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 N Ox). 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.

Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

“…A second class of methods deals with optimizing cycle length under both saturated and oversaturated intersection conditions [23,24]. The third group of methods are utilized to optimize other performance measures like pollutant emissions, fuel consumptions by adjusting the cycle length [25][26][27]. The past decade has witnessed rapid development in information and communication technologies such as fourth generation (4G), dedicated short range communication (DSRC), Internet of Things (IOT) sensors that provide unprecedented opportunities for detailed data collection.…”

Spatiotemporal Evolution Analysis of NO 2 Column Density before and after COVID-19 Pandemic in Henan Province Based on SI-APSTE Model

“…The spatiotemporal evolution of air pollution is similar to the generation, migration, aggregation and dissipation of particle swarm. Combining with the SI algorithm such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) 27,28 , the RA objects that emission pollution can be reversely inferred according to the CA pollution state, so as to achieve the purpose of pollution sources identification. The pollutant concentrations D PS (s, t) of cellular CA(s, t) meet the following requirements:…”

A Case Study on Computer-Based Analysis of the Stochastic Stability of Mechanical Structures Driven by White and Colored Noise: Utilizing Artificial Intelligence Techniques to Design an Effective Active Suspension System

“…In 2015, the performance, emission, and combustion characteristics of a single-cylinder, four-stroke variable compression ratio engine were predicted with the aid of ANN by Muralidharan and Vasudevan [6]. As an intelligent technique, particle swarm optimization has been adopted by Olivera et al [7] to reduce vehicle emissions and fuel consumption in the city. In 2016, Lotfan et al [8] combined ANN and nondominated sorting genetic algorithm II to model and reduce CO and NOx emissions from a direct injection dual-fuel engine.…”