Passenger Flow Prediction Based on Newly Adopted Algorithms - Pekel, Engin; Kara, Selin Soner (TAYLOR & FRANCIS INC, 2017)
Passenger flow forecasting is an essential part of transportation systems. Neural networks in the transportation field have been applied to passenger demand prediction. In this paper, we developed two hybrid methods, known as parlimentary optimization algorithm-artificial neural network (POA-ANN), and intelligent water drops algorithm-ANN (IWD algorithm-ANN). In addition, we applied the proposed algorithms to illustrate the effect of precise prediction for passenger queues. We mainly focus on predicting passenger demand by comparing the genetic algorithm-ANN (GA-ANN) with POA-ANN and IWD-ANN. The results of prediction methods suggest that both POA-ANN and IWD-ANN provide a better forecasting performance, which is obtained via mean square error (MSE), than GA-ANN in the field of passenger flow prediction. This study illustrates that the newly adopted algorithms exhibit good performance for passenger prediction.
Keywords: ARRIVAL TIME PREDICTION; WATER DROPS ALGORITHM; GENETIC-ALGORITHM; NEURAL-NETWORK; GLOBAL OPTIMIZATION; REGRESSION; FRAMEWORK; QUALITY; SYSTEMS; Computer Science; Engineering