Monthly Traffic Safety Analysis

96 CRASHES IN
MEDFORD, MA
FEBRUARY 2024

All metrics benchmarked againstFebruary 2023

In February 2024, MEDFORD experienced 96 crashes, a decrease of 5.9% compared to the 102 crashes recorded in February 2023. A significant shift was observed in total injuries, which surged by 161.5% from 13 to 34 injured persons year-over-year, despite fewer overall crashes.

96

-5.9%was 102

Total Crash Events

0

Persons Killed

34

161.5%was 13

Persons Injured

14

-17.6%was 17

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, total crashes in MEDFORD saw a slight decrease of 5.9%, from 102 in February 2023 to 96 in February 2024. However, this period was marked by a substantial increase in total injuries, rising by 161.5% from 13 to 34 injured persons.

14

Hit-and-Run Crashes — February 2024

-17.6% vs prior (17)

Hit-and-run crashes decreased from 17 in February 2023 to 14 in February 2024. Correspondingly, the hit-and-run rate declined from 16.7% to 14.6% year-over-year, indicating a downward trend for this type of incident.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

3

Pedestrians Injured

Prior: 30.0%

2

Cyclists Injured

Prior: 0%

29

Motorists Injured

Prior: 10190.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · 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 February 2023 (26 crashes) to Thursday in February 2024 (20 crashes). While the number of crashes during the peak hour remained at 13, the peak hour itself shifted from 4 PM in February 2023 to 5 PM in February 2024.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatal crashes and fatalities remained at zero for both February 2023 and February 2024. However, the proportion of crashes resulting in any injury (Serious, Minor, or Possible) significantly increased from 12.7% (13 out of 102 crashes) in February 2023 to 29.2% (28 out of 96 crashes) in February 2024. Specifically, minor injuries rose from 11 to 20, and possible injuries increased from 1 to 7 year-over-year.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes1%
0.0%prior 1
Minor Injury20minor injury crashes20.8%
81.8%prior 11
Possible Injury7possible injury crashes7.3%
600.0%prior 1
No Injury62no injury crashes64.6%
-20.5%prior 78

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factor, 'No improper driving,' decreased by 25% in count, from 28 in February 2023 to 21 in February 2024. Conversely, 'Followed too closely' increased by 40% in count, rising from 10 to 14, and 'Failed to yield right of way' increased by 55.6% in count, from 9 to 14. These shifts resulted in 'Followed too closely' and 'Failed to yield right of way' moving up to become the second most frequent factors in February 2024.

Officer-Reported Primary Contributing Cause

No improper driving21 (21.9%)-25.0%prior 28
Followed too closely14 (14.6%)40.0%prior 10
Failed to yield right of way14 (14.6%)55.6%prior 9
Other improper action5 (5.2%)
Failure to keep in proper lane or running off road5 (5.2%)
Inattention4 (4.2%)-33.3%prior 6
Distracted4 (4.2%)
Disregarded traffic signs, signals, road markings3 (3.1%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner3 (3.1%)
Exceeded authorized speed limit2 (2.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear' weather conditions slightly increased from 69 to 73 year-over-year, while crashes on 'Wet' road surfaces decreased from 13 to 8. Crashes during 'Daylight' conditions decreased from 64 to 54, and crashes in 'Dark - lighted roadway' conditions increased from 29 to 35. The prior period also reported crashes in 'Snow', 'Sleet, hail', 'Ice', and 'Slush' conditions, which were not observed in the current period.

Weather

Clear73 (77.7%)
5.8%prior 69
Cloudy7 (7.4%)
-12.5%prior 8
Clear/Clear5 (5.3%)
Clear/Other3 (3.2%)
Cloudy/Cloudy2 (2.1%)
Cloudy/Rain1 (1.1%)
Rain1 (1.1%)
Unknown/Unknown1 (1.1%)
Clear/Unknown1 (1.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Weather condition at time of crash

Lighting

Daylight54 (57.4%)
-15.6%prior 64
Dark - lighted roadway35 (37.2%)
20.7%prior 29
Dawn2 (2.1%)
Dusk2 (2.1%)
Dark - roadway not lighted1 (1.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Lighting condition field

Road Surface

Dry85 (91.4%)
7.6%prior 79
Wet8 (8.6%)
-38.5%prior 13

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes remained relatively stable, decreasing slightly from 197 to 195. Toyota remained the most common vehicle make, though its count decreased from 36 to 35, while Honda saw an increase from 27 to 33. Regarding persons involved, the 55-64 age group saw a notable increase from 20 to 33, and the 65+ age group also rose from 13 to 22 year-over-year.

Top Vehicle Makes (195 vehicles)

1
TOYOTA35 (17.9%)
-2.8%prior 36
2
HONDA33 (16.9%)
22.2%prior 27
3
NISSAN20 (10.3%)
100.0%prior 10
4
FORD15 (7.7%)
-37.5%prior 24
5
CHEVROLET11 (5.6%)
-8.3%prior 12
6
SUBARU7 (3.6%)
-22.2%prior 9
7
HYUNDAI7 (3.6%)
-41.7%prior 12
8
DODGE6 (3.1%)
9
JEEP6 (3.1%)
-40.0%prior 10
10
MERCEDES-BENZ6 (3.1%)
-14.3%prior 7

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Vehicle unit records

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

Sex Distribution (202 persons with recorded sex)

Male114 (56.4%)
0.9%prior 113
Female88 (43.6%)
4.8%prior 84

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 25 mph zones decreased from 67 in February 2023 to 58 in February 2024, and crashes in 30 mph zones saw a significant drop from 10 to 2. Conversely, crashes in 35 mph zones increased substantially from 9 to 26 year-over-year. There were no fatal crashes recorded in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · 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: 2024-02-01 through 2024-02-29
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2024-02-01 through 2024-02-29 (29 days)
  • Geographic scope: MEDFORD, MA
  • Total crash records analyzed: 96
  • Total persons involved: 235
  • Total vehicles involved: 195

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). "MEDFORD, MA Crash Intelligence Report: February 2024." Published June 21, 2026. Reporting period: 2024-02-01 to 2024-02-29. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/medford/february-2024-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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Medford, MA Crash Report — February 2024 | ThatCarHitMe.com