AIM4Mobility: Predictive Modeling and Demand Forecasting

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A practical course on building predictive models for urban mobility using AI and machine learning. Learners will apply key techniques to a real bike-sharing dataset, blending theory and hands-on projects to tackle real-world mobility challenges.
PROFESSIONAL TRAINING COURSE

The future of urban mobility relies on data-driven decisions

Predictive Modeling and Demand Forecasting is a hands-on, self-paced course designed to equip professionals and students with the practical skills to build and apply predictive models in real mobility contexts.

Using machine learning and AI techniques, learners will explore the fundamentals of demand forecasting and work directly with real-world data from a bike-sharing system. Blending theory and practice, this course helps participants turn complex data into actionable insights for smarter, more responsive mobility systems.

Training Format

Self-paced course.

Duration

8 hours (considering theoretical and practical tasks)

Language

English

Certificate of completion

Upon course compeltion, each participant will recieve a diploma from the EIT Urban Mobility.

What will the participants get from the course

By the end of this course, learners will be able to:

Apply

machine learning and AI techniques to real-world urban mobility problems.

Build and evaluate

predictive models using urban mobility datasets.

Understand 

spatial clustering and geospatial machine learning methods for identifying mobility patterns and urban analysis.

MODULES

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AIM4Mobility is made up for 4 courses

Enrol in all four courses and get a +50% discount. More info >

Tools and Techniques for Geospatial ML

This course teaches geospatial data analysis with Python to tackle urban mobility challenges, using OSM data and spatial tools.

Fundamental Trends of AI & ML

The couse covers applications like demand forecasting, traffic optimisation, and autonomous vehicles, as well as key tools, challenges, and ethical issues.

Predictive Modeling and Demand Forecasting

This course teaches how to build and apply predictive models in urban mobility using AI and ML.


AI Challenges & Ethical Considerations

Introduction to ethical principles for AI in urban mobility, covering oversight, accountability, data governance, and fairness.

Want to learn more about the AIM4Mobility course?

Visit the main page of the course which explains the AIM4Mobility framework, format and detailed information of all four courses. 
This project is supported by EIT Urban Mobility, an initiative of the European Institute of Innovation and Technology (EIT), a body of the European Union. EIT Urban Mobility acts to accelerate positive change on mobility to make urban spaces more liveable. Learn more: eiturbanmobility.eu