Abstract
The paper presents the analysis of traffic data for determining classes of time series of traffic flow intensity for use in traffic forecasting employing neural networks. Data from traffic detectors on the main access road to the city of Gliwice in the period of past year is the basis for statistical analysis. Four classes of time series are proposed as representative of the traffic flow. The time series map temporarily smoothed detector counts. Different smoothing periods are used to retain the dynamic characteristics of the flows. A neural network is developed to classify incoming traffic data into the proposed time series classes. The specific time series implies a traffic control or management strategy, which indicates the capability of the NN to work out decisions for use in Intelligent Transportation Systems (ITS) applications.
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Pamuła, T. (2012). Traffic Flow Analysis Based on the Real Data Using Neural Networks. In: Mikulski, J. (eds) Telematics in the Transport Environment. TST 2012. Communications in Computer and Information Science, vol 329. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34050-5_41
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DOI: https://doi.org/10.1007/978-3-642-34050-5_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34049-9
Online ISBN: 978-3-642-34050-5
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