Monthly Traffic Safety Analysis

91 CRASHES IN
MEDFORD, MA
MAY 2022

All metrics benchmarked againstMay 2021

MEDFORD experienced a notable decrease in traffic incidents in May 2022 compared to May 2021, with total crashes falling from 128 to 91, representing a 28.9% reduction. This decline was accompanied by a significant 31.3% decrease in total injuries, from 32 to 22. The most significant year-over-year shift was the overall reduction in crash frequency and associated injuries.

91

-28.9%was 128

Total Crash Events

0

Persons Killed

22

-31.3%was 32

Persons Injured

17

-32.0%was 25

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 · 2022-05-01 to 2022-05-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash data for MEDFORD indicates a downward trend year-over-year, with total crashes decreasing by 28.9% from 128 in May 2021 to 91 in May 2022. Concurrently, the total number of injuries also fell by 31.3%, from 32 to 22, signaling a general improvement in traffic safety outcomes for the period.

17

Hit-and-Run Crashes — May 2022

-32.0% vs prior (25)

The number of hit-and-run crashes decreased from 25 in May 2021 to 17 in May 2022. The hit-and-run rate also saw a slight decrease, moving from 19.5% of total crashes in May 2021 to 18.7% in May 2022.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

22

Motorists Injured

Prior: 30-26.7%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-05-01 to 2022-05-31 · 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 between the two periods. In May 2021, the peak day for crashes was Saturday with 26 incidents, but in May 2022, Tuesday became the peak day with 19 crashes. The peak hour also shifted from 2p with 14 crashes in May 2021 to 3p with 11 crashes in May 2022.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-05-01 to 2022-05-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-05-01 to 2022-05-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There were no fatal crashes or fatalities in either May 2021 or May 2022. Injury crash proportions saw a decrease, with 25 injury crashes (1 serious, 12 minor, 12 possible) in May 2021 representing 19.5% of total crashes, compared to 15 injury crashes (8 minor, 7 possible) in May 2022, representing 16.5% of total crashes. Specifically, minor injuries decreased from 12 to 8, and possible injuries decreased from 12 to 7.

Outcome by Severity (Crash Events)

Minor Injury8minor injury crashes8.8%
-33.3%prior 12
Possible Injury7possible injury crashes7.7%
-41.7%prior 12
No Injury70no injury crashes76.9%
-24.7%prior 93

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-05-01 to 2022-05-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-05-01 to 2022-05-31 · Most severe injury per crash record

Top Contributing Factors

The top contributing factors saw shifts in counts and rankings year-over-year. 'Followed too closely' decreased significantly from 20 crashes in May 2021 to 12 crashes in May 2022, a 40% reduction in count. 'No improper driving' increased from 16 crashes to 19 crashes, while 'Failed to yield right of way' decreased from 16 to 13 crashes. The factor 'No improper driving' moved from the second most common factor to the most common factor.

Officer-Reported Primary Contributing Cause

No improper driving19 (20.9%)18.8%prior 16
Failed to yield right of way13 (14.3%)-18.8%prior 16
Followed too closely12 (13.2%)-40.0%prior 20
Other improper action8 (8.8%)-38.5%prior 13
Inattention6 (6.6%)-45.5%prior 11
Failure to keep in proper lane or running off road4 (4.4%)-42.9%prior 7
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner3 (3.3%)
Made an improper turn2 (2.2%)
Physical impairment2 (2.2%)
Fatigued/asleep1 (1.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-05-01 to 2022-05-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring under clear weather conditions decreased from 94 in May 2021 to 65 in May 2022, while crashes during rain decreased from 14 to 3. Similarly, crashes on dry road surfaces decreased from 103 to 82, and on wet surfaces from 20 to 8. The proportion of crashes on wet road surfaces decreased from 15.6% in May 2021 to 8.8% in May 2022.

Weather

Clear65 (73.0%)
-30.9%prior 94
Clear/Clear11 (12.4%)
120.0%prior 5
Cloudy8 (9.0%)
33.3%prior 6
Rain3 (3.4%)
-78.6%prior 14
Cloudy/Clear1 (1.1%)
Sleet, hail (freezing rain or drizzle)/Cloudy1 (1.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-05-01 to 2022-05-31 · Weather condition at time of crash

Lighting

Daylight67 (75.3%)
-31.6%prior 98
Dark - lighted roadway18 (20.2%)
-14.3%prior 21
Dusk3 (3.4%)
Dark - roadway not lighted1 (1.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-05-01 to 2022-05-31 · Lighting condition field

Road Surface

Dry82 (91.1%)
-20.4%prior 103
Wet8 (8.9%)
-60.0%prior 20

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-05-01 to 2022-05-31 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes decreased from 267 in May 2021 to 183 in May 2022, a reduction of 84 vehicles. While the top vehicle makes remained similar, the counts for most top makes decreased; for instance, TOYOTA decreased from 45 to 33, and FORD from 27 to 15. Information regarding vehicle types was not available for either period.

Top Vehicle Makes (183 vehicles)

1
TOYOTA33 (18%)
-26.7%prior 45
2
HONDA27 (14.8%)
-12.9%prior 31
3
FORD15 (8.2%)
-44.4%prior 27
4
NISSAN13 (7.1%)
-7.1%prior 14
5
SUBARU8 (4.4%)
-38.5%prior 13
6
JEEP8 (4.4%)
-38.5%prior 13
7
CHEVROLET7 (3.8%)
-68.2%prior 22
8
MERCEDES-BENZ7 (3.8%)
9
MAZDA5 (2.7%)
-16.7%prior 6
10
GMC4 (2.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-05-01 to 2022-05-31 · Vehicle unit records

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

Sex Distribution (186 persons with recorded sex)

Male121 (65.1%)
1.7%prior 119
Female65 (34.9%)
-33.7%prior 98

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-05-01 to 2022-05-31 · Person-level records linked to crash events

Speed Limit Zones

The majority of crashes in both periods occurred in the 25 mph speed zone, decreasing from 68 crashes in May 2021 to 49 crashes in May 2022. Crashes in the 35 mph zone also decreased from 29 to 15. Notably, crashes in the 55 mph zone increased from 8 in May 2021 to 11 in May 2022, indicating a slight shift towards higher speed zones for a portion of incidents.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-05-01 to 2022-05-31 · 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: 2022-05-01 through 2022-05-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2022-05-01 through 2022-05-31 (31 days)
  • Geographic scope: MEDFORD, MA
  • Total crash records analyzed: 91
  • Total persons involved: 219
  • Total vehicles involved: 183

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: May 2022." Published June 21, 2026. Reporting period: 2022-05-01 to 2022-05-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/medford/may-2022-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 — May 2022 | ThatCarHitMe.com