| Autor: |
Nelson L. S. da Fonseca, Lucas Zanco Ladeira, Oscar J. Ciceri, Matheus Ferraroni Sanches, Islene C. Garcia, Maria Vitoria R. Oliveira, Leandro A. Villas |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
DCOSS |
| DOI: |
10.1109/dcoss52077.2021.00032 |
| Popis: |
The demographic growth in cities has increased carbon dioxide (CO2) emissions, a significant problem that challenges societies worldwide. The CO2 emission raises the pollution levels causing health risks for the people and contributes to climate change. Moreover, the transportation sector is responsible for 20.6% of CO2 emissions. Thus, it is necessary to reduce vehicle fuel consumption to minimize CO2 emissions. This paper proposes a system based on artificial intelligence techniques: a fuzzy controller and a neural network to find the instantaneous speed, which reduces vehicle fuel consumption. The proposed system employs only vehicle and highway information, which means communication between vehicles is not required. The simulation scenario comprises a loaded truck traveling through a highway with slopes based on a data set. Results derived from simulation show that both techniques produce a lower fuel consumption than the standard Simulation of Urban MObility (SUMO) algorithm. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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