Monthly Traffic Safety Analysis

23 CRASHES IN
WATERTOWN, MA
MARCH 2026

All metrics benchmarked againstMarch 2025

In March 2026, Watertown recorded 23 total crashes, an increase of 9.5% compared to the 21 crashes reported in March 2025. Fatalities remained at zero for both periods, while total injuries decreased slightly from 7 to 6. A notable shift was observed in contributing factors, with 'Inattention' becoming the leading factor in the current period.

23

9.5%was 21

Total Crash Events

0

Persons Killed

6

-14.3%was 7

Persons Injured

0

-100.0%was 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 · 2026-03-01 to 2026-03-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash incidents in Watertown showed an upward trend year-over-year, increasing by 9.5% from 21 crashes in March 2025 to 23 crashes in March 2026. Despite this rise in total crashes, the number of fatalities remained unchanged at zero for both periods.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

1

Pedestrians Injured

Prior: 3-66.7%

1

Cyclists Injured

Prior: 10.0%

4

Motorists Injured

Prior: 333.3%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes shifted significantly year-over-year. In March 2026, the peak day for crashes was Tuesday with 7 incidents, whereas in March 2025, Sunday had the highest count with 5 crashes. Similarly, the peak hour for crashes moved from 10 AM with 4 incidents in the prior period to 4 PM with 6 incidents in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity distributions saw some changes, although fatal crashes remained at zero in both periods. The number of minor injuries increased from 2 in March 2025 to 3 in March 2026, representing a rise from 9.5% to 13% of all crashes. Conversely, possible injuries decreased from 4 (19% of crashes) to 2 (8.7% of crashes) year-over-year.

Outcome by Severity (Crash Events)

Minor Injury3minor injury crashes13%
50.0%prior 2
Possible Injury2possible injury crashes8.7%
-50.0%prior 4
No Injury18no injury crashes78.3%
28.6%prior 14

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Most severe injury per crash record

Top Contributing Factors

The top contributing factors showed significant shifts year-over-year. 'Inattention' more than doubled, increasing from 3 crashes (14.3% share) in March 2025 to 6 crashes (26.1% share) in March 2026, becoming the leading factor. Conversely, 'Failed to yield right of way' saw a substantial decrease, dropping from 7 crashes (33.3% share) to only 1 crash (4.3% share) in the current period. Additionally, 'No improper driving' emerged as a prominent factor with 5 crashes (21.7% share) in the current period.

Officer-Reported Primary Contributing Cause

Inattention6 (26.1%)
No improper driving5 (21.7%)
Followed too closely2 (8.7%)
Disregarded traffic signs, signals, road markings2 (8.7%)
Failed to yield right of way1 (4.3%)-85.7%prior 7
Other improper action1 (4.3%)
Over-correcting/over-steering1 (4.3%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway1 (4.3%)
Visibility obstructed1 (4.3%)
Glare1 (4.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crash conditions showed some changes, particularly concerning winter weather. While crashes in clear and daylight conditions remained consistent year-over-year, March 2026 saw 3 crashes occurring under snowy or sleety weather conditions, which were not present in March 2025. Correspondingly, crashes on dry road surfaces decreased from 18 to 16, while 3 crashes occurred on slushy, snowy, or watery surfaces in the current period, compared to none in the prior period.

Weather

Clear15 (65.2%)
0.0%prior 15
Cloudy3 (13.0%)
Cloudy/Rain1 (4.3%)
Cloudy/Snow1 (4.3%)
Rain1 (4.3%)
Sleet, hail (freezing rain or drizzle)/Snow1 (4.3%)
Snow/Sleet, hail (freezing rain or drizzle)1 (4.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Weather condition at time of crash

Lighting

Daylight17 (73.9%)
0.0%prior 17
Dark - lighted roadway4 (17.4%)
Dusk2 (8.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Lighting condition field

Road Surface

Dry16 (69.6%)
-11.1%prior 18
Wet4 (17.4%)
Slush1 (4.3%)
Snow1 (4.3%)
Water (standing, moving)1 (4.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (42 vehicles)

1
TOYOTA12 (28.6%)
2
HONDA7 (16.7%)
3
CHEVROLET3 (7.1%)
4
MAZDA2 (4.8%)
5
LEXUS2 (4.8%)
6
KIA2 (4.8%)
7
FORD2 (4.8%)
-60.0%prior 5
8
MNNI1 (2.4%)
9
NISSAN1 (2.4%)
10
SUBARU1 (2.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Vehicle unit records

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

Sex Distribution (44 persons with recorded sex)

Male24 (54.5%)
-7.7%prior 26
Female20 (45.5%)
66.7%prior 12

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · Person-level records linked to crash events

Speed Limit Zones

The distribution of crashes across speed zones saw minor shifts, with no fatal crashes recorded in any zone for either period. Crashes in the 30 mph speed zone increased from 19 in March 2025 to 22 in March 2026. The prior period recorded 1 crash in a 20 mph zone and 1 crash in a 35 mph zone, neither of which had crashes in the current period, which instead reported 1 crash in a 25 mph zone.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-03-01 to 2026-03-31 · 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: 2026-03-01 through 2026-03-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2026-03-01 through 2026-03-31 (31 days)
  • Geographic scope: WATERTOWN, MA
  • Total crash records analyzed: 23
  • Total persons involved: 49
  • Total vehicles involved: 42

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). "WATERTOWN, MA Crash Intelligence Report: March 2026." Published June 21, 2026. Reporting period: 2026-03-01 to 2026-03-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/watertown/march-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

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Watertown, MA Crash Report — March 2026 | ThatCarHitMe.com