ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · LEXINGTON, MA · AUGUST 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/lexington/august-2025-report
Monthly Traffic Safety Analysis
43 CRASHES IN
LEXINGTON, MA
AUGUST 2025
In August 2025, LEXINGTON experienced 43 crashes, a decrease from 47 crashes in August 2024, representing an 8.51% reduction. Total injuries also decreased by 20%, from 15 to 12. The most notable shift was the increase in pedestrian crashes, rising from 0 in the prior period to 3 in the current period.
43
▼ -8.5%was 47
Total Crash Events
0
Persons Killed
12
▼ -20.0%was 15
Persons Injured
5
▼ -28.6%was 7
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. 2 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, crash data for LEXINGTON shows a downward trend year-over-year. Total crashes decreased by 8.51%, from 47 in August 2024 to 43 in August 2025. Similarly, total injuries saw a 20% reduction, falling from 15 to 12.
5
Hit-and-Run Crashes — August 2025
▼ -28.6% vs prior (7)
Hit-and-run crashes decreased by 28.57% year-over-year, falling from 7 in August 2024 to 5 in August 2025. The hit-and-run rate also saw a decrease, moving from 14.9% in the prior period to 11.6% in the current period.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
3
Pedestrians Injured
1
Cyclists Injured
8
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · 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 Sunday in August 2024 (11 crashes) to Thursday in August 2025 (9 crashes). Friday crashes saw a significant increase, rising from 1 in the prior period to 8 in the current period. The peak hour for crashes shifted from 5 PM (6 crashes) in the prior period to 3 PM (8 crashes) in the current period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
There were no fatal crashes in either period. Serious injuries (Severity A) increased from 1 in August 2024 to 2 in August 2025. Possible injuries (Severity C) decreased from 5 to 3, while minor injuries (Severity B) remained constant at 5.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · Most severe injury per crash record
Top Contributing Factors
Contributing factors saw notable changes year-over-year. Crashes attributed to 'Followed too closely' decreased by 41.67% in count, from 12 to 7. Conversely, 'No improper driving' and 'Failure to keep in proper lane or running off road' both increased by 300% in count, rising from 2 to 8 each.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Adverse weather conditions, such as rain and cloudy weather, were associated with a lower proportion of crashes in August 2025 (9.3% of crashes) compared to August 2024 (31.9% of crashes). Crashes on wet road surfaces also decreased significantly, accounting for 7% of crashes in the current period versus 31.9% in the prior period. The proportion of crashes occurring in daylight conditions increased slightly from 80.9% to 88.4%.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes decreased by 9.09%, from 88 in August 2024 to 80 in August 2025. Honda vehicles saw a decrease in involvement, from 18 to 8, while Toyota involvement slightly decreased from 17 to 16. In terms of persons involved, individuals aged 55-64 and 65+ both saw an increase in representation, rising from 6 to 12 and 7 to 12 respectively.
Top Vehicle Makes (80 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · Vehicle unit records
11 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (89 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes occurring in 55 mph speed zones decreased from 20 in August 2024 to 15 in August 2025. Conversely, crashes in 20 mph speed zones increased from 2 to 5. There was also a slight increase in crashes in 35 mph zones, rising from 5 to 6.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-08-01 to 2025-08-31 · 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: 2025-08-01 through 2025-08-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2025-08-01 through 2025-08-31 (31 days)
- Geographic scope: LEXINGTON, MA
- Total crash records analyzed: 43
- Total persons involved: 101
- Total vehicles involved: 80
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). "LEXINGTON, MA Crash Intelligence Report: August 2025." Published June 21, 2026. Reporting period: 2025-08-01 to 2025-08-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/lexington/august-2025-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: 2025-08-01 – 2025-08-31
Generated: June 21, 2026 · All rights reserved