Infering Air Quality from Traffic Data Using Transferable Neural Network Models
| Autor: | Benjamin N. Passow, Miguel A. Molina-Cabello, David Elizondo, Jolanta Obszynska, Enrique Domínguez |
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| Rok vydání: | 2019 |
| Předmět: |
010504 meteorology & atmospheric sciences
Artificial neural network SIMPLE (military communications protocol) Computer science Calidad del aire Urban environments 010501 environmental sciences computer.software_genre Pollution 01 natural sciences Neural network Redes neuronales (Informática) Work (electrical) Order (exchange) Air quality Process information Data mining State (computer science) computer Air quality index Polución 0105 earth and related environmental sciences |
| Zdroj: | Advances in Computational Intelligence ISBN: 9783030205201 IWANN (1) RIUMA. Repositorio Institucional de la Universidad de Málaga instname |
| Popis: | This work presents a neural network based model for inferring air quality from traffic measurements. It is important to obtain information on air quality in urban environments in order to meet legislative and policy requirements. Measurement equipment tends to be expensive to purchase and maintain. Therefore, a model based approach capable of accurate determination of pollution levels is highly beneficial. The objective of this study was to develop a neural network model to accurately infer pollution levels from existing data sources in Leicester, UK. Neural Networks are models made of several highly interconnected processing elements. These elements process information by their dynamic state response to inputs. Problems which were not solvable by traditional algorithmic approaches frequently can be solved using neural networks. This paper shows that using a simple neural network with traffic and meteorological data as inputs, the air quality can be estimated with a good level of generalisation and in near real-time. By applying these models to links rather than nodes, this methodology can directly be used to inform traffic engineers and direct traffic management decisions towards enhancing local air quality and traffic management simultaneously. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. |
| Databáze: | OpenAIRE |
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