Model predictive control of port-city scenarios based on traffic simulation and efficient sampling design

The joint occupation of urban road infrastructures by logistic flows and general mobility traffic, typical of contexts in which a port is located within a city, is a known source of unwanted externalities such as congestion and pollution. In this paper we propose a model-predictive control scheme aimed at maximizing the throughput of vehicles into the zone where the two kinds of traffic interact, thus mitigating the impact of negative phenomena, as well as increasing the overall economic benefit of the port-city system. In order to provide the detailed representation of the network dynamics required by the predictive controller, we define a model supported by micro-simulation of the port-city area. Then, since the direct application of micro-simulation is not feasibile in real-time, we resort to a surrogate data-driven approximation to be used by the controller. To this purpose, we define a principled sampling design scheme aimed at yielding a suitable training set for the surrogate model, and provide a theoretical analysis on the conditions to guarantee an efficient covering of the relevant sets. A simulation case study involving the port-city context of Genova in north-west Italy is presented, in order to showcase the advantages of the proposed sampling scheme and the performance of the model-predictive controller as well.