Multimodal Ai for Smart Transportation Systems

To train participants in designing and deploying multimodal AI systems that combine computer vision, natural language processing, geospatial analysis, and real-time sensor fusion for building intelligent, adaptive, and efficient transportation systems.

• Introduce participants to multimodal AI principles and toolkits

• Enable fusion of heterogeneous data sources for decision-making

• Promote innovation in congestion reduction, public safety, and autonomous navigation

• Demonstrate real-world smart transport use cases through hands-on labs

• Foster collaboration between AI technologists and transport professionals

📅 Day 1 – Fusing Traffic CCTV, GPS, and V2X Streams

• 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

📅 Day 2 – Spatio-Temporal Graph Networks for Congestion Prediction

• 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

📅 Day 3 – Decision Support for Adaptive Signalling

• 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

• Learn to process and integrate multimodal data streams for real-time insights

• Build AI models that combine visual, spatial, and linguistic inputs

• Predict traffic trends, detect anomalies, and automate transport decision flows

• Develop a complete prototype of an AI-enabled smart transportation system

• Receive certification acknowledging your expertise in multimodal AI for mobility

• AI/ML and computer vision professionals

• Transportation engineers and urban planners

• Researchers in mobility, logistics, and autonomous systems

• Public policy and smart city innovation stakeholders

• Students with technical backgrounds in CS, EE, or civil engineering