ThatCarHitMe.com
An Injuria.ai Company
CRASH INTELLIGENCE REPORT · BATON ROUGE, LA · SEPTEMBER 2022
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/september-2022-report
Monthly Traffic Safety Analysis
1,287 CRASHES IN
BATON ROUGE, LA
SEPTEMBER 2022
In September 2022, Baton Rouge recorded 1,287 traffic crashes, resulting in 8 fatalities and 939 injuries. Analysis of contributing factors shows that 'Violations' were cited in 76.6% of all incidents, representing the most dominant factor in crash causation during this period. The most frequent collision type was front-to-rear crashes, accounting for 34.2% of the total.
1,287
Total Crash Events
8
Fatal Crashes
939
Injury Crashes
22.6%
Hit-and-Run Rate
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 · 2022-09-01 to 2022-09-30 · Aggregate counts from crash, person, and vehicle records
291
Hit-and-Run Crashes — September 2022
During this period, 291 crashes were classified as hit-and-run incidents, accounting for 22.6% of all reported crashes. This classification is based on the initial determination made by the responding officer at the scene.
When Crashes Happen
Crash occurrences peaked toward the end of the work week, with Thursday recording 261 incidents and Friday recording 257. In terms of lighting, a significant majority of crashes, 983 out of 1,287 (76.4%), occurred during daylight hours. Crashes in darkness with street lights present were the next largest group, with 205 incidents.
Source: Baton Rouge Crash Data · Socrata Open Data · 2022-09-01 to 2022-09-30 · Crash date field aggregated by weekday
Crash Severity Breakdown
Of the 1,287 total crashes, 73% resulted in at least one injury, while 26.4% involved no injuries. There were 8 fatal crashes recorded during this period, which resulted in a total of 8 fatalities.
Outcome by Severity (Crash Events)
Source: Baton Rouge Crash Data · Socrata Open Data · 2022-09-01 to 2022-09-30 · 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 · 2022-09-01 to 2022-09-30 · Most severe injury per crash record
Top Contributing Factors
The primary contributing factor cited in crashes was 'Violations,' which was associated with 986 incidents, or 76.6% of the total. 'Movement prior to crash' was the second most common factor, listed in 18.1% of crashes. All other individual factors each accounted for less than 3% of incidents.
Officer-Reported Primary Contributing Cause
Source: Baton Rouge Crash Data · Socrata Open Data · 2022-09-01 to 2022-09-30 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The vast majority of crashes occurred in favorable environmental conditions. Specifically, 88.7% of crashes happened in clear weather and 91.3% on dry road surfaces. Crashes during rain were reported in 37 instances, while 63 crashes occurred on wet roads.
Weather
Source: Baton Rouge Crash Data · Socrata Open Data · 2022-09-01 to 2022-09-30 · Weather condition at time of crash
Lighting
Source: Baton Rouge Crash Data · Socrata Open Data · 2022-09-01 to 2022-09-30 · Lighting condition field
Road Surface
Source: Baton Rouge Crash Data · Socrata Open Data · 2022-09-01 to 2022-09-30 · Road surface condition field
Relation to Roadway
The data indicates that 1,064 crashes, or 82.7% of the total, occurred directly on the roadway. A smaller portion of incidents happened adjacent to travel lanes, with 71 crashes (5.5%) occurring in a parking lane or zone. In total, 197 crashes, or 15.3%, took place off the primary roadway surface in locations such as shoulders, medians, or roadsides.
Relation to Roadway
"Other" combines 3 smaller categories (17 records): Separator/traffic island (7), On shoulder, right side (5), Gore (5).
Source: Baton Rouge Crash Data · Socrata Open Data · 2022-09-01 to 2022-09-30 · Crash-level records
Crash Location Type
Crashes were most prevalent on 'City street' facilities, which accounted for 628 incidents, or 48.8% of all crashes in the period. Major thoroughfares also saw significant activity, with 243 crashes on Interstates and a combined 342 crashes on State and US highways. Crashes on 'Off road/private property' accounted for 72 incidents.
Crash Location Type
Source: Baton Rouge Crash Data · Socrata Open Data · 2022-09-01 to 2022-09-30 · Crash-level records
Manner of Collision
The most common crash type was 'Front to rear - rear end,' representing 440 incidents, or 34.2% of all collisions. Single-vehicle crashes, categorized as 'Not a collision between two motor vehicles in transport,' were the second most frequent type with 161 occurrences (12.5%). 'Sideswipe - same direction' and 'Angle - perpendicular/other angle' crashes followed, with 159 and 151 incidents respectively.
Manner of Collision
"Other" combines 14 smaller categories (212 records): Backing - rear to front (29), Front to front - head on (26), Angle - right into flow (26), Front to front - left against flow (19), Sideswipe - opposite direction (16), Sideswipe - left overtake (15), Backing - rear to side (14), Angle - right across flow (13), Angle - left across flow (13), Backing - rear to rear (12), Sideswipe - right overtake (9), Front to front - right against flow (8), Angle - left overtake (8), Angle - right overtake (4).
Source: Baton Rouge Crash Data · Socrata Open Data · 2022-09-01 to 2022-09-30 · Crash-level records
Vehicles Per Crash
The majority of crashes, 1,092 incidents or 84.8% of the total, involved two vehicles. Single-vehicle crashes accounted for 7.5% of all incidents, with 96 recorded. Crashes involving three or more vehicles were less common, with 83 crashes involving three vehicles and 16 crashes involving four or more.
Vehicles Per Crash
Source: Baton Rouge Crash Data · Socrata Open Data · 2022-09-01 to 2022-09-30 · Crash-level records
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: 2022-09-01 through 2022-09-30
- Report generated: June 19, 2026
Data Coverage
- Reporting period: 2022-09-01 through 2022-09-30 (30 days)
- Geographic scope: Baton Rouge, LA
- Total crash records analyzed: 1,287
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: September 2022." Published June 19, 2026. Reporting period: 2022-09-01 to 2022-09-30. Data source: Baton Rouge Crash Data, Socrata Open Data. Available at: https://thatcarhitme.com/crash-data/louisiana/baton-rouge/september-2022-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: 2022-09-01 – 2022-09-30
Generated: June 19, 2026 · All rights reserved