Abstract
The presented study comprised the employement of a neural network (NN) algorithm, radial basis function (RBF), for the purpose of daily trip flow forecasting in Istanbul Metropolitan Area. The RBF NN predictions were quite close to the observations as reflected in the selected performance criteria.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
McLeod, W. T. Hanks P. (eds.), (1986) “The New Collins Concise Dictionary of the English Language,” William Collins, Sons & Co., Glasgow.
Ahmed S. A., Cook A. R. (1979) “Analysis of Freeway Traffic Time Series Data by Using Box-Jenkins Techniques,” Transportation Research Record 722, Transportation Research Board, Washington, D.C., pp 1–9
Smith B. L., Demetsky M. J. (1994) “Short-term Traffic Flow Prediction: Neural Network Approach,” Transportation Research Record 1453, Transportation Research Board, Washington, D.C., pp 98–104
Stamatiadis C., Taylor W. (1994) “Travel Time Predictions for Dynamic Route Guidance with a Recursive Adaptive Algorithm,” Proceedings of 73 rd Annual Meeting of Transportation Research Board, Washington, D.C.
Lint V., Hoogendoorn S. P., Zuylen H. J. (2002) “Freeway Travel Time Prediction with State-Space Neural Networks,” Proceedings of 81 st Annual Meeting of Transportation Research Board, Washington D.C., 2002
Yasdi R. (1999) “Prediction of Road Traffic using a Neural Network Approach,” Neural Computing and Applications, vol 8, no 2: 135–142
Zhang H. M. (2000) “Recursive Prediction of Traffic Conditions With Neural Network Models,” ASCE Journal of Transportation Engineering, Vol. 126, No 6: 472–481
Zhang H. M., Ritchie S. G., Lo Z. P. (2000) “Macroscopic Modeling of Freeway Traffic using an Artificial Neural Network,” Transportation Research Record 1588, TRB, National Research Council, Washington, D.C., pp 110–119
Ishak S., Kotha P. (2002) “Optimization of Dynamic Neural Network Performance for Short-Term Traffic Predictions,” Proceedings of 81 st Annual Meeting of Transportation Research Board, Washington D.C.
Cigizoglu H. K. (2003a) “Incorporation of ARMA models into flow forecasting by artificial neural networks,” Environmetrics, 14(4): 417–427
Cigizoglu H. K. (2003b) “Estimation, forecasting and extrapolation of flow data by artificial neural networks,” Hydrological Sciences Journal, 48(3):349–361
Maier H. R., Dandy G. C. (2000) “Neural network for the prediction and forecasting of water resources variables: a review of modeling issues and applications,” Environmental Modeling and Software, 15: 101–124
ASCE Task Committee (2000a) “Artificial neural networks in Hydrology I,” ASCE Journal of Hydrologic Engineering, 5(2): 115–123
Park J., Sandberg I. W. (1991) “Universal approximation using radial basis function networks,” Neural Comput., Vol. 3, No 2: 246–257
Poggio T., Girosi F. (1990) “Networks for approximation and learning,” Proc. IEEE, Vol. 78, pp 1481–1497
Powell M. J. D. (1987) “Radial Basis Function for Multivariate Interpolation,” In: Mason J. C., Cox M. G., (eds) A Review Algorithms for the Approximation of Functions and Data. Oxford, U.K.: Clarendon
Sudheer K. P., Gosain A. K., Ramasastri K. S. (2002) “A data-driven algorithm for constructing artificial neural network rainfall-runoff models,” Hydrological Processes, 16: 1325–1330
Box G. E. P., Jenkins G. M. (1976) “Time Series Analysis, Forecasting and Control,” Holden Day Inc., San Francisco, California
Celikoglu H. B., Akad M. (2003) “Estimation of Public Transport Trips by Feed Forward Back Propagation Neural Networks; A Case Study for Istanbul,” Proceedings of 8 th Online World Conference on Soft Computing in Industrial Applications (WSC8), 29th September–10th October, Fakultät für Elektrotechnik und Informationstechnik, Dortmund, Germany
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Celikoglu, H.B. (2005). Radial Basis Function Neural Network Approach to Estimate Public Transport Trips in Istanbul. In: Abraham, A., Dote, Y., Furuhashi, T., Köppen, M., Ohuchi, A., Ohsawa, Y. (eds) Soft Computing as Transdisciplinary Science and Technology. Advances in Soft Computing, vol 29. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32391-0_11
Download citation
DOI: https://doi.org/10.1007/3-540-32391-0_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25055-5
Online ISBN: 978-3-540-32391-4
eBook Packages: EngineeringEngineering (R0)