Applied AI in transportation is the art of using AI to solve transportation problems. It involves using the different AI concepts and developing different programs, applications and software that solve real-world problems. It is a combination of interdisciplinary expertise in subject areas such as transportation/urban planning, mathematics/statistics, and computer science/IT.
The course content is structured around models/algorithms, practical Python exercises, and real projects in transportation: AI models and learning algorithms, tutorials on Python implementation using TensorFlow, and AI applications in transportation projects.
During AI models and learning algorithms, you will learn: Conventional machine learning models (supervised and unsupervised learning, such as regression, classification, text mining, clustering, and PCA), deep learning models (such as neural networks, convolutional neural networks, transfer learning), and reinforcement learning models (such as deep Q learning).
During the training sessions you will have: Two hours of practical training with two parts. Part I - Instructed tutorial to illustrate learned algorithms in the lecture (data and code provided). Part II - Individual practice and Q&A with the teaching assistants to solve the practice tasks.
During AI applications in transportation projects, we will present real projects and share our experiences/lessons on using AI in practice. For example, optimization (robust scheduling), prediction (real-time prediction in public transport), and inference (estimation of traffic conditions).