Data-Driven Decision-Making for Sustainable Cities

Aligned with CIVIS Hub 5, this 4 ECTS Blended Intensive Programme (BIP) explores how digital tools and artificial intelligence (AI) can be used to improve urban mobility and support sustainable development. The programme helps students understand how data, algorithms, and simulation technologies can make cities more efficient while respecting ethical and legal standards. 

By combining technical, legal, and social perspectives, the course shows how digital innovation can be applied to real problems such as traffic congestion, pollution, and citizen engagement. Through practical works and case studies, students learn to use technology for improving urban environments, reflecting the Hub's goal of promoting responsible digital transformation in society.

The curriculum is divided into four interdisciplinary modules:

• Module 1: Urban Traffic Modeling - covers network definition, traffic demand modeling, and data-driven calibration using SUMO.
  
• Module 2: Data-Driven Decision Support - focuses on modeling in low-data contexts, privacy-preserving techniques, and evaluating "what-if" scenarios.
  
• Module 3: Ethical and Technical Challenges - addresses algorithmic fairness, differential privacy, and ethical dilemmas in smart city deployments.
  
• Module 4: Governance and the Social Perspective - examines the components of urban mobility, policy impacts, and Mobility-as-a-Service (MaaS).

Implementation Schedule

• Virtual Component (part 1): 29 March – 2 April, 2027
  
• Physical Component: 5 – 9 April, 2027
  
• Virtual Component (part 2): 12 – 16 April 16, 2027

The BIP aims to provide students with the necessary competences to understand and address the challenges of designing and managing sustainable mobility systems in urban environments. The main objectives are to:

• provide an interdisciplinary understanding of mobility systems combining technical, legal, and ethical perspectives;
• develop participants’ ability to apply artificial intelligence (AI) and data-driven tools to optimize urban transportation while minimizing environmental impact;
• foster awareness of the socio-political dynamics of urban mobility and the need for citizen engagement in mobility planning;
• build competence in assessing the legal and ethical implications of AI-based mobility solutions, including compliance with the EU Data Act, GDPR, and AI Act;
• encourage collaboration and problem-solving through real-world case studies, with Brussels serving as a practical example of urban mobility challenges.

Upon successful completion of the BIP, participants will be able to:

• explain the fundamental concepts of urban mobility, intermodal transport, and sustainable mobility strategies;
• describe key methodologies for traffic data collection, modeling, calibration, and simulation;
• understand the regulatory framework applicable to AI-based mobility solutions, including data protection, AI ethics, and citizen rights;
• identify the socio-technical challenges associated with the deployment of smart mobility systems in urban environments.

The physical part will cover Modules 2 and 4 (see attached detailed program). This includes:

• lectures, workshops, and field visits;
• practical sessions on data analysis, traffic modeling, and ethical/ legal discussions;
• group project development and presentation.

The virtual component of the BIP includes both preparatory and follow-up activities. Before physical mobility, sessions, students will take part in an online phase with teaching sessions introducing the main concepts of sustainable urban mobility, AI applications.

After the in-person phase, a follow-up virtual period will allow students to continue developing their group projects, receive online mentoring, introducing legal-ethical frameworks, and finalize their reports and presentations. This stage consolidates learning from the mobility period.

Assessment in the BIP will focus on the practical application of knowledge and teamwork. Students will be evaluated through three main components:

• Group project (50%): teams will design and analyze a mobility scenario using simulation tools and real or synthetic data. This assesses technical skills, collaboration, and the ability to apply concepts to real cases.
• Oral presentation (30%): each group will present their results, showing their understanding of sustainable mobility, AI applications, and legal or ethical implications.
• Individual report (20%): each student will submit a short written reflection on what they learned.

These methods align with the learning outcomes by testing students’ ability to combine theory and practice, use analytical tools, and think critically about policy and ethics.

Feedback will be provided continuously during workshops and after presentations. Instructors will review draft work and support students' work during group sessions. Final feedback will be given after the project presentations.

Motivation Letter

Level of english (According to CEFR)

CV

Students will be selected based on the relevance of their background, CV, motivation, and overall alignment with the program's goals.

Dimitris Sacharidis is an Assistant Professor at the Data Science and Engineering Lab at the Université Libre de Bruxelles (ULB), focusing his research on data science, data engineering, and responsible AI. 

Carl Mörch serves as the Co-director of FARI (AI for the Common Good Institute) at ULB-VUB, where he specializes in the concrete application of ethical principles and the responsible development of technologies in society. 

Davide Andrea Guastella, an Associate Professor in the Machine Learning Group at Aix-Marseille University, brings specialized expertise in smart mobility initiatives, traffic simulation, and mobility data analysis. 

Isaac Taylor, an Associate Professor in Practical Philosophy at Stockholm University and a former Research Fellow at the Alan Turing Institute, investigates the moral and political implications of AI, particularly how it affects responsibility and democratic governance.