Monthly Traffic Safety Analysis

191 CRASHES IN
LYNN, MA
JANUARY 2023

All metrics benchmarked againstJanuary 2022

Total crashes in LYNN for January 2023 were 191 compared to 179 in January 2022, representing a 6.7% increase. A notable shift was observed in pedestrian injuries, which increased by 8 persons (160%) from 5 to 13.

191

6.7%was 179

Total Crash Events

0

Persons Killed

64

18.5%was 54

Persons Injured

53

32.5%was 40

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash incidents in LYNN showed an upward trend, increasing from 179 crashes in January 2022 to 191 crashes in January 2023. This represents a 6.7% rise in total crashes year-over-year. Total injuries also increased by 10 persons, from 54 to 64.

53

Hit-and-Run Crashes — January 2023

32.5% vs prior (40)

Hit-and-run crashes increased from 40 in January 2022 to 53 in January 2023, representing a 32.5% increase. The hit-and-run rate also rose from 22.3% to 27.7% of all crashes year-over-year, 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%

13

Pedestrians Injured

Prior: 5160.0%

1

Cyclists Injured

Prior: 0%

50

Motorists Injured

Prior: 492.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · 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 with 36 crashes in January 2022 to Monday with 44 crashes in January 2023. The peak hour for crashes also changed from 2p to 5p, though both hours recorded 16 crashes. Crashes on Friday decreased by 14, while crashes on Monday increased by 14.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatal crashes remained at 0 in both January 2022 and January 2023. Serious injury crashes decreased from 4 in the prior period to 2 in the current period. Minor injury crashes increased from 33 to 50, while possible injury crashes decreased from 10 to 6.

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes1%
-50.0%prior 4
Minor Injury50minor injury crashes26.2%
51.5%prior 33
Possible Injury6possible injury crashes3.1%
-40.0%prior 10
No Injury116no injury crashes60.7%
-6.5%prior 124

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Most severe injury per crash record

Top Contributing Factors

The top contributing factor, "No improper driving," increased slightly from 56 crashes in January 2022 to 59 crashes in January 2023. Crashes attributed to "Driving too fast for conditions" decreased significantly from 7 to 2, while "Operating vehicle in erratic, reckless, careless, negligent or aggressive manner" doubled from 2 to 4 crashes.

Officer-Reported Primary Contributing Cause

No improper driving59 (30.9%)5.4%prior 56
Inattention4 (2.1%)-20.0%prior 5
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner4 (2.1%)
Fatigued/asleep3 (1.6%)
Failed to yield right of way2 (1%)
Other improper action2 (1%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway2 (1%)
Wrong side or wrong way2 (1%)
Failure to keep in proper lane or running off road2 (1%)
Distracted2 (1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

There was a shift in crash conditions from January 2022 to January 2023, with crashes on wet road surfaces increasing from 29 to 73. Correspondingly, crashes in rain increased from 5 to 27, while crashes on dry road surfaces decreased from 104 to 89. Crashes in snow conditions also decreased from 27 to 14.

Weather

Clear85 (45.2%)
-15.0%prior 100
Rain27 (14.4%)
440.0%prior 5
Cloudy16 (8.5%)
6.7%prior 15
Snow14 (7.4%)
-48.1%prior 27
Clear/Clear12 (6.4%)
-14.3%prior 14
Sleet, hail (freezing rain or drizzle)10 (5.3%)
Rain/Cloudy5 (2.7%)
Snow/Sleet, hail (freezing rain or drizzle)5 (2.7%)
Rain/Rain3 (1.6%)
Snow/Snow3 (1.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Weather condition at time of crash

Lighting

Dark - lighted roadway85 (45.2%)
4.9%prior 81
Daylight82 (43.6%)
-3.5%prior 85
Dusk10 (5.3%)
100.0%prior 5
Dark - roadway not lighted5 (2.7%)
Dark - unknown roadway lighting4 (2.1%)
Dawn2 (1.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Lighting condition field

Road Surface

Dry89 (47.3%)
-14.4%prior 104
Wet73 (38.8%)
151.7%prior 29
Snow14 (7.4%)
-54.8%prior 31
Ice11 (5.9%)
37.5%prior 8
Slush1 (0.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes increased from 346 to 357 year-over-year. Honda vehicles saw a notable increase in involvement from 65 to 88, making it the top make involved in crashes, surpassing Toyota which increased from 66 to 70. The number of persons aged 65 and older involved in crashes more than doubled from 12 to 25.

Top Vehicle Makes (357 vehicles)

1
HONDA88 (24.6%)
35.4%prior 65
2
TOYOTA70 (19.6%)
6.1%prior 66
3
FORD32 (9%)
-11.1%prior 36
4
CHEVROLET27 (7.6%)
80.0%prior 15
5
NISSAN19 (5.3%)
-42.4%prior 33
6
SUBARU11 (3.1%)
57.1%prior 7
7
HYUNDAI9 (2.5%)
-18.2%prior 11
8
ACURA9 (2.5%)
-25.0%prior 12
9
JEEP9 (2.5%)
-55.0%prior 20
10
MERCEDES-BENZ7 (2%)
16.7%prior 6

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Vehicle unit records

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

Sex Distribution (386 persons with recorded sex)

Male214 (55.4%)
-8.5%prior 234
Female172 (44.6%)
5.5%prior 163

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in the 25 mph speed zone increased from 107 in January 2022 to 127 in January 2023, an 18.7% increase. Conversely, crashes in the 30 mph speed zone decreased from 43 to 33, a 23.3% reduction. There were no fatal crashes reported in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · 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: 2023-01-01 through 2023-01-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-01-01 through 2023-01-31 (31 days)
  • Geographic scope: LYNN, MA
  • Total crash records analyzed: 191
  • Total persons involved: 581
  • Total vehicles involved: 357

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: January 2023." Published June 21, 2026. Reporting period: 2023-01-01 to 2023-01-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/lynn/january-2023-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 — January 2023 | ThatCarHitMe.com