A novel Kumaraswamy interval type-2 TSK fuzzy logic system for subway passenger demand prediction

Fuzzy logic systems (FLSs) are proper tools for learning and predicting of real-world problems. Type-2 fuzzy sets are developments of the conventional type-1 fuzzy sets which are applied for prediction problems with uncertainty. Interval type-2 fuzzy logic system (IT2 FLSs) is the most wildly used type-2 FLS due to its efficiency and simplicity. Passenger demand prediction has a crucial role in the public transportation sector. Because of the nonlinearity and instability of the passenger arrivals prediction, IT2 FLS can be an appropriate method for solving this problem. In this paper, we develop a fuzzy logic system named KIT2 TSK for passenger arrivals prediction in subway stations. In our proposed model, we utilize the Kumaraswamy distribution in the construction of an IT2 TSK FLS. Furthermore, we develop a new input selection measure that applies the Schweizer–Sklar t-conorm operator in the variable selection process. The flexibility of the Kumaraswamy distribution leads to the ability to approximate several distributions using the same equation by different values for its shape parameters. Utilizing this property, we adopt our proposed model for passenger arrivals prediction of one line of the Tehran subway system as a case study. Moreover, to see the results on unusual days, passenger demand on public holidays, weekends, and special events are also taken into account. The results demonstrate that our proposed methodology has better performance in the hourly prediction of passenger arrivals compared to the benchmarks. The results for the chaotic Mackey-Glass problem also show the superiority of our proposed model.