Future urban mobility systems

Summary

This course introduces future urban mobility systems from a data-driven perspective, covering mobility data ecosystems, sensing technologies, information extraction, traffic state and performance analysis, forecasting, control, active sensing, digital twins, and emerging smart-mobility applications.

Content

Keywords

Smart Cities; Urban mobility; Mobility data; Traffic forecasting; Drones

Learning Prerequisites

Required courses

No mandatory prerequisite specified

Recommended courses

Fundamentals of traffic operations and control (CIVIL-457); Traffic engineering (CIVIL-349)

Important concepts to start the course

Traffic flow fundamentals; transport systems; probability/statistics; geospatial data; Python/data analysis; basic machine learning concepts.

Learning Outcomes

By the end of the course, the student must be able to:

• Explain data ecosystems and sensing technologies for future urban mobility systems.
• Apply data cleaning, validation and machine learning methods to urban mobility datasets for traffic analysis and forecasting.
• Analyze trajectory, sensor and environmental data to estimate urban mobility related indicators.
• Design data-driven insights and decision-support concepts for future mobility systems and digital twins.
• Assess / Evaluate forecasting, deep learning and reinforcement learning approaches for mobility analysis and control.

Transversal skills

• Plan and carry out activities in a way which makes optimal use of available time and other resources.
• Set objectives and design an action plan to reach those objectives.
• Use both general and domain specific IT resources and tools
• Access and evaluate appropriate sources of information.
• Make an oral presentation.

Teaching methods

Lectures with slides; invited lecture; in-class discussions and demonstrations; hands-on computational/data exercises; project work with real urban mobility datasets.

Expected student activities

Attend lectures, participate in exercises and discussions, analyze real mobility datasets, complete hands-on work, collaborate on the project, and present final results.

Assessment methods

Midterm assessment; hands-on project work/reports or notebooks; final project presentation. Weighting and detailed format to be confirmed.

Supervision

Resources

Virtual desktop infrastructure (VDI)

No

Bibliography

• Barmpounakis, E., & Geroliminis, N. (2020). On the new era of urban traffic monitoring with massive drone data: The pNEUMA large-scale field experiment. Transportation research part C: emerging technologies, 111, 50-71.
• Mahajan, V., Barmpounakis, E., Alam, M. R., Geroliminis, N., & Antoniou, C. (2023). Treating Noise and Anomalies in Vehicle Trajectories From an Experiment With a Swarm of Drones. IEEE Transactions On Intelligent Transportation Systems, 24 (9), 9055-9067.
• Espadaler-Clapés, J., Barmpounakis, E., & Geroliminis, N. (2023). Traffic congestion and noise emissions with detailed vehicle trajectories from UAVs. Transportation Research Part D: Transport and Environment, 121, 103822.
• Barmpounakis, M., Espadaler-Clapés, J., Tsitsokas, D., Mordan, T., & Geroliminis, N. (2025). A New Perspective on Urban Mobility Through Large-Scale Drone Experiments for Smarter, Sustainable Cities. Drones, 9(9), 637.
• Xu, M., & Geroliminis, N. (2026). Knowledge-aware path planning for UAV parcel delivery and road monitoring. Transportation Research Part C: Emerging Technologies, 190, 105780.

Moodle Link

• https://go.epfl.ch/CIVIL-415