Final Report Summary - METAFERW (Modeling and controlling traffic congestion and propagation in large-scale urban multimodal networks)
METAFERW aims to investigate where the collective traffic flow dynamics of sub-networks capture the main characteristics of traffic congestion, such as the evolution of space-mean flows and spatiotemporal propagation of densities in different regions of the city. This framework is utilized to introduce elegant effective hierarchical control strategies to improve mobility and decrease delays in large urban networks that local ones are unable to succeed. We develop the methodologies to model and understand the collective behavior for different types of urban systems, with emphasis in conflicts for the same road space. Our findings highlight under what physical properties the aggregated laws provide reasonable description of congestion for single- and multi-modal systems. The methodology integrates network level physical models with advanced clustering algorithms and proper control theory. Ultimately, the goal is to develop optimization tools and investigate what type of real-time active traffic management schemes (congestion pricing, vehicle restriction, large scale traffic signal control) can improve mobility measures in a city for cities of different structures. We build a hierarchical feedback control network of multiple levels. The validation of the modeling methodologies and the traffic management schemes are conducted in various and complex city structures scenarios using data from field experiments and micro-simulations.