ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · BATON ROUGE, LA · NOVEMBER 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/louisiana/baton-rouge/november-2025-report
Monthly Traffic Safety Analysis
1,223 CRASHES IN
BATON ROUGE, LA
NOVEMBER 2025
In November 2025, Baton Rouge recorded 1,223 total crashes, a 12.0% decrease from the 1,389 crashes recorded in November 2024. This year-over-year comparison shows a notable reduction in overall crash volume, accompanied by fewer fatalities and injuries. The most significant change was the overall decline in collisions, which fell by 166 incidents.
1,223
▼ -12.0%was 1,389
Total Crash Events
1
▼ -66.7%was 3
Fatal Crashes
926
▼ -14.5%was 1,083
Injury Crashes
270
▼ -5.9%was 287
Hit-and-Run Crashes
Note: "Fatal Crashes" and "Injury Crashes" count crash events — this source publishes crash-level counts only, not individual persons.
Source: Baton Rouge Crash Data · Socrata Open Data · 2025-11-01 to 2025-11-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Crash trends in Baton Rouge show a significant year-over-year decline for November. Total crashes fell by 12.0%, from 1,389 in November 2024 to 1,223 in November 2025. This downward trend was also reflected in crash outcomes, with total injuries decreasing by 14.5% and fatalities dropping from 3 to 1.
270
Hit-and-Run Crashes — November 2025
▼ -5.9% vs prior (287)
While the total number of hit-and-run crashes decreased from 287 in November 2024 to 270 in November 2025, the hit-and-run rate shows an upward trend. Because the overall number of crashes fell more sharply, the proportion of crashes involving a hit-and-run driver increased from 20.7% to 22.1% year-over-year. This indicates that hit-and-runs constituted a larger share of all collisions in the more recent period.
When Crashes Happen
Temporal patterns show that Friday remained the day with the most crashes in both November 2025 (194 crashes) and November 2024 (261 crashes). While the peak day of the week was consistent, the volume of crashes on Friday decreased by 25.7% year-over-year. Data on hourly crash distribution was not available for a detailed comparison of peak times of day.
Source: Baton Rouge Crash Data · Socrata Open Data · 2025-11-01 to 2025-11-30 · Crash date field aggregated by weekday
Crash Severity Breakdown
Crash severity decreased in November 2025 compared to the previous year. The number of fatal crashes dropped from 3 to 1, with the fatal crash rate falling from 0.22% to 0.08%. The proportion of crashes resulting in an injury also saw a slight decrease, from 78.0% of all crashes in the prior year to 75.7% in the current period. Correspondingly, the share of crashes with no injuries rose from 21.8% to 24.2%.
Outcome by Severity (Crash Events)
Source: Baton Rouge Crash Data · Socrata Open Data · 2025-11-01 to 2025-11-30 · Severity derived from reported fatal/injury indicators (no KABCO A/B/C codes)
Severity Distribution (Crash Events)
Source: Baton Rouge Crash Data · Socrata Open Data · 2025-11-01 to 2025-11-30 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factors remained consistent year-over-year, with 'Violations' being the most cited factor in both periods. The count of crashes attributed to 'Violations' decreased by 5.2% from 1,005 to 953, though its share of all contributing factors increased from 72.4% to 77.9%. The second-ranked factor, 'Movement prior to crash,' saw a more significant reduction, with its count falling 32.6% from 331 to 223 incidents.
Officer-Reported Primary Contributing Cause
Source: Baton Rouge Crash Data · Socrata Open Data · 2025-11-01 to 2025-11-30 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes in November 2025 occurred under significantly better environmental conditions compared to the previous year. The number of crashes during rain fell from 148 to 23, and collisions on wet road surfaces dropped from 208 to 41. Consequently, the proportion of crashes happening in adverse weather (not 'Clear') decreased from 17.7% to 6.0% year-over-year. Crashes in dark or low-light conditions also decreased in both count and proportion, falling from 465 to 387 incidents.
Weather
Source: Baton Rouge Crash Data · Socrata Open Data · 2025-11-01 to 2025-11-30 · Weather condition at time of crash
Lighting
Source: Baton Rouge Crash Data · Socrata Open Data · 2025-11-01 to 2025-11-30 · Lighting condition field
Road Surface
Source: Baton Rouge Crash Data · Socrata Open Data · 2025-11-01 to 2025-11-30 · Road surface condition field
Data Sources & Methodology
Primary Data Source
All crash data in this report is sourced from Baton Rouge Crash Data, accessed programmatically via the Socrata 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: Socrata 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: 2025-11-01 through 2025-11-30
- Report generated: June 19, 2026
Data Coverage
- Reporting period: 2025-11-01 through 2025-11-30 (30 days)
- Geographic scope: Baton Rouge, LA
- Total crash records analyzed: 1,223
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). "Baton Rouge, LA Crash Intelligence Report: November 2025." Published June 19, 2026. Reporting period: 2025-11-01 to 2025-11-30. Data source: Baton Rouge Crash Data, Socrata Open Data. Available at: https://thatcarhitme.com/crash-data/louisiana/baton-rouge/november-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: Baton Rouge Crash Data · Socrata
Period: 2025-11-01 – 2025-11-30
Generated: June 19, 2026 · All rights reserved