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- W3131974906 abstract "Machine learning models have been widely adopted for passenger flow prediction in urban metros; however, the authors find machine learning models may underperform under anomalous large passenger flow conditions. In this study, they develop a prediction framework that combines the advantage of complex network models in capturing the collective behaviour of passengers and the advantage of online learning algorithms in characterising rapid changes in real-time data. The proposed method considerably improves the accuracy of passenger flow prediction under anomalous conditions. This study can also serve as an exploration of interdisciplinary methods for transportation research." @default.
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- W3131974906 date "2020-12-01" @default.
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- W3131974906 title "Hybrid model for predicting anomalous large passenger flow in urban metros" @default.
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- W3131974906 doi "https://doi.org/10.1049/iet-its.2020.0054" @default.
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