Graph convolutional network approach applied to predict hourly bike-sharing demands considering spatial, temporal, and global effects
Autoři:
Tae San Kim aff001; Won Kyung Lee aff001; So Young Sohn aff001
Působiště autorů:
Department of Industrial Engineering, Yonsei University, Shinchon-dong, Seoul, Republic of Korea
aff001
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0220782
Souhrn
Solving the supply–demand imbalance is the most crucial issue for stable implementation of a public bike-sharing system. This gap can be reduced by increasing the accuracy of demand prediction by considering spatial and temporal properties of bike demand. However, only a few attempts have been made to account for both features simultaneously. Therefore, we propose a prediction framework based on graph convolutional networks. Our framework reflects not only spatial dependencies among stations, but also various temporal patterns over different periods. Additionally, we consider the influence of global variables, such as weather and weekday/weekend to reflect non-station-level changes. We compare our framework to other baseline models using the data from Seoul’s bike-sharing system. Results show that our approach has better performance than existing prediction models.
Klíčová slova:
Biology and life sciences – Physical sciences – Engineering and technology – Research and analysis methods – Neuroscience – Computer and information sciences – Mathematics – Statistics – Mathematical and statistical techniques – Statistical methods – Transportation – Earth sciences – Atmospheric science – Structural engineering – Built structures – Neural networks – Artificial intelligence – Machine learning – Deep learning – Management engineering – Decision analysis – Decision trees – Decision tree learning – Meteorology – Rain
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