Hierarchical Control of congestion in smart cities with big multi-sensor data Nikos Geroliminis École Polytechnique Fédérale de Lausanne (EPFL) Our research tackles the problem of modeling and optimization in large-scale congested traffic networks in a holistic way with an aggregated realistic representation of traffic dynamics and route choice and multiple modes of transport. This talk will talk about the integration of big multi-sensor data, the understanding of multimodal patterns, the coordination and optimization of urban efficiency for the travel of people. This is challenging because cities are highly complex systems. This seminar will describe methodologies to model and understand the collective behavior for different types of urban systems. It will highlight under what physical properties the aggregated laws will provide reasonable description of congestion for single- and multi-modal systems. It will also describe how to develop hierarchical feedback control and optimization tools and investigate what type of real-time active traffic management schemes (vehicle restriction, large scale traffic signal control) can improve mobility measures in a city for cities of different structures. The validation of the methodologies and the traffic management schemes are conducted in various and complex city structures scenarios using data from large field experiments and detailed simulations.