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Current interest in short-term traffic volume forecasting focuses on incorporating temporal and spatial volume characteristics in the forecasting process. This article addresses the problem of integrating and optimizing predictive information from multiple locations of an urban signalized arterial and proposes a modular neural predictor consisting of temporal genetically optimized structures of multilayer perceptrons (MLP) that are fed with volume data from sequential locations to improve the accuracy of short-term forecasts. The results show that the proposed methodology provides more accurate forecasts compared to the conventional statistical methodologies applied, as well as to the static forms of neural networks.

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