Multimodal Ai for Smart Transportation Systems

Introduction to multimodal data in intelligent transportation systems (ITS)

Overview of traffic data sources: CCTV footage, GPS traces, and V2X (vehicle-to-everything) communication

Data fusion techniques: early, late, and hybrid fusion approaches

Synchronization and preprocessing of heterogeneous data streams

Object detection and tracking from CCTV using deep learning

GPS-based trajectory extraction and analysis

V2X communication data: protocols and use cases

Real-time data pipelines for multimodal integration

Understanding traffic as a dynamic spatio-temporal graph

Basics of Graph Neural Networks (GNNs)

Temporal dynamics with Recurrent and Transformer models

Building spatio-temporal graph neural networks (ST-GNNs)

Feature engineering for nodes (intersections) and edges (roads)

Modeling congestion patterns and hotspot detection

Dataset sources: METR-LA, PeMS, OpenTraffic

Evaluation metrics: MAE, RMSE, MAPE for prediction models

Introduction to adaptive traffic signal control systems

AI-based decision-making frameworks for urban mobility

Reinforcement learning for signal phase and timing optimization

Integrating predictions into real-time decision engines

Dashboard interfaces for city traffic managers

Multi-objective optimization: delay, emissions, throughput

Case studies: AI-powered signal control in smart cities

Challenges: scalability, safety, latency, and governance