ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · BATON ROUGE, LA · OCTOBER 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/louisiana/baton-rouge/october-2024-report
Monthly Traffic Safety Analysis
1,485 CRASHES IN
BATON ROUGE, LA
OCTOBER 2024
In October 2024, Baton Rouge recorded 1,485 vehicle crashes, an 11.2% increase from the 1,336 crashes reported in October 2023. While overall collisions and injuries rose, the number of fatalities decreased from two in the prior period to zero in the current period. The total number of injuries increased from 1,066 to 1,148, a 7.7% rise year-over-year.
1,485
▲ 11.2%was 1,336
Total Crash Events
0
▼ -100.0%was 2
Fatal Crashes
1,148
▲ 7.7%was 1,066
Injury Crashes
327
▲ 6.2%was 308
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 · 2024-10-01 to 2024-10-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Year-over-year data indicates an upward trend in traffic collisions for the month of October. Total crashes increased by 11.2%, rising from 1,336 in October 2023 to 1,485 in October 2024. In parallel, the number of reported injuries grew by 7.7% during the same period, increasing from 1,066 to 1,148.
327
Hit-and-Run Crashes — October 2024
▲ 6.2% vs prior (308)
The absolute number of hit-and-run incidents increased from 308 in October 2023 to 327 in October 2024. However, because the total number of crashes increased at a faster pace, the hit-and-run rate as a proportion of all crashes slightly decreased. The hit-and-run rate was 22.0% in the current period, down from 23.1% in the prior year's period.
When Crashes Happen
The temporal distribution of crashes showed a shift in the peak day of the week between the two periods. In October 2024, Thursday was the day with the most crashes (265), a change from October 2023 when Wednesday saw the highest volume (243 crashes). Collision counts increased on most days of the week year-over-year, with Thursday experiencing the largest growth in crash volume.
Source: Baton Rouge Crash Data · Socrata Open Data · 2024-10-01 to 2024-10-31 · Crash date field aggregated by weekday
Crash Severity Breakdown
Crash severity outcomes improved year-over-year, with zero fatal crashes recorded in October 2024 compared to two in October 2023. This brought the number of fatalities down from two to zero. The proportion of crashes resulting in an injury decreased from 79.8% to 77.3% of all collisions, while the share of crashes with no reported injuries increased from 20.1% to 22.7%.
Outcome by Severity (Crash Events)
Source: Baton Rouge Crash Data · Socrata Open Data · 2024-10-01 to 2024-10-31 · 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 · 2024-10-01 to 2024-10-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factors cited in crashes remained consistent year-over-year, with 'Violations' being the most common factor in both periods. The number of crashes attributed to violations increased from 1,000 in October 2023 to 1,127 in October 2024. The second most common factor, 'Movement prior to crash,' also saw its count increase from 286 to 320. The top three primary contributing factors did not change in their rank order between the two periods.
Officer-Reported Primary Contributing Cause
Source: Baton Rouge Crash Data · Socrata Open Data · 2024-10-01 to 2024-10-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Environmental conditions at the time of crashes were highly similar between October 2023 and October 2024. In both periods, the vast majority of collisions occurred in clear weather (90.6% prior vs. 92.3% current) and during daylight hours (72.4% prior vs. 72.7% current). The proportion of crashes on dry road surfaces increased from 92.6% to 94.9%, while the absolute count of crashes on wet roads decreased from 75 to 57.
Weather
Source: Baton Rouge Crash Data · Socrata Open Data · 2024-10-01 to 2024-10-31 · Weather condition at time of crash
Lighting
Source: Baton Rouge Crash Data · Socrata Open Data · 2024-10-01 to 2024-10-31 · Lighting condition field
Road Surface
Source: Baton Rouge Crash Data · Socrata Open Data · 2024-10-01 to 2024-10-31 · 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: 2024-10-01 through 2024-10-31
- Report generated: June 19, 2026
Data Coverage
- Reporting period: 2024-10-01 through 2024-10-31 (31 days)
- Geographic scope: Baton Rouge, LA
- Total crash records analyzed: 1,485
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: October 2024." Published June 19, 2026. Reporting period: 2024-10-01 to 2024-10-31. Data source: Baton Rouge Crash Data, Socrata Open Data. Available at: https://thatcarhitme.com/crash-data/louisiana/baton-rouge/october-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: Baton Rouge Crash Data · Socrata
Period: 2024-10-01 – 2024-10-31
Generated: June 19, 2026 · All rights reserved