Monthly Traffic Safety Analysis

141 CRASHES IN
LYNN, MA
MAY 2025

All metrics benchmarked againstMay 2024

In May 2025, Lynn, MA experienced a notable decrease in traffic incidents compared to May 2024. Total crashes fell from 183 to 141, marking a 22.95% reduction year-over-year. This period saw a general decline across various crash metrics, contributing to an overall safer month.

141

▼ -23.0%was 183

Total Crash Events

0

Persons Killed

57

▼ -26.9%was 78

Persons Injured

25

▼ -30.6%was 36

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crashes in Lynn, MA showed a significant downward trend year-over-year. The total number of crashes decreased by 42, from 183 in May 2024 to 141 in May 2025, representing a 22.95% reduction.

25

Hit-and-Run Crashes — May 2025

▼ -30.6% vs prior (36)

Hit-and-run crashes decreased from 36 in May 2024 to 25 in May 2025. Correspondingly, the hit-and-run rate decreased from 19.7% in the prior period to 17.7% in the current period, indicating a downward trend.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 0→0.0%

5

Pedestrians Injured

Prior: 6↓-16.7%

2

Cyclists Injured

Prior: 3↓-33.3%

50

Motorists Injured

Prior: 66↓-24.2%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes remained Friday in both periods, though the count decreased from 37 in May 2024 to 23 in May 2025. The peak crash hour shifted from 7 AM with 15 crashes in May 2024 to 5 PM with 18 crashes in May 2025.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

No fatal crashes were reported in either May 2024 or May 2025. Total injuries decreased from 78 in the prior period to 57 in the current period. Serious injuries (Severity A) saw a decrease from 2 to 1, while minor injuries (Severity B) decreased from 51 to 38.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes0.7%
↓-50.0%prior 2
Minor Injury38minor injury crashes27%
↓-25.5%prior 51
Possible Injury7possible injury crashes5%
→0.0%prior 7
No Injury86no injury crashes61%
↓-19.6%prior 107

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Most severe injury per crash record

Top Contributing Factors

The count of crashes attributed to 'No improper driving' decreased by 23, from 81 in May 2024 to 58 in May 2025. 'Failed to yield right of way' as a contributing factor saw a significant reduction from 10 crashes to 1 crash. Conversely, 'Other improper action' increased by 5 crashes, from 8 to 13 year-over-year.

Officer-Reported Primary Contributing Cause

No improper driving58 (41.1%)↓-28.4%prior 81
Other improper action13 (9.2%)↑62.5%prior 8
Inattention9 (6.4%)→0.0%prior 9
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner6 (4.3%)↓-40.0%prior 10
Failure to keep in proper lane or running off road6 (4.3%)
Disregarded traffic signs, signals, road markings4 (2.8%)
Over-correcting/over-steering4 (2.8%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway2 (1.4%)
Glare2 (1.4%)
Distracted1 (0.7%)↓-80.0%prior 5

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear' weather conditions decreased from 132 in May 2024 to 92 in May 2025. Incidents during 'Daylight' conditions also decreased from 122 to 95. Crashes on 'Dry' road surfaces decreased from 156 to 112, while crashes on 'Wet' road surfaces remained relatively stable, increasing slightly from 26 to 28.

Weather

Clear92 (65.2%)
↓-30.3%prior 132
Clear/Clear13 (9.2%)
↓-7.1%prior 14
Rain12 (8.5%)
↓-33.3%prior 18
Cloudy9 (6.4%)
↓-25.0%prior 12
Rain/Rain4 (2.8%)
Rain/Cloudy4 (2.8%)
Cloudy/Cloudy2 (1.4%)
Rain/Sleet, hail (freezing rain or drizzle)1 (0.7%)
Clear/Cloudy1 (0.7%)
Cloudy/Clear1 (0.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Weather condition at time of crash

Lighting

Daylight95 (67.4%)
↓-22.1%prior 122
Dark - lighted roadway40 (28.4%)
↓-21.6%prior 51
Dark - roadway not lighted4 (2.8%)
Dusk2 (1.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Lighting condition field

Road Surface

Dry112 (79.4%)
↓-28.2%prior 156
Wet28 (19.9%)
↑7.7%prior 26
Sand, mud, dirt, oil, gravel1 (0.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Road surface condition field

Vehicles & Demographics

The total number of persons involved in crashes decreased from 471 in May 2024 to 368 in May 2025. The 21-25 age group saw a decrease from 61 to 46 persons, and the 26-34 age group decreased from 73 to 60 persons. For vehicle makes, Honda crashes decreased by 23 (from 84 to 61) and Toyota crashes decreased by 6 (from 66 to 60).

Top Vehicle Makes (297 vehicles)

1
HONDA61 (20.5%)
↓-27.4%prior 84
2
TOYOTA60 (20.2%)
↓-9.1%prior 66
3
FORD27 (9.1%)
↓-22.9%prior 35
4
CHEVROLET13 (4.4%)
↓-58.1%prior 31
5
JEEP13 (4.4%)
↓-18.8%prior 16
6
SUBARU11 (3.7%)
↑120.0%prior 5
7
NISSAN11 (3.7%)
↓-52.2%prior 23
8
HYUNDAI10 (3.4%)
→0.0%prior 10
9
BMW6 (2%)
↓-14.3%prior 7
10
AUDI6 (2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Vehicle unit records

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

Sex Distribution (299 persons with recorded sex)

Male175 (58.5%)
↓-32.2%prior 258
Female124 (41.5%)
↓-4.6%prior 130

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in the 25 mph speed zone decreased from 111 in May 2024 to 85 in May 2025. Similarly, crashes in the 30 mph zone decreased from 30 to 26. However, crashes in the 35 mph zone saw a slight increase from 12 to 14 year-over-year.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-05-01 to 2025-05-31 · 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: 2025-05-01 through 2025-05-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2025-05-01 through 2025-05-31 (31 days)
  • Geographic scope: LYNN, MA
  • Total crash records analyzed: 141
  • Total persons involved: 368
  • Total vehicles involved: 297

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). "LYNN, MA Crash Intelligence Report: May 2025." Published June 21, 2026. Reporting period: 2025-05-01 to 2025-05-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/lynn/may-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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Lynn, MA Crash Report — May 2025 | ThatCarHitMe.com