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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Optimising traffic lights with metaheuristics: Reduction of car emissions and consumption
Original language description
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.
Czech name
—
Czech description
—

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the International Joint Conference on Neural Networks
ISBN
978-1-4799-1484-5
ISSN
—
e-ISSN
—
Number of pages
7
Pages from-to
48-54
Publisher name
Institute of Electrical and Electronics Engineers
Place of publication
New York
Event location
Beijing
Event date
Jul 6, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000371465700008

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