ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · LEXINGTON, MA · SEPTEMBER 2024
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/september-2024-report
Monthly Traffic Safety Analysis
42 CRASHES IN
LEXINGTON, MA
SEPTEMBER 2024
In September 2024, Lexington experienced 42 crashes, a 25% decrease from the 56 crashes reported in September 2023. Total injuries also saw a significant reduction, falling by 41.7% from 12 to 7. A notable shift includes the decrease in crashes attributed to speeding, which dropped from 9 to 1.
42
▼ -25.0%was 56
Total Crash Events
0
Persons Killed
7
▼ -41.7%was 12
Persons Injured
3
▼ -50.0%was 6
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. 3 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, crash data for September 2024 indicates a declining trend in Lexington compared to the same month last year. Total crashes decreased by 14, representing a 25% reduction from 56 crashes to 42. This decline is also reflected in total injuries, which fell by 41.7% from 12 to 7.
3
Hit-and-Run Crashes — September 2024
▼ -50.0% vs prior (6)
Hit-and-run crashes decreased by 50% year-over-year, falling from 6 incidents in September 2023 to 3 in September 2024. Consequently, the hit-and-run crash rate also declined from 10.7% of all crashes in the prior period to 7.1% in the current period, representing a decrease of 3.6 percentage points.
Vulnerable Road User Casualties
0
Cyclists Killed
0
Motorists Killed
1
Cyclists Injured
6
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal distribution of crashes shifted year-over-year, with the peak day moving from Tuesday in September 2023 (15 crashes) to Thursday in September 2024 (8 crashes). Similarly, the peak hour for crashes changed from 8 AM (11 crashes) in the prior period to 2 PM (6 crashes) in the current period, indicating a shift in the busiest crash times.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Fatalities remained at zero for both September 2023 and September 2024. However, total injuries decreased from 12 in the prior period to 7 in the current period, representing a 41.7% reduction. The proportion of crashes resulting in any injury (Minor or Possible) also decreased from 19.6% (11 out of 56 crashes) in September 2023 to 14.3% (6 out of 42 crashes) in September 2024.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Most severe injury per crash record
Top Contributing Factors
Several contributing factors saw notable changes in crash counts. Crashes attributed to 'Driving too fast for conditions' decreased significantly from 8 in September 2023 to 0 in September 2024, while 'Failed to yield right of way' crashes decreased by 5, from 7 to 2. Conversely, crashes where 'Followed too closely' was a factor increased by 2, from 10 to 12. Regarding manner of collision, 'Single vehicle crash' incidents decreased by 11 (from 17 to 6), while 'Rear-end' collisions increased by 7 (from 15 to 22).
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring on wet road surfaces decreased from 16 in September 2023 to 5 in September 2024, a reduction of 11 crashes. The number of crashes in clear weather conditions also decreased from 33 to 27 year-over-year. Crashes occurring in daylight conditions decreased from 43 to 30, while crashes in dark-lighted roadway conditions remained constant at 7 for both periods.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes decreased from 99 in September 2023 to 85 in September 2024. While TOYOTA remained the top vehicle make involved, its count increased slightly from 16 to 17. Notably, the number of HONDA vehicles involved decreased by 10, from 13 to 3, and HYUNDAI vehicles involved increased from 1 to 5. The 45-54 age group saw the largest decrease in persons involved, dropping by 14 from 23 to 9, while the 26-34 age group saw an increase of 9 persons involved, from 16 to 25.
Top Vehicle Makes (85 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Vehicle unit records
11 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (90 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Person-level records linked to crash events
Speed Limit Zones
Crashes occurring in the 55 mph speed limit zone decreased by 9, from 23 in September 2023 to 14 in September 2024, yet it remained the zone with the highest crash count. Crashes in the 30 mph zone also saw a reduction, decreasing by 6 from 10 to 4. No fatalities were recorded in any speed limit zone for either period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · 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: 2024-09-01 through 2024-09-30
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2024-09-01 through 2024-09-30 (30 days)
- Geographic scope: LEXINGTON, MA
- Total crash records analyzed: 42
- Total persons involved: 103
- Total vehicles involved: 85
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: September 2024." Published June 21, 2026. Reporting period: 2024-09-01 to 2024-09-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/lexington/september-2024-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: 2024-09-01 – 2024-09-30
Generated: June 21, 2026 · All rights reserved