ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · LEE, MA · 2022
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/lee/2022-annual-report
Yearly Traffic Safety Analysis
194 CRASHES IN
LEE, MA
2022
In Lee, MA, total traffic crashes increased by 16.2% from 167 in 2021 to 194 in 2022. While fatalities remained at zero for both years, the number of injuries rose from 41 to 50. The most notable year-over-year shift was the emergence of serious injury crashes, with four recorded in 2022 after none were reported in the prior year.
194
▲ 16.2%was 167
Total Crash Events
0
Persons Killed
50
▲ 22.0%was 41
Persons Injured
11
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. 7 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, traffic collisions in Lee are on an upward trend. Total crashes rose from 167 in 2021 to 194 in 2022, a 16.2% year-over-year increase. Similarly, the number of people injured in these incidents increased by 22.0%, from 41 in the prior year to 50 in the current year, while fatalities remained at zero in both periods.
11
Hit-and-Run Crashes — 2022
▼ 0.0% vs prior (11)
The absolute number of hit-and-run incidents in Lee remained unchanged, with 11 crashes reported in both 2021 and 2022. However, due to the 16.2% increase in total crashes year-over-year, the hit-and-run rate as a proportion of all crashes decreased. The rate trended downward from 6.6% in 2021 to 5.7% in 2022.
Vulnerable Road User Casualties
0
Motorists Killed
50
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes showed some shifts between the two periods. While Saturday remained the peak day for crashes in both 2021 (34 crashes) and 2022 (35 crashes), the peak hour of the day changed. In 2021, the most crashes occurred at 3 p.m. (18 crashes), whereas in 2022 the peak shifted to 12 p.m. (18 crashes).
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Crash severity worsened year-over-year, despite fatal crashes remaining at zero for both periods. In 2022, four crashes resulted in a serious injury, accounting for 2.1% of all incidents; no serious injury crashes were recorded in 2021. The number of minor injury crashes held steady at 22 for both years, though their share of total crashes decreased from 13.2% to 11.3% due to the overall increase in collisions.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Most severe injury per crash record
Top Contributing Factors
The ranking of the top three contributing factors remained consistent year-over-year, though their counts changed. Crashes with 'No improper driving' cited as a factor increased from 37 to 50, and 'Inattention' related crashes rose slightly from 31 to 33. A notable increase was observed in crashes attributed to 'Failure to keep in proper lane or running off road,' which grew by 50% from 8 incidents in 2021 to 12 in 2022.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes on adverse road surfaces saw a significant increase year-over-year. While collisions on dry roads were most common in both periods, the number of crashes on wet, snowy, icy, or slush-covered roads increased by 37.8%, from 45 incidents in 2021 to 62 in 2022. The proportion of crashes occurring in daylight was stable at approximately 68% for both years, and clear weather was the dominant condition in both periods.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Road surface condition field
Vehicles & Demographics
Vehicle and person demographics saw notable shifts between 2021 and 2022. While Toyota remained the most common vehicle make involved in crashes in both years, Subaru's involvement nearly doubled from 16 vehicles in 2021 to 31 in 2022, moving it into the top three makes. The number of people aged 65 and older involved in crashes increased by 63.8%, from 47 individuals in 2021 to 77 in 2022, making this the most represented age group in the current period.
Top Vehicle Makes (305 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records
36 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (363 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across speed zones remained relatively consistent, with 25 mph zones accounting for the most crashes in both 2021 (44 crashes) and 2022 (48 crashes). Crashes in 65 mph zones were also frequent in both years, with 39 and 40 incidents respectively. The most significant change occurred in 35 mph zones, where the number of crashes increased by 66.7% from 18 in 2021 to 30 in 2022. No fatal crashes were recorded in any speed zone in either year.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · 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: 2022-01-01 through 2022-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-01-01 through 2022-12-31 (365 days)
- Geographic scope: LEE, MA
- Total crash records analyzed: 194
- Total persons involved: 397
- Total vehicles involved: 305
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). "LEE, MA Crash Intelligence Report: 2022." Published June 21, 2026. Reporting period: 2022-01-01 to 2022-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/lee/2022-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: 2022-01-01 – 2022-12-31
Generated: June 21, 2026 · All rights reserved