ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · BATON ROUGE, LA · JANUARY 2026
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/january-2026-report
Monthly Traffic Safety Analysis
1,150 CRASHES IN
BATON ROUGE, LA
JANUARY 2026
In January 2026, Baton Rouge recorded 1,150 total traffic crashes, a 13.9% increase from the 1,010 crashes documented in January 2025. This rise in collisions was accompanied by a 10.9% increase in total injuries, from 811 to 899. The most significant shift was a substantial increase in crashes attributed to 'Violations' as a contributing factor, which grew from 790 to 934 incidents year-over-year.
1,150
▲ 13.9%was 1,010
Total Crash Events
1
Fatal Crashes
899
▲ 10.9%was 811
Injury Crashes
263
▲ 14.8%was 229
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 · 2026-01-01 to 2026-01-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall traffic collisions in Baton Rouge showed a rising trend year-over-year. Total crashes increased by 13.9%, from 1,010 in January 2025 to 1,150 in January 2026. This was mirrored by a 10.9% rise in the number of injuries, while fatalities remained unchanged with one death in each period.
263
Hit-and-Run Crashes — January 2026
▲ 14.8% vs prior (229)
Hit-and-run incidents increased in both count and rate compared to the previous year. The total number of hit-and-run crashes rose from 229 to 263. The corresponding hit-and-run rate, representing the percentage of all crashes that were hit-and-runs, trended slightly upward from 22.7% to 22.9%.
When Crashes Happen
The temporal pattern of crashes shifted slightly between the two periods. In January 2026, the peak day for crashes was Thursday with 232 incidents. This marks a change from January 2025, when Friday was the peak day with 202 crashes.
Source: Baton Rouge Crash Data · Socrata Open Data · 2026-01-01 to 2026-01-31 · Crash date field aggregated by weekday
Crash Severity Breakdown
While the number of fatal crashes remained constant at one for both periods, the fatal crash rate decreased slightly from 0.1% to 0.09% due to the overall increase in total crashes. The proportion of crashes resulting in injury also decreased, from 80.3% in the prior year to 78.2% in the current period. Correspondingly, the share of non-injury crashes grew from 19.6% to 21.7% of all incidents.
Outcome by Severity (Crash Events)
Source: Baton Rouge Crash Data · Socrata Open Data · 2026-01-01 to 2026-01-31 · 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 · 2026-01-01 to 2026-01-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factors remained consistent, with 'Violations' ranking first in both periods. The number of crashes attributed to 'Violations' increased from 790 to 934, a rise of 144 incidents. In contrast, crashes linked to 'Movement prior to crash', the second-ranked factor, decreased in count from 177 to 159. The share of crashes involving 'Violations' also grew from 78.2% to 81.2%.
Officer-Reported Primary Contributing Cause
Source: Baton Rouge Crash Data · Socrata Open Data · 2026-01-01 to 2026-01-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in adverse weather conditions increased from 15.0% to 18.8% of all incidents year-over-year, with crashes in the rain more than doubling from 48 to 114. The proportion of crashes on non-dry road surfaces also rose from 11.6% to 14.9%, largely due to an increase in crashes on wet roads from 83 to 164. Collisions in non-daylight conditions saw a smaller increase, rising from 34.4% to 37.0% of the total.
Weather
Source: Baton Rouge Crash Data · Socrata Open Data · 2026-01-01 to 2026-01-31 · Weather condition at time of crash
Lighting
Source: Baton Rouge Crash Data · Socrata Open Data · 2026-01-01 to 2026-01-31 · Lighting condition field
Road Surface
Source: Baton Rouge Crash Data · Socrata Open Data · 2026-01-01 to 2026-01-31 · 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: 2026-01-01 through 2026-01-31
- Report generated: June 19, 2026
Data Coverage
- Reporting period: 2026-01-01 through 2026-01-31 (31 days)
- Geographic scope: Baton Rouge, LA
- Total crash records analyzed: 1,150
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: January 2026." Published June 19, 2026. Reporting period: 2026-01-01 to 2026-01-31. Data source: Baton Rouge Crash Data, Socrata Open Data. Available at: https://thatcarhitme.com/crash-data/louisiana/baton-rouge/january-2026-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: 2026-01-01 – 2026-01-31
Generated: June 19, 2026 · All rights reserved