ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · LEE, MA · 2025
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/2025-annual-report
Yearly Traffic Safety Analysis
168 CRASHES IN
LEE, MA
2025
In 2025, Lee recorded 168 total traffic crashes, a 10.2% decrease from the 187 crashes documented in 2024. While the total number of crashes fell, reported injuries increased slightly from 44 to 46, and there were no fatalities in either period. The most significant shift in contributing factors was the emergence of 'Inattention' as the leading cause with 38 crashes in 2025, overtaking 'No improper driving' which was the top category in the prior year with 58 crashes.
168
▼ -10.2%was 187
Total Crash Events
0
Persons Killed
46
▲ 4.5%was 44
Persons Injured
9
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. 6 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, traffic crashes in Lee show a downward trend, decreasing by 10.2% from 187 in 2024 to 168 in 2025. Despite the drop in total incidents, the number of people injured saw a slight increase of 4.5%, rising from 44 to 46. Fatalities remained at zero for both years.
9
Hit-and-Run Crashes — 2025
▼ 0.0% vs prior (9)
The number of hit-and-run crashes remained unchanged, with 9 incidents recorded in both 2025 and 2024. However, due to the overall decrease in total crashes in 2025, the hit-and-run rate, as a percentage of all crashes, increased from 4.8% to 5.4%.
Vulnerable Road User Casualties
0
Motorists Killed
0
Other Killed
45
Motorists Injured
1
Other Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · 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 year-over-year. The day with the most crashes moved from Saturday (35 incidents) in 2024 to Thursday (31 incidents) in 2025. Similarly, the peak hour for crashes changed from the 4 p.m. hour in the prior year (19 crashes) to the 10 a.m. hour in the current year (17 crashes).
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
There were no fatal crashes recorded in either 2025 or 2024. However, the number of serious injury crashes increased from 1 incident (0.5% of total crashes) in 2024 to 5 incidents (3.0% of total crashes) in 2025. The count of minor injury crashes was unchanged at 23 for both periods, though its share of total crashes rose from 12.3% to 13.7% due to the lower overall crash volume in 2025.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record
Top Contributing Factors
The ranking of top contributing factors changed significantly between the two years. In 2025, 'Inattention' was the most cited factor with 38 crashes, an increase from 36 crashes in 2024. This displaced 'No improper driving,' which saw its count drop from 58 crashes in 2024 to 31 in 2025. Other notable changes include an increase in crashes attributed to 'Failure to keep in proper lane or running off road' (from 13 to 19) and 'Failed to yield right of way' (from 14 to 19).
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The distribution of crashes across lighting and road surface conditions remained broadly similar year-over-year, with most incidents occurring in daylight (66.7% in 2025 vs. 67.9% in 2024) and on dry roads (69.0% vs. 67.4%). A notable shift occurred in weather conditions, where the count of crashes during snow doubled from 7 in 2024 to 14 in 2025. Crashes in clear weather decreased from 110 to 94.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field
Vehicles & Demographics
The top vehicle makes involved in crashes saw a slight change in ranking. In 2025, Toyota was the most common make with 38 vehicles, followed by Ford with 29; in the prior year, Ford and Toyota were tied for first with 34 vehicles each. Regarding the age of persons involved, the 65+ age group's representation increased from 54 individuals (14.2% of total persons) in 2024 to 64 individuals (19.0% of total persons) in 2025, becoming the largest single age cohort.
Top Vehicle Makes (269 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records
33 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (297 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across different speed zones changed notably year-over-year. Crashes in 65 mph zones decreased significantly, from 43 incidents in 2024 to 23 in 2025. Conversely, crashes in 50 mph zones increased from 3 to 12. Incidents in 25 mph zones (49 crashes) and 30 mph zones (29 crashes) remained relatively stable compared to the prior year's counts of 52 and 32, respectively. No fatal crashes were recorded in any speed zone during either period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · 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: 2025-01-01 through 2025-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2025-01-01 through 2025-12-31 (365 days)
- Geographic scope: LEE, MA
- Total crash records analyzed: 168
- Total persons involved: 336
- Total vehicles involved: 269
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: 2025." Published June 21, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/lee/2025-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: 2025-01-01 – 2025-12-31
Generated: June 21, 2026 · All rights reserved