In this paper, railway passenger flows are analyzed and a suitable modeling method proposed. Based on historical data composed from monthly passenger counts realized on Serbian railway network it is concluded that the time series has a strong autocorrelation of seasonal characteristics. In order to deal with seasonal periodicity, Seasonal AutoRegressive Integrated Moving Average (SARIMA) method is applied for fitting and forecasting the time series that spans over the January 2004-June 2014 periods. Experimental results show good prediction performances. Therefore, developed SARIMA model can be considered for forecasting of monthly passenger flows on Serbian railways.

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In this paper we propose a fuzzy neural network prediction approach based on metaheuristics for container flow forecasting. The approach uses fuzzy if-then rules for selection between two different heuristics for developing neural network architecture, simulated annealing and genetic algorithm, respectively. These non-parametric models are compared with traditional parametric ARIMA technique. Time series composed from monthly container traffic observations for Port of Barcelona are used for model developing and testing. Models are compared based on the most important criteria for performance evaluation and for each of the data sets (total container traffic, loaded, unloaded, transit and empty) the appropriate model is selected.