Aim & Scope

AI Foundations for Transportation Systems
- Novel machine learning architectures for traffic analysis and prediction  
- Deep learning approaches for autonomous vehicle perception and control  
- Reinforcement learning frameworks for dynamic transportation optimization  

Adaptive and Continual Learning in Intelligent Transportation Systems (ITS)
- Lifelong learning systems for evolving transportation networks  
- Incremental learning techniques for real-time traffic data processing  
- Catastrophic forgetting mitigation in long-term transportation models  

Intelligent Mobility Optimization 
- Real-time model adaptation for changing traffic conditions  
- Resource-efficient algorithms for edge computing in vehicles and infrastructure  
- Scalable AI solutions for large-scale urban mobility systems  

Knowledge Transfer and Multitask Learning  
- Cross-domain transfer learning for diverse transportation scenarios  
- Unified architectures for simultaneous traffic prediction and control  
- Adaptive knowledge reuse in heterogeneous transportation networks  

Emerging Applications  
- Autonomous and connected vehicle technologies  
- Smart infrastructure and V2X communication systems  
- AI-powered solutions for sustainable urban mobility  
- Predictive maintenance and fault detection in transportation assets  
- Human-centric AI for enhanced transportation user experiences  

Security and Reliability 
- Robust AI systems for safety-critical transportation applications  
- Cybersecurity in connected and autonomous vehicle ecosystems  
- Privacy-preserving techniques for mobility data analysis  

Future-AITS welcomes high-quality theoretical contributions, applied research, and case studies that push the boundaries of AI in transportation. The journal particularly encourages submissions that demonstrate real-world implementations, scalability assessments, and interdisciplinary approaches to intelligent transportation challenges.