Open AccessShort‐term railway passenger demand forecast using improved Wasserstein generative adversarial nets and web search terms
Author(s) -
Feng Fenling,
Zhang Jiaqi,
Liu Chengguang,
Li Wan,
Jiang Qiwei
Publication year - 2021
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/itr2.12036
Subject(s) - adaptability , computer science , demand forecasting , generative model , data mining , generative grammar , engineering , artificial intelligence , operations research , ecology , biology
Accurately predicting railway passenger demand is conducive for managers to quickly adjust strategies. It is time‐consuming and expensive to collect large‐scale traffic data. With the digitization of railway tickets, a large amount of user data has been accumulated. We propose a method to predict railway passenger demand using web search terms data. In order to improve the prediction accuracy, we improved Wasserstein Generative Adversarial Nets (WGAN), which were good at generating and identifying data, by adding a predictor and supervised learning adversarial training to predict railway passenger demand. The improved WGAN could generate virtual data to expand real data, and use parallel data to predict railway passenger demand. We used search times of web search terms on different devices as training data to predict railway passenger demand in Beijing. The results show that the change in demand for railway passenger lags behind the change in the data of web search terms by one month. It is suitable for forecasting in advance. Compared with other forecasting methods, the improved WGAN performance is better, and the mean absolute percentage error is 1.98%. Because it can use mixed data for training and prediction, it has stronger adaptability when data scale decreases.