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- W3207823215 abstract "Subway passenger flow prediction is of great significance in transportation planning and operation. Special events, as for vocal concerts and sports games, lead large-scaled passenger flow with few periodic trends. Therefore, predicting subway outbound flow during events is a challenging task. In recent years, social media has been used for socio-economic forecasting. Correlation analysis shows that the trend of social media volume can be used for passenger flow prediction under events occurrences. In this paper, besides traditional smart card data, we incorporate social media data into passenger flow prediction. The multivariate disturbance-based hybrid deep neural network (MDB-HDNN), which models the disturbances of the inbound flow from the nearby stations and the social media post trends, is proposed for subway passenger flow prediction during events. Experimental results on three real-world datasets demonstrate that the MDB-HDNN performs well under various settings and has better robustness. Our findings and results can provide decision support for schedule formulation and passenger flow guidance." @default.
- W3207823215 created "2021-10-25" @default.
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- W3207823215 date "2022-02-01" @default.
- W3207823215 modified "2023-10-18" @default.
- W3207823215 title "Forecasting the subway passenger flow under event occurrences with multivariate disturbances" @default.
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- W3207823215 doi "https://doi.org/10.1016/j.eswa.2021.116057" @default.
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