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YEAR-OVER-YEAR CRASH REPORT · LYNN, MA · SEPTEMBER 2023
Purpose: Machine-readable JSON endpoint for AI agents, LLMs, researchers, and programmatic consumers. Returns all underlying crash data and AI-generated commentary without HTML.
Authentication: None required. Public endpoint.
GET: https://thatcarhitme.com/api/crash-data/reports/data/massachusetts/lynn/september-2023-report
Monthly Traffic Safety Analysis
170 CRASHES IN
LYNN, MA
SEPTEMBER 2023
In September 2023, LYNN experienced 170 total crashes, a decrease from 183 crashes in September 2022. This represents a 7.1% reduction in total crashes year-over-year. The most notable shift was a 20% decrease in total injuries, falling from 80 to 64.
170
▼ -7.1%was 183
Total Crash Events
0
Persons Killed
64
▼ -20.0%was 80
Persons Injured
30
▼ -18.9%was 37
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 · 2023-09-01 to 2023-09-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend indicates a decrease in crash activity, with total crashes falling by 7.1% from 183 to 170. Total injuries also saw a significant reduction, decreasing by 20% from 80 to 64 year-over-year. Fatalities remained stable at zero in both periods.
30
Hit-and-Run Crashes — September 2023
▼ -18.9% vs prior (37)
Hit-and-run crashes decreased from 37 in the prior period to 30 in the current period. The hit-and-run crash rate also declined, moving from 20.2% of all crashes to 17.6% year-over-year. This indicates a downward trend in hit-and-run incidents.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
0
Other Killed
7
Pedestrians Injured
3
Cyclists Injured
52
Motorists Injured
2
Other Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · 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 Saturday, with 31 incidents in the prior period, to Sunday, with 29 incidents in the current period. Similarly, the peak crash hour moved from 1 PM (16 crashes) in the prior year to 4 PM (17 crashes) in the current year. These shifts suggest a change in the timing of peak crash occurrences.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While there were no fatalities in either period, serious injuries (code A) increased from 3 (1.6% of crashes) to 6 (3.5% of crashes). Minor injuries (code B) decreased from 54 (29.5% of crashes) to 34 (20% of crashes). Overall, crashes resulting in any injury decreased from 67 (36.6% of crashes) to 47 (27.6% of crashes).
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Most severe injury per crash record
Top Contributing Factors
The top contributing factor, "No improper driving," remained constant at 62 crashes in both periods, with its share of total crashes increasing from 33.9% to 36.5%. Factors like "Inattention" and "Other improper action" saw substantial count increases, rising from 5 to 10 crashes (100% increase) and 4 to 10 crashes (150% increase), respectively. Conversely, "Failed to yield right of way" decreased by 2 crashes, from 4 to 2.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in clear weather conditions decreased from 154 (Clear + Clear/Clear) in the prior period to 126 in the current period. Conversely, crashes during rainy conditions increased from 17 to 27. The number of crashes on wet road surfaces also increased from 26 to 39, while crashes in dark conditions decreased from 58 to 48.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes decreased from 353 in the prior period to 328 in the current period. Honda, Toyota, and Ford remained the top three vehicle makes involved, though their counts decreased across the board. Notably, the count of persons aged 0-15 involved in crashes increased from 34 to 64, while those aged 16-20 decreased from 55 to 34.
Top Vehicle Makes (328 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Vehicle unit records
64 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (422 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 25 mph zones decreased from 118 in the prior period to 87 in the current period. In contrast, crashes in 20 mph zones saw an increase from 7 to 24, and crashes in 10 mph zones rose from 3 to 8. No fatalities were recorded in any speed zone during either period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · 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: 2023-09-01 through 2023-09-30
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-09-01 through 2023-09-30 (30 days)
- Geographic scope: LYNN, MA
- Total crash records analyzed: 170
- Total persons involved: 475
- Total vehicles involved: 328
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 2023." Published June 21, 2026. Reporting period: 2023-09-01 to 2023-09-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/lynn/september-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
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly
Period: 2023-09-01 – 2023-09-30
Generated: June 21, 2026 · All rights reserved