Abstract
Dockless bike-sharing is becoming popular all over the world, and short-term spatiotemporal distribution forecasting on system state has been further enlarged due to its dynamic spatiotemporal characteristics. We employ a deep learning approach, named the convolutional long short-term memory network (conv-LSTM), to address the spatial dependences and temporal dependences. The spatiotemporal variables including number of bicycles in area, distribution uniformity, usage distribution, and time of day as a spatiotemporal sequence in which both the input and the prediction target are spatiotemporal 3D tensors within one end-to-end learning architecture. Experiments show that conv-LSTM outperforms LSTM on capturing spatiotemporal correlations.
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17 July 2024
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s00521-024-10079-4
References
http://www.cityweekend.com.cn/shanghai/article/mobike-vs-mobike-lite-vs-ofo
Pidd H, Lavelle D (2017) Chinese bike-share scheme launches in rainy Manchester. The Guardian. Retrieved 6 July 2017
Mobike bicycle-hire scheme to be launched in Greater Manchester. BBC. 13 June 2017. Retrieved 6 July 2017
Cox, C (2017) Everything you need to know about the new Mobike service coming to Manchester and Salford. Manchester Evening News
Mobike launches in Sapporo. Shanghai Daily. Shanghai Daily. 23 August 2017. Retrieved 24 August 2017
Bai Y, Sun Z, Zeng B, Deng J, Li C (2017) A multi-pattern deep fusion model for short-term bus passenger flow forecasting. Appl Soft Comput 58:669–680
Ma Z, Xing J, Mesbah M, Ferreira L (2014) Predicting short-term bus passenger demand using a pattern hybrid approach. Transp Res Part C 39(39):148–163
Wei Y, Chen MC (2012) Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks. Transp Res Part C 21(1):148–162
Yang H, Leung CW, Wong SC, Bell MG (2010) Equilibria of bilateral taxi–customer searching and meeting on networks. Transp Res Part B 44:1067–1083
Yang H, Yang T (2011) Equilibrium properties of taxi markets with search frictions. Transp Res Part B 45:696–713
Ke JT, Zheng HY, Yang H et al (2017) Short-term forecasting of passenger demand under on-demand ride services: a spatio-temporal deep learning approach. Transp Res C Emerg Technol 85:591–608
Wu Y, Tan H (2016) Short-term traffic flow forecasting with spatial–temporal correlation in a hybrid deep learning framework. arXiv preprint arXiv:1612.01022
Zhang J, Zheng Y, Qi D (2016) Deep spatio-temporal residual networks for citywide crowd flows prediction. arXiv preprint arXiv:1610.00081
Shaheen S, Zhang H, Martin E, Guzman S (2011) China’s Hangzhou public bicycle: understanding early adoption and behavioral response to bikesharing. Transp Res Rec J Transp Res Board 2247:33–41
Caggiani L, Ottomanelli M (2013) A dynamic simulation based model for optimal fleet repositioning in bike-sharing systems. Proc Soc Behav Sci 87:203–210
Caggiani L, Ottomanelli M (2012) A modular soft computing based method for vehicles repositioning in bike-sharing systems. Proc Soc Behav Sci 54:675–684
Zeng D, Xu J, Gu J, Liu L, Xu G (2008) Short term traffic flow prediction based on online learning SVR. In: Workshop on power electronics and intelligent transportation system, p 616–620
Smith B, Demetsky M (1997) Traffic flow forecasting: comparison of modeling approaches. J Transp Eng 123(4):261–266
Sun Y, Leng B, Guan W (2015) A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system. Neurocomputing 166:109–121
Sapankevych N, Sankar R (2009) Time series prediction using support vector machines: a survey. IEEE Intell Syst 4(2):24–38
Yu H, Yang J, Han J, Li X (2005) Making SVMs scalable to large data sets using hierarchical cluster indexing. Data Min Knowl Disc 11(3):295–321
Zhao K, Khryashchev D, Freire J, Silva C, Vo H (2016) Predicting taxi demand at high spatial resolution: approaching the limit of predictability. In: IEEE international conference on big data
Lv Y, Duan Y, Kang W, Li Z, Wang F-Y (2015) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst 16(2):865–873
Huang W, Song G, Hong H, Xie K (2014) Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans Intell Transp Syst 15:2191–2201
Graham B (2014) Spatially-sparse convolutional neural networks. Comput Sci 34(6):864–867
Ma X, Tao Z, Wang Y, Yu H, Wang Y (2015) Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp Res Part C Emerg Technol 54:187–197
Shi X, Chen Z, Wang H, Yeung DY, Wong WK, Woo WC (2015) Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in neural information processing systems, p 802–810
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This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s00521-024-10079-4
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Ai, Y., Li, Z., Gan, M. et al. RETRACTED ARTICLE: A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system. Neural Comput & Applic 31, 1665–1677 (2019). https://doi.org/10.1007/s00521-018-3470-9
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DOI: https://doi.org/10.1007/s00521-018-3470-9