Forecasting the Number of Passengers on Hungarian Railway Routes Using a Similarity and Fuzzy Arithmetic-Based Inference Method

In this study, we present a similarity and fuzzy arithmetic-based fuzzy inference method and show how effectively it can be used to forecast the number of passengers on a railway route. We introduce a novel fuzzy similarity measure that is derived from the so-called epsilon function, which may be viewed as an alternative to the exponential function. After demonstrating the most important properties of the new similarity measure, we construct a fuzzy inference method that is founded on arithmetic operations over triangular fuzzy numbers. This inference method utilizes the proposed similarity measure to derive weight values for the above-mentioned arithmetic operations. The motivation behind the proposed method is twofold. On the one hand, we aim to construct a method that is simple and easy to implement. On the other hand, we intend to ensure that this method meets the practical requirements for rail passenger forecasts. Using a real-life case study, we demonstrate how well our method can predict the expected number of passengers on a new railway route based on characteristics of this relation. With respect to the studied case, we may conclude that although the similarity and fuzzy arithmetic-based fuzzy inference system has only two adjustable parameters, it may be regarded as a viable alternative to Sugeno-type fuzzy inference systems with a much greater number of adjustable parameters tuned by various optimization techniques.