Optimising traffic lights with metaheuristics: Reduction of car emissions and consumption

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86093031" target="_blank" >RIV/61989100:27240/14:86093031 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/IJCNN.2014.6889749" target="_blank" >http://dx.doi.org/10.1109/IJCNN.2014.6889749</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IJCNN.2014.6889749" target="_blank" >10.1109/IJCNN.2014.6889749</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Optimising traffic lights with metaheuristics: Reduction of car emissions and consumption

Popis výsledku v původním jazyce

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 (CO2 and NOx) 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. 2014 IEEE.

Název v anglickém jazyce

Optimising traffic lights with metaheuristics: Reduction of car emissions and consumption

Popis výsledku anglicky

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 (CO2 and NOx) 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. 2014 IEEE.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the International Joint Conference on Neural Networks

ISBN

978-1-4799-1484-5

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

48-54

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

New York

Místo konání akce

Beijing

Datum konání akce

6. 7. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000371465700008