Model-Predictive Control of Traffic Emissions in Port-City Environments

In this paper, we present a method, based on model predictive control (MPC), to reduce the impact of pollutant emissions in contexts where a port is located within a city. To this purpose, we first introduce a dynamic model of the interactions between truck flows generated by the port and general mobility traffic in the shared urban infrastructure at the port-city interface. In order to keep track of the multiclass and complex nature of the system, the model takes advantage of microsimulation and deep learning for the prediction of road network traffic and related pollutant emissions. Then, we define a MPC control scheme exploiting the proposed model, to be used in real time to maintain the emissions levels below a certain threshold by appropriately adjusting traffic inflows from the port to the city, which represent the controls optimized by the MPC procedure. A simulation case study, involving the port of Genova in north-west Italy, is presented to showcase the ability of the proposed MPC scheme to control emissions in the shared area, also in complex situations such as transitions to mobility rush hours.