Monthly Traffic Safety Analysis

183 CRASHES IN
LYNN, MA
SEPTEMBER 2022

All metrics benchmarked againstSeptember 2021

Total crashes in Lynn remained stable at 183 in September 2022, unchanged from September 2021. However, total injuries increased by 26.98%, rising from 63 to 80. The most notable shift was a 100% increase in bicycle crashes, which rose from 2 to 4 year-over-year.

183

Total Crash Events

0

Persons Killed

80

27.0%was 63

Persons Injured

37

-17.8%was 45

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The total number of crashes remained stable year-over-year, with 183 crashes reported in both September 2022 and September 2021. Despite this stability in crash count, the number of total injuries increased by 26.98%, from 63 to 80.

37

Hit-and-Run Crashes — September 2022

-17.8% vs prior (45)

Hit-and-run crashes decreased by 17.78%, from 45 in September 2021 to 37 in September 2022. Correspondingly, the hit-and-run rate decreased from 24.6% to 20.2%, indicating a downward trend for this period.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

0

Other Killed

Prior: 00.0%

7

Pedestrians Injured

Prior: 8-12.5%

5

Cyclists Injured

Prior: 2150.0%

66

Motorists Injured

Prior: 5324.5%

2

Other Injured

Prior: 0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · 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 Monday with 32 crashes in September 2021 to Saturday with 31 crashes in September 2022. Similarly, the peak hour for crashes moved from 3 PM with 18 crashes in the prior period to 1 PM with 16 crashes in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There were no fatal crashes in either September 2021 or September 2022. Serious injuries decreased from 4 (2.2% of crashes) to 3 (1.6% of crashes) year-over-year. Minor injuries significantly increased from 27 (14.8% of crashes) to 54 (29.5% of crashes), while possible injuries decreased from 17 (9.3% of crashes) to 10 (5.5% of crashes).

Outcome by Severity (Crash Events)

Serious Injury3serious injury crashes1.6%
-25.0%prior 4
Minor Injury54minor injury crashes29.5%
100.0%prior 27
Possible Injury10possible injury crashes5.5%
-41.2%prior 17
No Injury106no injury crashes57.9%
-11.7%prior 120

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factor, "No improper driving," increased by 26.53% from 49 crashes to 62 crashes, with its share rising from 26.8% to 33.9%. Conversely, "Inattention" crashes decreased by 54.55% from 11 to 5, and "Operating vehicle in erratic, reckless, careless, negligent or aggressive manner" decreased by 55.56% from 9 to 4 crashes.

Officer-Reported Primary Contributing Cause

No improper driving62 (33.9%)26.5%prior 49
Inattention5 (2.7%)-54.5%prior 11
Failed to yield right of way4 (2.2%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner4 (2.2%)-55.6%prior 9
Other improper action4 (2.2%)-33.3%prior 6
Physical impairment2 (1.1%)
Fatigued/asleep2 (1.1%)
Failure to keep in proper lane or running off road2 (1.1%)
Visibility obstructed2 (1.1%)
Over-correcting/over-steering1 (0.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear' weather conditions increased from 116 to 145, a 25% rise. Concurrently, crashes in 'Rain' conditions decreased from 12 to 10, and 'Cloudy' conditions decreased from 17 to 9. The number of crashes in 'Daylight' conditions remained constant at 119 for both periods.

Weather

Clear145 (79.2%)
25.0%prior 116
Rain10 (5.5%)
-16.7%prior 12
Clear/Clear9 (4.9%)
-70.0%prior 30
Cloudy9 (4.9%)
-47.1%prior 17
Cloudy/Rain3 (1.6%)
Rain/Cloudy3 (1.6%)
Clear/Other1 (0.5%)
Sleet, hail (freezing rain or drizzle)1 (0.5%)
Rain/Rain1 (0.5%)
Cloudy/Clear1 (0.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Weather condition at time of crash

Lighting

Daylight119 (65.0%)
0.0%prior 119
Dark - lighted roadway54 (29.5%)
3.8%prior 52
Dusk6 (3.3%)
0.0%prior 6
Dark - roadway not lighted2 (1.1%)
Dark - unknown roadway lighting2 (1.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Lighting condition field

Road Surface

Dry156 (85.2%)
-1.3%prior 158
Wet26 (14.2%)
4.0%prior 25
Other1 (0.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes decreased slightly from 365 to 353. Honda and Toyota, the top two vehicle makes, both saw decreases in involvement, from 81 to 72 and 72 to 67 respectively. The 0-15 age group saw a significant increase in persons involved, rising by 70% from 20 to 34, while the 65+ age group increased by 40% from 25 to 35.

Top Vehicle Makes (353 vehicles)

1
HONDA72 (20.4%)
-11.1%prior 81
2
TOYOTA67 (19%)
-6.9%prior 72
3
FORD42 (11.9%)
16.7%prior 36
4
NISSAN27 (7.6%)
92.9%prior 14
5
CHEVROLET23 (6.5%)
-25.8%prior 31
6
JEEP18 (5.1%)
50.0%prior 12
7
GMC13 (3.7%)
8
BMW10 (2.8%)
66.7%prior 6
9
HYUNDAI7 (2%)
-36.4%prior 11
10
KIA7 (2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Vehicle unit records

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

Sex Distribution (402 persons with recorded sex)

Male237 (59.0%)
0.0%prior 237
Female165 (41.0%)
4.4%prior 158

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes in the 25 mph speed zone increased by 14.56%, from 103 to 118. Conversely, crashes in the 30 mph zone decreased by 17.02%, from 47 to 39. There were no fatal crashes reported in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · 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: 2022-09-01 through 2022-09-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2022-09-01 through 2022-09-30 (30 days)
  • Geographic scope: LYNN, MA
  • Total crash records analyzed: 183
  • Total persons involved: 474
  • Total vehicles involved: 353

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: September 2022." Published June 21, 2026. Reporting period: 2022-09-01 to 2022-09-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/lynn/september-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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Lynn, MA Crash Report — September 2022 | ThatCarHitMe.com