Chapter
Aug 12, 2020
Studying on Demand Prediction of Shared Bikes Considering Land-Use Information
Publication: CICTP 2020
ABSTRACT
In order to provide better service, shared bikes should be dynamically scheduled according to demand. Currently, research on demand prediction can be divided into two categories: traditional statistical models and deep learning models. The former aims at short-term prediction of 5–10 min, but scheduling cannot respond that quickly. The latter ignores influential factors, leading to a decline in accuracy and reliability. Therefore, this paper aims to develop a demand forecasting model that can significantly improve accuracy for long-term prediction with higher reliability. Taking the region within the inner ring of Pudong, Shanghai as the study area, an improved LSTM NN model is proposed, based on POI data which is represented land-use information. The improved model takes land-use information as a factor that influences demand of shared bikes. Improved LSTM NN demonstrates higher accuracy than both statistical models and the original LSTM NN, especially for long-term prediction.
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© 2020 American Society of Civil Engineers.
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Published online: Aug 12, 2020
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1Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji Univ., P.O. Box 201804, Shanghai, China. Email: [email protected]
2Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji Univ., P.O. Box 201804, Shanghai, China. Email: [email protected]
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ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.
Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.