Monthly Traffic Safety Analysis

1,365 CRASHES IN
BATON ROUGE, LA
MARCH 2025

All metrics benchmarked againstMarch 2024

In March 2025, Baton Rouge recorded 1,365 total vehicle crashes, a 2.1% decrease from the 1,394 crashes in March 2024. This year-over-year comparison shows a slight decline in overall crash volume. The most notable shift was the decrease in total fatalities, which fell from 7 to 5.

1,365

▼ -2.1%was 1,394

Total Crash Events

5

▼ -28.6%was 7

Fatal Crashes

1,060

▼ -2.6%was 1,088

Injury Crashes

285

▼ -13.9%was 331

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 · 2025-03-01 to 2025-03-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall crash trend in Baton Rouge shows a modest year-over-year decline. Total crashes decreased by 2.1%, from 1,394 to 1,365. This downward trend is also reflected in crash outcomes, with total injuries falling from 1,088 to 1,060 and fatalities decreasing from 7 to 5.

285

Hit-and-Run Crashes — March 2025

▼ -13.9% vs prior (331)

Hit-and-run crashes showed a notable decrease year-over-year. The total count of hit-and-run incidents fell from 331 in March 2024 to 285 in March 2025. Consequently, the hit-and-run rate, as a percentage of all crashes, trended down from 23.7% to 20.9%.

When Crashes Happen

The peak day for crashes shifted from Friday (291 crashes) in March 2024 to Saturday (211 crashes) in March 2025. This change was driven by a significant year-over-year drop in Friday crashes. Conversely, crashes on Sundays increased from 121 in the prior period to 158 in the current period.

Source: Baton Rouge Crash Data · Socrata Open Data · 2025-03-01 to 2025-03-31 · Crash date field aggregated by weekday

Crash Severity Breakdown

Crash severity saw a slight improvement compared to the previous year. The number of fatal crashes decreased from 7 to 5, with the fatal crash share dropping from 0.5% to 0.4% of all incidents. The proportion of crashes resulting in injury remained nearly stable, moving from 78.0% in March 2024 to 77.7% in March 2025.

Outcome by Severity (Crash Events)

Fatal5fatal crashes0.4%
↓-28.6%prior 7
Injury1,060minor injury crashes77.7%
↓-2.6%prior 1,088
No Injury300no injury crashes22%
↑0.3%prior 299

Source: Baton Rouge Crash Data · Socrata Open Data · 2025-03-01 to 2025-03-31 · 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 · 2025-03-01 to 2025-03-31 · Most severe injury per crash record

Top Contributing Factors

While "Violations" remained the leading contributing factor in both periods, the count of crashes attributed to it increased by 6.6%, from 1,000 to 1,066. In contrast, the second-ranked factor, "Movement prior to crash," saw its incident count decrease by 25.7% from 334 to 248. "Driver condition" saw a small increase in count from 29 to 32 incidents.

Officer-Reported Primary Contributing Cause

Violations1,066 (78.1%)↑6.6%prior 1,000
Movement prior to crash248 (18.2%)↓-25.7%prior 334
Driver condition32 (2.3%)↑10.3%prior 29
Vehicle condition7 (0.5%)↓-12.5%prior 8
Vision obstructions6 (0.4%)
Road surface3 (0.2%)
Roadway condition1 (0.1%)↓-80.0%prior 5
Non-motorist condition1 (0.1%)
Lighting condition1 (0.1%)

Source: Baton Rouge Crash Data · Socrata Open Data · 2025-03-01 to 2025-03-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes under adverse conditions were less frequent in March 2025 compared to the prior year. Incidents on wet road surfaces decreased from 178 to 117, while crashes during rainfall fell from 107 to 80. The proportion of crashes occurring in dark conditions also declined slightly, from 22.1% to 20.7% of all crashes.

Weather

Clear1,176 (86.9%)
↑5.9%prior 1,110
Cloudy97 (7.2%)
↓-37.8%prior 156
Rain80 (5.9%)
↓-25.2%prior 107
Severe crosswinds1 (0.1%)

Source: Baton Rouge Crash Data · Socrata Open Data · 2025-03-01 to 2025-03-31 · Weather condition at time of crash

Lighting

Daylight1,031 (76.4%)
↓-0.3%prior 1,034
Dark - continuous street lights218 (16.2%)
↓-12.8%prior 250
Dawn/dusk34 (2.5%)
↑30.8%prior 26
Dark - street lights at intersection only34 (2.5%)
↓-2.9%prior 35
Dark - not lighted19 (1.4%)
↑35.7%prior 14
Dark - unknown lighting12 (0.9%)
↑33.3%prior 9
Other1 (0.1%)

Source: Baton Rouge Crash Data · Socrata Open Data · 2025-03-01 to 2025-03-31 · Lighting condition field

Road Surface

Dry1,232 (90.9%)
↑2.9%prior 1,197
Wet117 (8.6%)
↓-34.3%prior 178
Water (standing, moving)6 (0.4%)

Source: Baton Rouge Crash Data · Socrata Open Data · 2025-03-01 to 2025-03-31 · 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: 2025-03-01 through 2025-03-31
  • Report generated: June 19, 2026

Data Coverage

  • Reporting period: 2025-03-01 through 2025-03-31 (31 days)
  • Geographic scope: Baton Rouge, LA
  • Total crash records analyzed: 1,365

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: March 2025." Published June 19, 2026. Reporting period: 2025-03-01 to 2025-03-31. Data source: Baton Rouge Crash Data, Socrata Open Data. Available at: https://thatcarhitme.com/crash-data/louisiana/baton-rouge/march-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

Baton Rouge, LA Crash Report — March 2025 | ThatCarHitMe.com