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: 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:

Research and analysis methods – Mathematical and statistical techniques – Statistical methods – Decision analysis – Decision trees – Decision tree learning – Physical sciences – Mathematics – Statistics – Computer and information sciences – Artificial intelligence – Machine learning – Deep learning – Engineering and technology – Management engineering – Structural engineering – Built structures – Transportation – Earth sciences – Atmospheric science – Meteorology – Rain – Biology and life sciences – Neuroscience – Neural networks


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PLOS One


2019 Číslo 9

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