Applying Deep Learning Technology for Spatiotemporal Prediction of Air Pollution from Urban Mobile Sources - Couverture rigide
Synopsis
Mobile source emissions account for more than 80% of carbon monoxide and hydrocarbons and more than 90% of nitrogen oxides and solid particles in urban air pollutants. Also, mobile source emissions have become the main source of urban air pollution, causing serious damage to the social ecological environment. Therefore, it is necessary to study the comprehensive supervision and analysis methods of urban mobile source emissions, which is of great significance for protecting public health and improving rational urban planning as well as traffic conditions. Meanwhile, the temporal and spatial distribution of urban mobile source emissions is affected by many complex factors. On the one hand, from the perspective of long-term vehicle emission inventory calculation, it mainly depends on the city's total vehicle volume and vehicle type composition. On the other hand, in terms of short-term and real-time variation in traffic emissions, it is mainly influenced by urban road network topology, traffic flow conditions, and external meteorological factors. This series of factors has led to great challenges in achieving full-time monitoring and comprehensive supervision of urban mobile source emissions. By summarizing the existing literature, we find that the focus of mobile source emissions prediction tends to shift from the road segment level to urban region scale, from a single city to cross cities, and from macro inventory prediction to fine-grained instantaneous prediction.
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Applying Deep Learning Technology for Spatiotemporal Prediction of Air Pollution from Urban Mobile Sources (Hardcover)
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Hardcover. Etat : new. Hardcover. Mobile source emissions account for more than 80% of carbon monoxide and hydrocarbons and more than 90% of nitrogen oxides and solid particles in urban air pollutants. Also, mobile source emissions have become the main source of urban air pollution, causing serious damage to the social ecological environment. Therefore, it is necessary to study the comprehensive supervision and analysis methods of urban mobile source emissions, which is of great significance for protecting public health and improving rational urban planning as well as traffic conditions. Meanwhile, the temporal and spatial distribution of urban mobile source emissions is affected by many complex factors. On the one hand, from the perspective of long-term vehicle emission inventory calculation, it mainly depends on the city's total vehicle volume and vehicle type composition. On the other hand, in terms of short-term and real-time variation in traffic emissions, it is mainly influenced by urban road network topology, traffic flow conditions, and external meteorological factors. This series of factors has led to great challenges in achieving full-time monitoring and comprehensive supervision of urban mobile source emissions. By summarizing the existing literature, we find that the focus of mobile source emissions prediction tends to shift from the road segment level to urban region scale, from a single city to cross cities, and from macro inventory prediction to fine-grained instantaneous prediction. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9783725863303
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Applying Deep Learning Technology for Spatiotemporal Prediction of Air Pollution from Urban Mobile Sources (Hardcover)
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Hardcover. Etat : new. Hardcover. Mobile source emissions account for more than 80% of carbon monoxide and hydrocarbons and more than 90% of nitrogen oxides and solid particles in urban air pollutants. Also, mobile source emissions have become the main source of urban air pollution, causing serious damage to the social ecological environment. Therefore, it is necessary to study the comprehensive supervision and analysis methods of urban mobile source emissions, which is of great significance for protecting public health and improving rational urban planning as well as traffic conditions. Meanwhile, the temporal and spatial distribution of urban mobile source emissions is affected by many complex factors. On the one hand, from the perspective of long-term vehicle emission inventory calculation, it mainly depends on the city's total vehicle volume and vehicle type composition. On the other hand, in terms of short-term and real-time variation in traffic emissions, it is mainly influenced by urban road network topology, traffic flow conditions, and external meteorological factors. This series of factors has led to great challenges in achieving full-time monitoring and comprehensive supervision of urban mobile source emissions. By summarizing the existing literature, we find that the focus of mobile source emissions prediction tends to shift from the road segment level to urban region scale, from a single city to cross cities, and from macro inventory prediction to fine-grained instantaneous prediction. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9783725863303
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Applying Deep Learning Technology for Spatiotemporal Prediction of Air Pollution from Urban Mobile Sources (Hardcover)
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Hardcover. Etat : new. Hardcover. Mobile source emissions account for more than 80% of carbon monoxide and hydrocarbons and more than 90% of nitrogen oxides and solid particles in urban air pollutants. Also, mobile source emissions have become the main source of urban air pollution, causing serious damage to the social ecological environment. Therefore, it is necessary to study the comprehensive supervision and analysis methods of urban mobile source emissions, which is of great significance for protecting public health and improving rational urban planning as well as traffic conditions. Meanwhile, the temporal and spatial distribution of urban mobile source emissions is affected by many complex factors. On the one hand, from the perspective of long-term vehicle emission inventory calculation, it mainly depends on the city's total vehicle volume and vehicle type composition. On the other hand, in terms of short-term and real-time variation in traffic emissions, it is mainly influenced by urban road network topology, traffic flow conditions, and external meteorological factors. This series of factors has led to great challenges in achieving full-time monitoring and comprehensive supervision of urban mobile source emissions. By summarizing the existing literature, we find that the focus of mobile source emissions prediction tends to shift from the road segment level to urban region scale, from a single city to cross cities, and from macro inventory prediction to fine-grained instantaneous prediction. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N° de réf. du vendeur 9783725863303
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Applying Deep Learning Technology for Spatiotemporal Prediction of Air Pollution from Urban Mobile Sources
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Buch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Mobile source emissions account for more than 80% of carbon monoxide and hydrocarbons and more than 90% of nitrogen oxides and solid particles in urban air pollutants. Also, mobile source emissions have become the main source of urban air pollution, causing serious damage to the social ecological environment. Therefore, it is necessary to study the comprehensive supervision and analysis methods of urban mobile source emissions, which is of great significance for protecting public health and improving rational urban planning as well as traffic conditions. Meanwhile, the temporal and spatial distribution of urban mobile source emissions is affected by many complex factors. On the one hand, from the perspective of long-term vehicle emission inventory calculation, it mainly depends on the city's total vehicle volume and vehicle type composition. On the other hand, in terms of short-term and real-time variation in traffic emissions, it is mainly influenced by urban road network topology, traffic flow conditions, and external meteorological factors. This series of factors has led to great challenges in achieving full-time monitoring and comprehensive supervision of urban mobile source emissions. By summarizing the existing literature, we find that the focus of mobile source emissions prediction tends to shift from the road segment level to urban region scale, from a single city to cross cities, and from macro inventory prediction to fine-grained instantaneous prediction. N° de réf. du vendeur 9783725863303
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