Modelling public transport trips by radial basis function neural networks

Artificial neural networks (ANNs) are one of the recently explored advanced technologies, which show promise in the area of transportation engineering. The presented study used two different ANN algorithms, feed forward back-propagation (FFBP) and radial basis function (RBF), for the purpose of daily trip flow forecasting. The ANN predictions were quite close to the observations as reflected in the selected performance criteria. The selected stochastic model performance was quite poor compared with ANN results. It was seen that the RBF neural network did not provide negative forecasts in contrast to FFBP applications. Besides, the local minima problem faced by some FFBP algorithms was not encountered in RBF networks.

[1]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[2]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[3]  Kenneth Levenberg.  A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .

[4]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[5]  Shing Chung Josh Wong,et al.  Urban traffic flow prediction using a fuzzy-neural approach , 2002 .

[6]  Susan Grant-Muller,et al.  Use of sequential learning for short-term traffic flow forecasting , 2001 .

[7]  Stephen G. Ritchie,et al.  Macroscopic Modeling of Freeway Traffic Using an Artificial Neural Network , 1997 .

[8]  George E. P. Box,et al.  Time Series Analysis: Forecasting and Control , 1977 .

[9]  Michael J Demetsky,et al.  SHORT-TERM TRAFFIC FLOW PREDICTION: NEURAL NETWORK APPROACH , 1994 .

[10]  E Watanabe,et al.  APPLICATIONS OF STATE ESTIMATION ALGORITHMS TO HOURLY TRAFFIC VOLUME SYSTEM , 1995 .

[11]  A. R. Cook,et al.  ANALYSIS OF FREEWAY TRAFFIC TIME-SERIES DATA BY USING BOX-JENKINS TECHNIQUES , 1979 .

[12]  S. Y. Yun,et al.  A Performance evaluation of neural network models in traffic volume forecasting , 1998 .

[13]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[14]  Mark Dougherty,et al.  A REVIEW OF NEURAL NETWORKS APPLIED TO TRANSPORT , 1995 .

[15]  N. Null.  Artificial Neural Networks in Hydrology. I: Preliminary Concepts , 2000 .

[16]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[17]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[18]  K. P. Sudheer,et al.  A data‐driven algorithm for constructing artificial neural network rainfall‐runoff models , 2002 .

[19]  H. M. Zhang,et al.  RECURSIVE PREDICTION OF TRAFFIC CONDITIONS WITH NEURAL NETWORK MODELS , 2000 .

[20]  Mascha C. van der Voort,et al.  Combining kohonen maps with arima time series models to forecast traffic flow , 1996 .

[21]  Hikmet Kerem Cigizoglu,et al.  Estimation, forecasting and extrapolation of river flows by artificial neural networks , 2003 .

[22]  Mark Dougherty,et al.  SHORT TERM INTER-URBAN TRAFFIC FORECASTS USING NEURAL NETWORKS , 1997 .

[23]  Sherif Ishak,et al.  OPTIMIZATION OF DYNAMIC NEURAL NETWORKS PERFORMANCE FOR SHORT-TERM TRAFFIC PREDICTION , 2003 .

[24]  Eil Kwon,et al.  Adaptive demand-diversion prediction for integrated control of freeway corridors , 1993 .

[25]  Ramin Yasdi.  Prediction of Road Traffic using a Neural Network Approach , 1999, Neural Computing & Applications.

[26]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[27]  Patrick Hanks,et al.  The New Collins concise dictionary of the English language , 1982 .

[28]  Dimitri Lefebvre,et al.  Optimal neural networks architectures for the flow-density relationships of traffic models , 2002, Math. Comput. Simul..

[29]  H. K. Cigizoglu,et al.  Incorporation of ARMA models into flow forecasting by artificial neural networks , 2003 .