Monthly Traffic Safety Analysis

1,295 CRASHES IN
BATON ROUGE, LA
MAY 2025

All metrics benchmarked againstMay 2024

In May 2025, Baton Rouge recorded 1,295 total crashes, a 3.3% decrease from the 1,339 crashes reported in May 2024. Despite the overall reduction in collisions, the number of fatalities increased from 4 to 5 year-over-year.

1,295

▼ -3.3%was 1,339

Total Crash Events

5

▲ 25.0%was 4

Fatal Crashes

1,017

▼ -1.2%was 1,029

Injury Crashes

270

▼ -15.4%was 319

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 · 2025-05-01 to 2025-05-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash trends in Baton Rouge show a slight decrease in May 2025 compared to the same month in the previous year. Total collisions fell by 3.3%, from 1,339 to 1,295. While total injuries also saw a small reduction from 1,029 to 1,017, fatalities increased from 4 to 5.

270

Hit-and-Run Crashes — May 2025

▼ -15.4% vs prior (319)

The number of hit-and-run incidents decreased in May 2025 compared to the same month in 2024. The total count of hit-and-run crashes fell from 319 to 270. This corresponds to a drop in the hit-and-run rate, which decreased from 23.8% of all crashes in May 2024 to 20.8% in May 2025.

When Crashes Happen

The temporal patterns of crashes remained largely consistent year-over-year. Friday was the peak day for crashes in both May 2025 (275 crashes) and May 2024 (272 crashes). While most weekdays saw a decrease or remained stable, Saturday crashes increased from 144 to 176, while Wednesday crashes decreased from 212 to 182.

Source: Baton Rouge Crash Data · Socrata Open Data · 2025-05-01 to 2025-05-31 · Crash date field aggregated by weekday

Crash Severity Breakdown

While total crashes decreased, the severity of those crashes increased slightly in May 2025 compared to May 2024. The number of fatal crashes rose from 4 to 5, and the proportion of crashes resulting in an injury increased from a 76.8% share to a 78.5% share. Consequently, the share of crashes with no reported injuries decreased from 22.9% to 21.1% of all incidents.

Outcome by Severity (Crash Events)

Fatal5fatal crashes0.4%
↑25.0%prior 4
Injury1,017minor injury crashes78.5%
↓-1.2%prior 1,029
No Injury273no injury crashes21.1%
↓-10.8%prior 306

Source: Baton Rouge Crash Data · Socrata Open Data · 2025-05-01 to 2025-05-31 · 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 · 2025-05-01 to 2025-05-31 · Most severe injury per crash record

Top Contributing Factors

The primary contributing factors for crashes remained consistent between May 2024 and May 2025, with 'Violations' being the top cited factor in both periods. The count of crashes attributed to 'Violations' was nearly stable, increasing slightly from 1,012 to 1,016. Crashes related to 'Movement prior to crash' decreased from a count of 283 to 233, while those involving 'Driver condition' also fell from 24 to 18. The ranking of the top three contributing factors did not change year-over-year.

Officer-Reported Primary Contributing Cause

Violations1,016 (78.5%)↑0.4%prior 1,012
Movement prior to crash233 (18%)↓-17.7%prior 283
Driver condition18 (1.4%)↓-25.0%prior 24
Vision obstructions8 (0.6%)
Vehicle condition7 (0.5%)↑40.0%prior 5
Roadway condition4 (0.3%)↓-50.0%prior 8
Non-motorist action4 (0.3%)
Road surface2 (0.2%)
Traffic control1 (0.1%)
Non-motorist condition1 (0.1%)

Source: Baton Rouge Crash Data · Socrata Open Data · 2025-05-01 to 2025-05-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Environmental conditions for crashes were remarkably similar in May 2025 compared to the prior year. The vast majority of incidents in both periods occurred during daylight (79.3% of crashes in 2025 vs. 79.2% in 2024) and in clear weather (82.5% vs. 81.5%). Similarly, crashes on dry road surfaces accounted for 86.7% of the total in 2025 and 86.1% in 2024, indicating no significant shift in the proportion of crashes occurring in adverse conditions.

Weather

Clear1,069 (83.3%)
↓-2.0%prior 1,091
Rain114 (8.9%)
↓-1.7%prior 116
Cloudy101 (7.9%)
↓-9.8%prior 112

Source: Baton Rouge Crash Data · Socrata Open Data · 2025-05-01 to 2025-05-31 · Weather condition at time of crash

Lighting

Daylight1,027 (80.0%)
↓-3.2%prior 1,061
Dark - continuous street lights183 (14.3%)
↓-5.2%prior 193
Dark - street lights at intersection only35 (2.7%)
↑52.2%prior 23
Dawn/dusk20 (1.6%)
↓-25.9%prior 27
Dark - not lighted12 (0.9%)
↑33.3%prior 9
Dark - unknown lighting6 (0.5%)
↓-45.5%prior 11
Other1 (0.1%)

Source: Baton Rouge Crash Data · Socrata Open Data · 2025-05-01 to 2025-05-31 · Lighting condition field

Road Surface

Dry1,123 (87.3%)
↓-2.6%prior 1,153
Wet156 (12.1%)
↑0.6%prior 155
Water (standing, moving)3 (0.2%)
↓-40.0%prior 5
Other2 (0.2%)
Mud, dirt, gravel1 (0.1%)
Ice/frost1 (0.1%)

Source: Baton Rouge Crash Data · Socrata Open Data · 2025-05-01 to 2025-05-31 · 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: 2025-05-01 through 2025-05-31
  • Report generated: June 19, 2026

Data Coverage

  • Reporting period: 2025-05-01 through 2025-05-31 (31 days)
  • Geographic scope: Baton Rouge, LA
  • Total crash records analyzed: 1,295

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: May 2025." Published June 19, 2026. Reporting period: 2025-05-01 to 2025-05-31. Data source: Baton Rouge Crash Data, Socrata Open Data. Available at: https://thatcarhitme.com/crash-data/louisiana/baton-rouge/may-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

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