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- W656444769 abstract "One of the most important issues in traffic control is driver behavior while on the road and the reaction to specific perscriptive signals. Driver behavior can have a great impact on both traffic safety and on flow fluidity, and it can lead to a capacity reduction of an intersection. This reduction, giving rise to spillback phenomenon, may lead to congestion. This paper discusses the development of a methodology to construct models of the relationships between driver behavior, in particular, incorrect behavior, and specific conditions in traffic flow, within the environment and the requirements of the highway code. Considered are factors such as vehicle driven, sex, age of driver, and driving habits. The tool used was multilayered feedforward artificial neural networks with backpropagation learning. Three models have been implemented and they are discussed in some detail." @default.
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- W656444769 date "1998-01-01" @default.
- W656444769 modified "2023-09-23" @default.
- W656444769 title "A METHODOLOGY FOR MODELLING DRIVER BEHAVIOUR IN SIGNALIZED URBAN INTERSECTIONS USING ARTIFICIAL NEURAL NETWORKS. IN: NEURAL NETWORKS IN TRANSPORT APPLICATIONS" @default.
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