Traffic flow evolution effects to nitrogen dioxides predictability in large metropolitan areas

Traffic flow evolution effects to nitrogen dioxides predictability in large metropolitan areas

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Article ID: iaor20113510
Volume: 16
Issue: 4
Start Page Number: 273
End Page Number: 280
Publication Date: Jun 2011
Journal: Transportation Research Part D
Authors: , ,
Keywords: heuristics: genetic algorithms, neural networks
Abstract:

A genetically‐optimized modular neural network is used to predict the temporal of nitrogen dioxides in a highly congested urban freeway by integrating, in a single prediction shell, information on past values of nitrogen dioxide and ozone, as well as traffic volume, travel speed and occupancy. Results indicate that the approach is more accurate for one and multiple steps ahead predictions when compared to a simple static neural network. They also indicate that the integration of traffic information in the process of prediction improves to some extent the predictability of nitrogen dioxides evolution. It is also shown that the look‐back time window for pollutants‐related data increases with relation to the increase of the prediction horizon.

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