{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T22:49:09Z","timestamp":1769208549863,"version":"3.49.0"},"reference-count":32,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,5,29]],"date-time":"2020-05-29T00:00:00Z","timestamp":1590710400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As a kind of transportation in a smart city, urban public bicycles have been adopted by major cities and bear the heavy responsibility of the \u201clast mile\u201d of urban public transportation. At present, the main problem of the urban public bicycle system is that it is difficult for users to rent a bike during peak h, and real-time monitoring cannot be solved adequately. Therefore, predicting the demand for bicycles in a certain period and performing redistribution in advance is of great significance for solving the lag of bicycle system scheduling with the help of IoT. Based on the HOSVD-LSTM prediction model, a prediction model of urban public bicycles based on the hybrid model is proposed by transforming the source data (multiple time series) into a high-order tensor time series. Furthermore, it uses the tensor decomposition technology (HOSVD decomposition) to extract new features (kernel tenor) from higher-order tensors. At the same time, these kernel tenors are directly used to train tensor LSTM models to obtain new kernel tenors. The inverse tensor decomposition and high-dimensional, multidimensional, and tensor dimensionality reduction were introduced. The new kernel tenor obtains the predicted value of the source sequence. Then the bicycle rental amount is predicted.<\/jats:p>","DOI":"10.3390\/s20113072","type":"journal-article","created":{"date-parts":[[2020,6,2]],"date-time":"2020-06-02T09:19:27Z","timestamp":1591089567000},"page":"3072","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Short-Term Rental Forecast of Urban Public Bicycle Based on the HOSVD-LSTM Model in Smart City"],"prefix":"10.3390","volume":"20","author":[{"given":"Dazhou","family":"Li","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang 110016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6811-3851","authenticated-orcid":false,"given":"Chuan","family":"Lin","sequence":"additional","affiliation":[{"name":"Key Laboratory for Ubiquitous Network and Service Software of Liaoning province, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang 110016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zihui","family":"Meng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang 110016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Song","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.bjp.2013.12.020","article-title":"SmartSantander: IoT experimentation over a smart city testbed","volume":"61","author":"Galache","year":"2014","journal-title":"Comput. Netw."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Arasteh, H., Hosseinnezhad, V., Loia, V., Tommasetti, A., Troisi, O., Shafie-Khah, M., and Siano, P. (2016, January 7\u201310). Iot-based smart cities: A survey. Proceedings of the 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC), Florence, Italy.","DOI":"10.1109\/EEEIC.2016.7555867"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1016\/j.procs.2015.05.122","article-title":"Smart City Architecture and its Applications Based on IoT","volume":"52","author":"Gaur","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Clohessy, T., Acton, T., and Morgan, L. (2014, January 8\u201311). Smart City as a Service (SCaaS): A Future Roadmap for E-Government Smart City Cloud Computing Initiatives. Proceedings of the 2014 IEEE\/ACM 7th International Conference on Utility and Cloud Computing, London, UK.","DOI":"10.1109\/UCC.2014.136"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.sbspro.2014.10.025","article-title":"Smart City Logistics on Cloud Computing Model","volume":"151","author":"Nowicka","year":"2014","journal-title":"Procedia Soc. Behav. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"17576","DOI":"10.1109\/ACCESS.2017.2731382","article-title":"SmartCityWare: A Service-Oriented Middleware for Cloud and Fog Enabled Smart City Services","volume":"5","author":"Mohamed","year":"2017","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bao, J., He, T., Ruan, S., Li, Y., and Zheng, Y. (2017, January 22\u201327). Planning Bike Lanes based on Sharing-Bikes\u2019 Trajectories. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining\u2014KDD\u201917, Halifax, NS, Canada.","DOI":"10.1145\/3097983.3098056"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yang, Z., Hu, J., Shu, Y., Cheng, P., Chen, J., and Moscibroda, T. (2016, January 26\u201330). Epub Mobility Modeling and Prediction in Bike-Sharing Systems. Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services Companion\u2014MobiSys\u201916 Companion, Singapore.","DOI":"10.1145\/2906388.2906408"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhang, D., Wang, L., Yang, D., Ma, X., Li, S., Wu, Z., Pan, G., Nguyen, T.-M.-T., and Jakubowicz, J. (2016, January 12\u201316). Dynamic cluster-based over-demand prediction in bike sharing systems. