Passenger flow forecasting of Zhengzhou Metro Line one based on an improved whale optimization algorithm

With the continuous expansion of urban scale in China, the increase of passenger flow has brought great pressure to the urban public transport system. An accurate and timely prediction of the short-term passenger flow at each metro station is extremely important for the metro intelligent control system to make a timely decision. In this paper, based on the measured passenger flow data of Zijingshan station of Zhengzhou Metro Line 1, an improved whale optimization algorithm is proposed to predict the passenger flow on different time scales. The results show that the method has higher accuracy than the traditional least squares support vector machines algorithm. The paper opens a new window for nowcasting warning in the rush hour and long-period optimization of the public transport.