ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · LYNNFIELD, MA · 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/lynnfield/2023-annual-report
Yearly Traffic Safety Analysis
237 CRASHES IN
LYNNFIELD, MA
2023
In Lynnfield, total crashes decreased from 295 in 2022 to 237 in 2023, a 19.7% reduction. While overall crashes and injuries declined, the most notable year-over-year shift was a 109% increase in hit-and-run incidents, which rose from 11 to 23.
237
▼ -19.7%was 295
Total Crash Events
1
Persons Killed
59
▼ -30.6%was 85
Persons Injured
23
▲ 109.1%was 11
Hit-and-Run Crashes
Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 5 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 2023-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Traffic crashes in Lynnfield showed a downward trend year-over-year, with total collisions falling by 19.7% from 295 to 237. The number of people injured in these incidents also decreased by 30.6%, from 85 in 2022 to 59 in 2023. The number of fatalities remained stable at one for both periods.
23
Hit-and-Run Crashes — 2023
▲ 109.1% vs prior (11)
Hit-and-run incidents increased significantly in both count and rate. The number of hit-and-run crashes more than doubled, rising from 11 in 2022 to 23 in 2023. This drove the hit-and-run rate up from 3.7% of all crashes in the prior year to 9.7% in the current year.
Vulnerable Road User Casualties
0
Pedestrians Killed
1
Motorists Killed
1
Pedestrians Injured
58
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · 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 years. In 2022, the peak day for crashes was Friday with 54 incidents, and the peak hour was 6 p.m. with 27 incidents. In 2023, the peak day moved to Sunday with 39 crashes, and the peak hour shifted earlier to 1 p.m., which saw 23 crashes.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The number of fatal crashes was unchanged at one in both 2022 and 2023, though the fatal crash rate per 100 crashes increased from 0.34 to 0.42 due to the lower total crash volume. The proportion of crashes resulting in an injury was relatively stable, at 22.4% in 2022 and 21.5% in 2023. Crashes resulting in no injury made up 77.3% of collisions in 2022 and 76.4% in 2023.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factors saw some shifts between periods. "Followed too closely" remained the top factor but decreased in count from 46 to 43. Crashes attributed to "Inattention" fell by 31.3%, from 32 to 22. Conversely, the count of crashes involving combined speeding-related factors ("Driving too fast for conditions" and "Exceeded authorized speed limit") doubled from 7 in 2022 to 14 in 2023.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Year-over-year, a larger proportion of crashes occurred in adverse conditions. The share of crashes on wet roads increased from 15.6% in 2022 to 22.8% in 2023. Similarly, crashes occurring during rain more than doubled as a share of the total, rising from 5.4% to 11.4%. The proportion of crashes in daylight decreased from 71.9% to 63.7%, while the share in dark, lighted conditions rose from 22.4% to 25.3%.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field
Vehicles & Demographics
The top three vehicle makes involved in crashes remained the same, but their ranking changed. In 2022, Toyota was the most frequent make with 98 vehicles, whereas in 2023, Honda took the top spot with 63 vehicles. The 26-34 age group represented the largest cohort of people involved in crashes in both years, with its count decreasing from 133 to 96, in line with the overall decline in collisions.
Top Vehicle Makes (449 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records
45 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (463 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events
Speed Limit Zones
Crash distribution across speed zones changed year-over-year. Crashes in 35 mph zones decreased significantly from 65 to 29. The 55 mph zone had the highest number of crashes in both periods, with a slight drop from 68 to 64 incidents. In both 2022 and 2023, the single fatal crash occurred in a 25 mph zone.
Fatal crashes by zone: 25 mph: 1 of 40 (2.5%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · 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 2023-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-01-01 through 2023-12-31 (365 days)
- Geographic scope: LYNNFIELD, MA
- Total crash records analyzed: 237
- Total persons involved: 525
- Total vehicles involved: 449
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). "LYNNFIELD, MA Crash Intelligence Report: 2023." Published June 21, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/lynnfield/2023-annual-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: 2023-01-01 – 2023-12-31
Generated: June 21, 2026 · All rights reserved