Journal
IET INTELLIGENT TRANSPORT SYSTEMS
Volume 14, Issue 14, Pages 1987-1996Publisher
WILEY
DOI: 10.1049/iet-its.2020.0054
Keywords
learning (artificial intelligence); transportation; complex networks; rail traffic; real-time systems; behavioural sciences; transportation; real-time data; online learning algorithms; collective behaviour; urban metros; hybrid model; anomalous conditions; passenger flow prediction; complex network models; prediction framework; anomalous large passenger flow conditions; machine learning models
Funding
- National Natural Science Foundation of China [71871224]
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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.
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