Downloads · 30 days
0
ai-cargo-logistics-whitepaper
rajvivan/ai-cargo-logistics-whitepaper
ai-cargo-logistics-whitepaper is a machine learning model from rajvivan. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
[](https://creativecommons.org/licenses/by/4.0/)
Access
Public
Updated Apr 29, 2026
Repo size
—
Likes
0
Public
Repo makeup
Click a slice to open those files.
.md64.7 KB · 98%
At a glance
- Access
- Public
- Created
- Apr 29, 2026
- Updated
- Apr 29, 2026
- SHA
- 074febc9
Model card
From the Hugging Face model README
AI in Cargo & Logistics — Comprehensive White Paper
📄 Overview
A comprehensive, research-backed white paper on Artificial Intelligence in Cargo & Logistics, covering the full spectrum of AI applications transforming global supply chains.
Published: April 2026 | Citations: 33 sources (24 peer-reviewed papers, 9 industry reports/datasets)
📑 Contents
| Section | Topic |
|---|---|
| §1 | Executive Summary |
| §2 | The AI Imperative in Logistics |
| §3 | Market Landscape & Economic Impact ($20B+ market) |
| §4 | Core AI Technologies (ML, NLP, CV, RL, Digital Twins) |
| §5 | Intelligent Route Optimization & Fleet Management |
| §6 | Predictive Maintenance for Logistics Fleets |
| §7 | Warehouse Automation & Robotics |
| §8 | Supply Chain Visibility & Disruption Forecasting |
| §9 | Computer Vision for Cargo Security & Inspection |
| §10 | Last-Mile Delivery & Autonomous Systems |
| §11 | Demand Forecasting & Inventory Management |
| §12 | Generative AI & Agentic Systems |
| §13 | Data Infrastructure & Benchmark Datasets |
| §14 | Implementation Framework & Best Practices |
| §15 | Ethical Considerations & Responsible AI |
| §16 | Challenges & Barriers to Adoption |
| §17 | Future Outlook: 2026–2030 |
| §18 | Conclusions & Strategic Recommendations |
| §19 | References |
🔬 Key Findings (with evidence)
| Domain | AI Performance | Source |
|---|---|---|
| Route Optimization | 1.8% gap to optimal in 23s (vs. 7h for OR-Tools) | arXiv:2503.16159 |
| Predictive Maintenance | AUC-ROC 0.973, 100% event detection on real fleet | arXiv:2603.13343 |
| Supply Chain Disruption Forecasting | 80% calibration improvement, outperforms GPT-5 | arXiv:2604.01298 |
| Warehouse Pallet Detection | 99.5% mAP50 from synthetic-only data | arXiv:2503.22965 |
| Multi-Agent Warehouse | MARL scales to 15+ agents (vs. 8 for classical) | arXiv:2203.07092 |
| Cargo X-ray Inspection | 61.6 AP overall, 49.5 AP on hidden items | arXiv:2108.07020 |
📊 Referenced Datasets
| Dataset | HuggingFace Hub | Scale |
|---|---|---|
| LaDe (Last-Mile Delivery) | Cainiao-AI/LaDe | 10.67M packages |
| AIS Vessel Tracking | eyesofworld/AIS_Dataset | Real maritime AIS |
| Supply Chain Orders | alalfi/SupplyChainDataset | 53 columns |
| Supply Chain Analysis | Fayza2023/supply_chain_analysis | 24 columns |
📖 Read the Full Paper
➡️ whitepaper.md — The complete white paper with all sections, data tables, architecture diagrams, and references.
📜 License
This white paper is released under Creative Commons Attribution 4.0 International (CC BY 4.0).
- Created
- Apr 29, 2026
- Updated
- Apr 29, 2026