Monthly Traffic Safety Analysis

34 CRASHES IN
ARLINGTON, MA
MARCH 2025

All metrics benchmarked againstMarch 2024

Total crashes in ARLINGTON, MA increased by 17.24% year-over-year, rising from 29 in March 2024 to 34 in March 2025. This period also saw a significant 200% increase in total injuries, from 4 to 12. The most notable shift was in hit-and-run incidents, which increased from 1 to 5 crashes.

34

17.2%was 29

Total Crash Events

0

Persons Killed

12

200.0%was 4

Persons Injured

5

400.0%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. 4 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash activity in ARLINGTON, MA shows an upward trend, with total crashes increasing from 29 in March 2024 to 34 in March 2025, a 17.24% rise. Concurrently, total injuries saw a substantial increase, rising from 4 to 12, marking a 200% increase year-over-year.

5

Hit-and-Run Crashes — March 2025

400.0% vs prior (1)

Hit-and-run crashes increased significantly from 1 in March 2024 to 5 in March 2025, representing a 400% increase. The hit-and-run rate also rose from 3.4% to 14.7% of all crashes, indicating an upward trend in these incidents.

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: 0%

1

Cyclists Injured

Prior: 10.0%

10

Motorists Injured

Prior: 3233.3%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes shifted from Friday in March 2024 (9 crashes) to Monday in March 2025 (8 crashes). Similarly, the peak hour for crashes moved from 9 AM (5 crashes) in the prior period to 6 PM (7 crashes) in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There were no fatal crashes reported in either March 2024 or March 2025. However, total injuries increased from 4 to 12 year-over-year. Crashes resulting in minor injuries (Severity B) increased from 3 in March 2024 to 8 in March 2025, while crashes with possible injuries (Severity C) rose from 1 to 2.

Outcome by Severity (Crash Events)

Minor Injury8minor injury crashes23.5%
166.7%prior 3
Possible Injury2possible injury crashes5.9%
100.0%prior 1
No Injury20no injury crashes58.8%
-9.1%prior 22

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Most severe injury per crash record

Top Contributing Factors

Crashes attributed to 'Failed to yield right of way' increased from 7 in March 2024 to 8 in March 2025, a 14.3% increase in count. Conversely, crashes with 'No improper driving' as a factor decreased from 8 to 6, a 25% decrease in count. 'Followed too closely' incidents saw a 200% increase in count, rising from 1 to 3 crashes.

Officer-Reported Primary Contributing Cause

Failed to yield right of way8 (23.5%)14.3%prior 7
No improper driving6 (17.6%)-25.0%prior 8
Inattention3 (8.8%)
Followed too closely3 (8.8%)
Made an improper turn2 (5.9%)
Other improper action2 (5.9%)
Emotional1 (2.9%)
Visibility obstructed1 (2.9%)
Fatigued/asleep1 (2.9%)
Exceeded authorized speed limit1 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Rain' weather conditions increased from 2 in March 2024 to 6 in March 2025. Correspondingly, crashes on 'Wet' road surfaces rose from 6 to 10 during the same period. Conversely, crashes occurring during 'Daylight' conditions decreased from 24 to 21.

Weather

Clear18 (52.9%)
12.5%prior 16
Rain6 (17.6%)
Clear/Clear4 (11.8%)
-20.0%prior 5
Cloudy/Rain2 (5.9%)
Cloudy/Clear1 (2.9%)
Rain/Cloudy1 (2.9%)
Rain/Unknown1 (2.9%)
Sleet, hail (freezing rain or drizzle)1 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Weather condition at time of crash

Lighting

Daylight21 (63.6%)
-12.5%prior 24
Dark - lighted roadway4 (12.1%)
Dusk4 (12.1%)
Dark - unknown roadway lighting2 (6.1%)
Dawn2 (6.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Lighting condition field

Road Surface

Dry23 (67.6%)
4.5%prior 22
Wet10 (29.4%)
66.7%prior 6
Water (standing, moving)1 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes increased from 53 in March 2024 to 63 in March 2025. Toyota remained the top make involved, increasing from 9 to 12 vehicles, while Volkswagen saw a notable rise from 1 to 5 vehicles. Among persons involved, the 65+ age group increased from 10 to 16, and the 35-44 age group increased from 9 to 14.

Top Vehicle Makes (63 vehicles)

1
TOYOTA12 (19%)
33.3%prior 9
2
HONDA7 (11.1%)
0.0%prior 7
3
CHEVROLET6 (9.5%)
0.0%prior 6
4
VOLKSWAGEN5 (7.9%)
5
MERCEDES-BENZ4 (6.3%)
6
SUBARU3 (4.8%)
7
FORD3 (4.8%)
8
BMW2 (3.2%)
9
HYUNDAI2 (3.2%)
-60.0%prior 5
10
MAZDA2 (3.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Vehicle unit records

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

Sex Distribution (68 persons with recorded sex)

Female35 (51.5%)
25.0%prior 28
Male33 (48.5%)
-2.9%prior 34

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · Person-level records linked to crash events

Speed Limit Zones

The majority of crashes in both periods occurred in 25 mph speed zones, increasing from 18 crashes in March 2024 to 24 crashes in March 2025. Crashes in 30 mph zones decreased from 5 to 3. No fatal crashes were recorded in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-03-01 to 2025-03-31 · 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: 2025-03-01 through 2025-03-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2025-03-01 through 2025-03-31 (31 days)
  • Geographic scope: ARLINGTON, MA
  • Total crash records analyzed: 34
  • Total persons involved: 78
  • Total vehicles involved: 63

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). "ARLINGTON, MA Crash Intelligence Report: March 2025." Published June 21, 2026. Reporting period: 2025-03-01 to 2025-03-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/arlington/march-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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Arlington, MA Crash Report — March 2025 | ThatCarHitMe.com