 (19.7%)
18.2%prior 11
3
FORD9 (13.6%)
0.0%prior 9
4
KIA4 (6.1%)
5
PRCA3 (4.5%)
6
HYUNDAI3 (4.5%)
7
SUBARU3 (4.5%)
8
MERCEDES-BENZ3 (4.5%)
9
JEEP2 (3%)
-60.0%prior 5
10
AUDI2 (3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Vehicle unit records

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

Sex Distribution (64 persons with recorded sex)

Male40 (62.5%)
-32.2%prior 59
Female24 (37.5%)
-14.3%prior 28

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 25 mph zones, the most frequent speed limit for crashes, saw a slight decrease from 20 to 19 incidents. There was a notable reduction in crashes within 35 mph zones, falling from 7 to 3, and in 55 mph zones, decreasing from 6 to 2. Conversely, 15 mph zones, which had no crashes in the prior period, recorded 1 crash in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-02-01 to 2025-02-28 · 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: 2025-02-01 through 2025-02-28
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2025-02-01 through 2025-02-28 (28 days)
  • Geographic scope: SOMERVILLE, MA
  • Total crash records analyzed: 35
  • Total persons involved: 75
  • Total vehicles involved: 66

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). "SOMERVILLE, MA Crash Intelligence Report: February 2025." Published June 21, 2026. Reporting period: 2025-02-01 to 2025-02-28. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/somerville/february-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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Somerville, MA Crash Report — February 2025 | ThatCarHitMe.com