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학술대회 The Empirical Evaluation of Models Predicting Bike Sharing Demand
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저자
최승한, 한미경
발행일
202010
출처
International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1560-1562
DOI
https://dx.doi.org/10.1109/ICTC49870.2020.9289176
초록
Most bike sharing system has an imbalance problem in certain time zones and certain rental stations where bicycles are insufficient or overloaded. So, a demand forecasting model is required to solve this problem. In this paper, we evaluate the performance applying the machine learning, neural network model with the bicycle demand dataset collected from the bicycle sharing system in actual operation in order to develop a model that predicts bicycle demand information for choosing a proper demand forecasting model. From the results, the neural network models outperform the machine learning models.
키워드
Bike Sharing Demand, GRU, LSTM, RandomForest, XGBoost
KSP 제안 키워드
Bicycle sharing system, Bike Sharing System, Demand forecasting model, Empirical Evaluation, Imbalance problem, Time zone, actual operation, machine learning models, neural network model