Digital Mobility Short Course
In this digital era, transportation systems are experiencing a fundamental transformation in their structures and organization, triggered namely by the pervasiveness of technologies and the development of the shared economy. They are more than ever demand-driven, thanks to the data-rich environment, and they rely more and more on artificial intelligence.
This one-week program aims at providing transport professionals and researchers with the knowledge and skills that are necessary to understand and master the opportunities and challenges of digitalization in transport.
The course will cover the following topics:
- Demand models, including activity-based models;
- Demand forecasting, including calculation of indicators;
- Mobility as a service, including planning and design of large-scale shared mobility systems;
- Big data and traffic management for multimodal systems, including an introduction on optimization and control of on-demand systems;
- Artificial intelligence, including introduction to machine learning, and deep learning, forecasting models for mobility data;
- Computational thinking for digital twin, including introduction to data science and sensing technology;
- Laboratories with real and simulated data in a computer environment for the topics above. (Basic knowledge of the Python programming language is recommended).
Who should attend?
The course is designed for professionals (from industry and public authorities) and academic researchers (professors, researchers, PhD students), interested in transportation systems and mobility, such as managers, planners, economists, engineers, operations researchers.
It is assumed that participants have a basic knowledge of transport modeling, and data analysis.
Basic knowledge of the programming language Python is requested for the laboratories.