ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · LEXINGTON, MA · OCTOBER 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/october-2025-report
Monthly Traffic Safety Analysis
56 CRASHES IN
LEXINGTON, MA
OCTOBER 2025
In October 2025, Lexington recorded 56 crashes, an increase from 52 crashes in October 2024, representing a 7.7% rise. A notable shift is the decrease in total fatalities from 1 in the prior period to 0 in the current period.
56
▲ 7.7%was 52
Total Crash Events
0
▼ -100.0%was 1
Persons Killed
11
▼ -42.1%was 19
Persons Injured
7
▲ 250.0%was 2
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 · 2025-10-01 to 2025-10-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall crash incidents in Lexington show a slight upward trend, with total crashes increasing by 7.7% from 52 to 56 year-over-year. However, total injuries decreased by 42.1% from 19 to 11, and total fatalities decreased from 1 to 0, indicating a positive trend in crash outcomes despite the rise in incident count.
7
Hit-and-Run Crashes — October 2025
▲ 250.0% vs prior (2)
Hit-and-run crashes increased significantly from 2 in October 2024 to 7 in October 2025. This represents a substantial rise in the hit-and-run rate, from 3.8% of all crashes in the prior period to 12.5% in the current period, indicating an upward trend.
Vulnerable Road User Casualties
0
Cyclists Killed
0
Motorists Killed
1
Cyclists Injured
10
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · 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 Tuesday in October 2024 (12 crashes) to Thursday in October 2025 (17 crashes). While the peak hour remained 4 PM with 6 crashes in both periods, there was a notable increase in crashes on Thursdays and Fridays year-over-year, alongside a decrease on Tuesdays and Sundays.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The fatal crash rate decreased significantly from 1.9% in October 2024 to 0% in October 2025. The proportion of crashes resulting in any injury (Serious, Minor, or Possible) decreased from 30.8% (16 out of 52 crashes) in the prior period to 19.6% (11 out of 56 crashes) in the current period, indicating a shift towards less severe outcomes.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factor shifted from 'Followed too closely' in October 2024 (9 crashes) to 'No improper driving' in October 2025 (13 crashes), a 116.7% increase for the latter. Crashes attributed to 'Driving too fast for conditions' increased significantly from 0 to 5, while 'Failed to yield right of way' decreased by 22.2% from 9 to 7 crashes.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in clear weather conditions decreased from 46 in October 2024 to 37 in October 2025, while crashes in rainy conditions increased from 3 to 8. There was a notable increase in crashes occurring in 'Dark - roadway not lighted' conditions, rising from 5 to 12 year-over-year, and crashes on wet road surfaces increased from 3 to 9.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes increased from 94 to 112 year-over-year. Honda vehicles involved in crashes more than doubled from 11 to 23, becoming the most frequently involved make. There was a decrease in persons aged 65 and older involved in crashes, from 17 to 12, while involvement across most other age groups saw an increase.
Top Vehicle Makes (112 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Vehicle unit records
9 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (137 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 55 mph speed zones saw a significant increase, rising from 16 in October 2024 to 27 in October 2025. Conversely, crashes in 35 mph zones decreased from 8 to 5, and the fatal crash that occurred in a 35 mph zone in the prior period was not repeated in the current period. Crashes in 25 mph zones slightly increased from 3 to 4.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · 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: 2025-10-01 through 2025-10-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2025-10-01 through 2025-10-31 (31 days)
- Geographic scope: LEXINGTON, MA
- Total crash records analyzed: 56
- Total persons involved: 145
- Total vehicles involved: 112
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: October 2025." Published June 21, 2026. Reporting period: 2025-10-01 to 2025-10-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/lexington/october-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: 2025-10-01 – 2025-10-31
Generated: June 21, 2026 · All rights reserved