2026-06-01T10:46:19Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/302012026-02-03T11:50:33Zcom_10630_2254col_10630_37959
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
García-Nieto, José Manuel
author
Ferrer-Urbano, Francisco Javier
author
Alba-Torres, Enrique
2024-02-08T16:50:01Z
2024-02-08T16:50:01Z
2014-07
https://hdl.handle.net/10630/30201
In last years, enhancing the vehicular traffic flow becomes a mandatory task to minimize the impact of polluting emissions and unsustainable fuel consumption in our cities. Smart Mobility optimisation emerges then, with the goal of improving the traffic management in the city. With this aim, we propose in this paper an optimisation strategy based on swarm intelligence to find efficient cycle programs for traffic lights deployed in large urban areas. In concrete, in this work we focus on the improvement of the traffic flow with the global purpose of reducing contaminant emissions (CO 2 and NO x ) and fuel consumption in the analyzed areas. For the sake of standardization, we follow European Union reference framework for traffic emissions, called HandBook Emission FActors (HBEFA). As a case study, we have concentrated in two extensive urban areas in the cities of Malaga and Seville (in Spain). After several comparisons between different optimisation techniques (Differential Evolution and Random Search), as well as other solutions provided by experts, our proposal is shown to obtain significant reductions of fuel consumption and gas emissions.
spa
Redes neuronales (Informática)
Optimising traffic lights with metaheuristics: Reduction of car emissions and consumption
conference output
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URL
https://riuma.uma.es/bitstreams/5687219a-448a-47e1-b74b-5d408e559a39/download
File
MD5
1872c82757f0286292eebfb6733adad7
2704468
application/pdf
Optimising traffic lights with metaheuristics.pdf