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- W2891336692 abstract "Accurate forecast of railway passenger volume makes policy formulation effective and transportation resource allocation reasonable. With the popularity of the Internet, more and more people choose to use Internet to get information related to travel. Therefore, this paper puts forward to taking web search as training data to predict railway passenger volume. In addition, adversarial nets (AN) are proposed to predict railway passenger volume. The AN training consists of two steps in which the first step is adversarial training and the second is fine tuning. Through the unsupervised adversarial training, the initial parameters of the neural network are optimized and its generalization ability is increased. Then supervised fine tuning makes AN have ability to predict railway passenger volume. Besides, in order to optimize the parameters of AN, an improved particle swarm optimization algorithm is proposed. The experimental results show that the proposed model has better performance." @default.
- W2891336692 created "2018-09-27" @default.
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- W2891336692 date "2018-01-01" @default.
- W2891336692 modified "2023-09-26" @default.
- W2891336692 title "Railway Passenger Volume Forecast Based on Web Search Terms and Adversarial Nets" @default.
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- W2891336692 doi "https://doi.org/10.1007/978-3-030-00009-7_2" @default.
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