Reducing Vehicle Emissions and Fuel Consumption in the City by Using Particle Swarm Optimization

Abstract

Nowadays in current cities the increasing levels of pollution emissions and fuel consumption derived from the road traffic directly affect to the air quality, the economy, and specially the health of citizens. Therefore, improving the traffic flow is a mandatory task in order to mitigate such critical problems. In this work, we propose a Swarm Intelligence approach for optimizing signal light timing programs in metropolitan areas. In this way, we can improve the traffic flow of vehicles with the global target of reducing their fuel consumption and gas emissions (CO and NOx). In this article we optimize the timing programs of signal lights and analyze their effect in pollution by following the standard HBEFA as traffic emission model. In concrete, we are focused here on two large and heterogeneous urban instances located in the cities of Malaga and Seville (in Spain). In comparison with timing programs of signal lights predefined by experts (close to real ones), our proposal obtains significant reductions in terms of the emission rate and the total fuel consumption.