Prediction of Railway Passenger Traffic Volume Based on Neural Tree Model

The railway passenger traffic volume (RPTV) forecast can offer scientific basis for the establishment of policy and making of transportation development plan. This paper applies the neural tree model for predicting the railway passenger traffic volume. The optimal structure is developed using the Improved Probabilistic Incremental Program Evolution (IPIPE) and the free parameters encoded in the optimal tree are optimized by the Particle Swarm Optimization (PSO) algorithm, and an improved sigmoid function is applied as the neural activation function, a new fitness function combines error and Occam´s razor is used for for balancing of accuracy and parsimony of evolved structures. Based on the RPTV from 1985 to 2007 of China, the performance and efficiency of the applied model are evaluated and compared with the multi-layer feed-forward network (MLFN) and support vector machine (SVM).