Abstract
The prediction of the traffic data is a vital requirement for advanced traffic management and traffic information systems, which aim to influence the traveler behaviors, reducing the traffic congestion, improving the mobility and enhancing the air quality. Both the stochastic time series (TS) techniques and artificial intelligent (AI) techniques can be used for this aim. Daily traffic demand in Second Tolled Bridge of Bosphorus, which has an important role in urban traffic networks of Istanbul has been predicted by both a TS approach using an autoregressive (AR) model, and an AI approach using an artificial neural network (ANN) model. The results have shown that the prediction error obtained by ANN model is smaller than the error obtained by AR model. The results have also pointed out that many other transportation data prediction studies can be implemented easily and successfully by using the developed ANN simulator.
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References
Faraway, J.: Time series forecasting with neural networks, a comparative study using the airline data. Applied Statistics, Royal Statistical Society, UK, 231–250 (1998)
Box, G.E.P., Jenkins, G.: Time Series Analysis, Forecasting and Control, Golden-Day Publishing Company, San Francisco, CA, 20–35 (1970)
General Directorate Of Highways. Traffic and Transportation Survey 2000-2004. GDOH, Ankara, Turkey (2004)
Hipel, K.W., Mcleod, A.I.: Time Series Modeling of Water Resources and Environmental Systems, pp. 14–25. Elsevier, Amsterdam (1994)
De Gooijer, J.G., Kumar, K.: Some recent developments in non-linear time series modeling, testing, and forecasting. International Journal of Forecasting 8, 135–156 (1992)
SPSS For WINDOWS ver.10, Web site: http://www.spssscience.com
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Topuz, V. (2007). Traffic Demand Prediction Using ANN Simulator. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74819-9_106
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DOI: https://doi.org/10.1007/978-3-540-74819-9_106
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74817-5
Online ISBN: 978-3-540-74819-9
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