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing\u2014UbiComp\u201916, New York, NY, USA.","DOI":"10.1145\/2971648.2971652"},{"key":"ref_10","unstructured":"Singhvi, D., Singhvi, S., Frazier, P.I., Henderson, S.G., O\u2019Mahony, E., Shmoys, D.B., and Woodard, D.B. (2016, January 25\u201330). Predicting bike usage for new york city\u2019s bike sharing system. Proceedings of the Workshops at the twenty-ninth AAAI conference on artificial intelligence, Austin, TX, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1016\/j.physa.2018.09.123","article-title":"Understanding bike sharing travel patterns: An analysis of trip data from eight cities","volume":"515","author":"Kou","year":"2019","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1080\/0740817X.2013.770186","article-title":"Optimal inventory management of a bike-sharing station","volume":"45","author":"Raviv","year":"2013","journal-title":"IIE Trans."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.omega.2013.12.001","article-title":"The bike sharing rebalancing problem: Mathematical formulations and benchmark instances","volume":"45","author":"Hadjicostantinou","year":"2014","journal-title":"Omega"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Qiu, L.-Y., and He, L.-Y. (2018). Bike Sharing and the Economy, the Environment, and Health-Related Externalities. Sustainability, 10.","DOI":"10.3390\/su10041145"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1002\/net.21736","article-title":"Full-load route planning for balancing bike sharing systems by logic-based benders decomposition","volume":"69","author":"Raidl","year":"2017","journal-title":"Networks"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Caggiani, L., Camporeale, R., and Ottomanelli, M. (2017, January 26\u201328). A real time multi-objective cyclists route choice model for a bike-sharing mobile application. Proceedings of the 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Naples, Italy.","DOI":"10.1109\/MTITS.2017.8005593"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.trc.2017.03.016","article-title":"Free-floating bike sharing: Solving real-life large-scale static rebalancing problems","volume":"80","author":"Pal","year":"2017","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhao, J., Li, Y., Jia, H., Jian, H., and Cai, J. (2019, January 6\u20138). Study on Allocation Scheme of Bicycle Sharing without Piles. Proceedings of the CICTP 2019, Nanjing, China.","DOI":"10.1061\/9780784482292.140"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.trb.2016.11.003","article-title":"A hybrid large neighborhood search for the static multi-vehicle bike-repositioning problem","volume":"95","author":"Ho","year":"2017","journal-title":"Transp. Res. Part B Methodol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Goh, C.Y., Yan, C., and Jaillet, P. (2019). Estimating Primary Demand in Bike-sharing Systems. SSRN Electron. J.","DOI":"10.2139\/ssrn.3311371"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, J., Sun, L., Chen, W., and Xiong, H. (2016, January 13\u201317). Rebalancing Bike Sharing Systems. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining\u2014KDD\u201916, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939776"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1007\/s00521-018-3470-9","article-title":"A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system","volume":"31","author":"Ai","year":"2018","journal-title":"Neural Comput. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.trc.2018.07.013","article-title":"The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets","volume":"95","author":"Xu","year":"2018","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, C., Zhang, L., Liu, Y., and Yang, X. (2018, January 4\u20137). Short-term Prediction of Bike-sharing Usage Considering Public Transport: A LSTM Approach. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569726"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1016\/j.procs.2019.01.217","article-title":"Predicting bike sharing demand using recurrent neural networks","volume":"147","author":"Pan","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.trpro.2018.11.029","article-title":"Short-term prediction for bike-sharing service using machine learning","volume":"34","author":"Wang","year":"2018","journal-title":"Transp. Res. Procedia"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1007\/s10618-012-0280-z","article-title":"Tensor factorization using auxiliary information","volume":"25","author":"Narita","year":"2012","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2980","DOI":"10.1109\/TIT.2010.2046205","article-title":"Matrix Completion From a Few Entries","volume":"56","author":"Keshavan","year":"2010","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s12532-012-0044-1","article-title":"Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm","volume":"4","author":"Wen","year":"2012","journal-title":"Math. Program. Comput."},{"key":"ref_30","unstructured":"Liu, Y., Shang, F., Fan, W., Cheng, J., and Cheng, H. (2014). Generalized higher-order orthogonal iteration for tensor decomposition and completion. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1109\/TPAMI.2012.39","article-title":"Tensor Completion for Estimating Missing Values in Visual Data","volume":"35","author":"Liu","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1956","DOI":"10.1137\/080738970","article-title":"A Singular Value Thresholding Algorithm for Matrix Completion","volume":"20","author":"Cai","year":"2010","journal-title":"SIAM J. 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