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Conference Paper The Empirical Evaluation of Models Predicting Bike Sharing Demand
Cited 8 time in scopus Share share facebook twitter linkedin kakaostory
Authors
Seung-Han Choi, Mi-Kyung Han
Issue Date
2020-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1560-1562
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC49870.2020.9289176
Abstract
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.
KSP Keywords
Bicycle sharing system, Demand forecasting model, Imbalance Problem, Neural network model, Time zone, actual operation, bike sharing system, empirical evaluation, machine learning models, neural network(NN)