{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T07:10:19Z","timestamp":1778915419976,"version":"3.51.4"},"reference-count":44,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61602141"],"award-info":[{"award-number":["61602141"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2019,1]]},"abstract":"<jats:p>Bike\u2010sharing is a new low\u2010carbon and environment\u2010friendly mode of public transport based on the \u201csharing economy\u201d. Since 2017, the bike\u2010sharing market has boomed in China\u2019s major cities. Bikes equipped with GPS transmitters are docked along sidewalks that can be easily accessed through smartphone apps. However, this new form of transport has also led to problems, such as illegal parking, vandalism, and theft, each of which presents a major administrative challenge. Further, imbalances in user demand and bike availability need to be overcome to ensure a convenient, flexible service for customers. Hence, predicting a cyclist\u2019s destination could be of great importance to shared\u2010bike operators. In this paper, we propose an innovative deep learning model to predict the most probable destination for each user. The model, called destination prediction network based on spatiotemporal data (DPNst), comprises three steps. First, the data is preprocessed and a pool of likely candidate destinations is generated based on frequent item mining. This candidate set is then used to build the DPNst model: a long short\u2010term memory network learns the user\u2019s behavior; a convolutional neural network learns the spatial relationships between the origin and the candidate destinations; and a fully connected neural network learns the external features. In the final step, DPNst dynamically aggregates the output of the three neural networks based on the given data and generates the predictions. In a series of experiments on real\u2010world stationless bike\u2010sharing data, DPNst returned an F1 score of 42.71% and demonstrated better performance overall than the compared baselines.<\/jats:p>","DOI":"10.1155\/2019\/7643905","type":"journal-article","created":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T23:31:44Z","timestamp":1546385504000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["A Destination Prediction Network Based on Spatiotemporal Data for Bike\u2010Sharing"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1510-3972","authenticated-orcid":false,"given":"Jian","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4121-1587","authenticated-orcid":false,"given":"Fei","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6681-9209","authenticated-orcid":false,"given":"Jin","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hang","family":"Lv","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1371-5801","authenticated-orcid":false,"given":"Jia","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2019,1]]},"reference":[{"key":"e_1_2_12_1_2","unstructured":"https:\/\/www.ft.com\/content\/5efe95f6-0aeb-11e7-97d1-5e720a26771b."},{"key":"e_1_2_12_2_2","doi-asserted-by":"crossref","unstructured":"BaoJ. HeT. RuanS. LiY. andZhengY. Planning bike lanes based on sharing-bikes\u2032 trajectories Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD 2017 August 2017 Canada 1377\u20131386 2-s2.0-85029124970.","DOI":"10.1145\/3097983.3098056"},{"key":"e_1_2_12_3_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_12_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_2_12_5_2","doi-asserted-by":"publisher","DOI":"10.1093\/imamci\/7.1.77"},{"key":"e_1_2_12_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/2743025"},{"key":"e_1_2_12_7_2","doi-asserted-by":"publisher","DOI":"10.1023\/B:DAMI.0000005258.31418.83"},{"key":"e_1_2_12_8_2","doi-asserted-by":"crossref","unstructured":"ZhangJ. ZhengY. andQiD. Deep spatio-temporal residual networks for citywide crowd flows prediction Proceedings of the 31st AAAI Conference on Artificial Intelligence AAAI 2017 February 2017 USA 1655\u20131661 2-s2.0-85028459149.","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"e_1_2_12_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-49430-8_2"},{"key":"e_1_2_12_10_2","article-title":"Adam: A Method for Stochastic Optimization","author":"Kingma D. P.","year":"2014","journal-title":"Computer Science"},{"key":"e_1_2_12_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/78.650093"},{"key":"e_1_2_12_12_2","doi-asserted-by":"crossref","unstructured":"AltcheF.andde La FortelleA. An LSTM network for highway trajectory prediction Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) October 2017 Yokohama 353\u2013359 https:\/\/doi.org\/10.1109\/ITSC.2017.8317913.","DOI":"10.1109\/ITSC.2017.8317913"},{"key":"e_1_2_12_13_2","unstructured":"CuiZ. KeR. andWangY. Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction 2018."},{"key":"e_1_2_12_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2018.03.076"},{"key":"e_1_2_12_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"e_1_2_12_16_2","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-173443"},{"key":"e_1_2_12_17_2","unstructured":"https:\/\/biendata.com\/competition\/mobike\/data\/."},{"key":"e_1_2_12_18_2","unstructured":"http:\/\/www.cma.gov.cn\/2011qxfw\/2011qsjgx\/."},{"key":"e_1_2_12_19_2","unstructured":"https:\/\/www.tensorflow.org\/."},{"key":"e_1_2_12_20_2","doi-asserted-by":"publisher","DOI":"10.5038\/2375-0901.12.4.3"},{"key":"e_1_2_12_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/2560188"},{"key":"e_1_2_12_22_2","article-title":"The Role of Smart Bike-sharing Systems in Urban Mobility","volume":"2","author":"Midgley P.","year":"2009","journal-title":"Journeys"},{"key":"e_1_2_12_23_2","doi-asserted-by":"crossref","unstructured":"SmithS. L. PavoneM. SchwagerM. FrazzoliE. andRusD. Rebalancing the rebalancers: Optimally routing vehicles and drivers in mobility-on-demand systems Proceedings of the 1st American Control Conference ACC 2013 June 2013 Washington DC USA IEEE 2362\u20132367 2-s2.0-84883535872.","DOI":"10.1109\/ACC.2013.6580187"},{"key":"e_1_2_12_24_2","unstructured":"FroehlichJ. NeumannJ. andOliverN. Sensing and Predicting the Pulse of the City through Shared Bicycling Proceedings of the 21st international jont conference on Artifical intelligence 2009 Pasadena California USA Morgan Kaufmann Publishers Inc. 1420\u20131426."},{"key":"e_1_2_12_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2010.07.002"},{"key":"e_1_2_12_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sbspro.2011.08.058"},{"key":"e_1_2_12_27_2","volume-title":"Balancing A Dynamic Public Bike-Sharing System","author":"Contardo C.","year":"2012"},{"key":"e_1_2_12_28_2","doi-asserted-by":"publisher","DOI":"10.1051\/ro\/2011102"},{"key":"e_1_2_12_29_2","doi-asserted-by":"publisher","DOI":"10.1142\/S0219525911002950"},{"key":"e_1_2_12_30_2","doi-asserted-by":"crossref","unstructured":"VogelP.andMattfeldD. C. Strategic and Operational Planning of Bike-Sharing Systems by Data Mining \u2013 A Case Study 6971 Proceedings of the International Conference on Computational Logistics Hamburg Germany Springer Berlin Heidelberg 127\u2013141 https:\/\/doi.org\/10.1007\/978-3-642-24264-9_10.","DOI":"10.1007\/978-3-642-24264-9_10"},{"key":"e_1_2_12_31_2","doi-asserted-by":"crossref","unstructured":"YoonJ. W. PinelliF. andCalabreseF. Cityride: A predictive bike sharing journey advisor Proceedings of the 13th International Conference on Mobile Data Management MDM 2012 July 2012 Karnataka India IEEE 306\u2013311 https:\/\/doi.org\/10.1109\/MDM.2012.16 2-s2.0-84870731703.","DOI":"10.1109\/MDM.2012.16"},{"key":"e_1_2_12_32_2","doi-asserted-by":"crossref","unstructured":"LiY. ZhengY. ZhangH. andChenL. Traffic prediction in a bike-sharing system Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems 2015 Seattle WA USA ACM https:\/\/doi.org\/10.1145\/2820783.2820837.","DOI":"10.1145\/2820783.2820837"},{"key":"e_1_2_12_33_2","doi-asserted-by":"crossref","unstructured":"ZhangJ. PanX. LiM. andYuP. S. Bicycle-Sharing System Analysis and Trip Prediction Proceedings of the 2016 17th IEEE International Conference on Mobile Data Management (MDM) June 2016 Porto Portugal IEEE 174\u2013179 https:\/\/doi.org\/10.1109\/MDM.2016.35.","DOI":"10.1109\/MDM.2016.35"},{"key":"e_1_2_12_34_2","doi-asserted-by":"publisher","DOI":"10.1007\/s007790200035"},{"key":"e_1_2_12_35_2","doi-asserted-by":"crossref","unstructured":"PattersonD. J. LiaoL. FoxD. andKautzH. Inferring high-level behavior from low-level sensors Proceedings of the 5th International Conference on Ubiquitous Computing 2003 Seattle WA USA Springer 73\u201389 https:\/\/doi.org\/10.1007\/978-3-540-39653-6_6.","DOI":"10.1007\/978-3-540-39653-6_6"},{"key":"e_1_2_12_36_2","doi-asserted-by":"crossref","unstructured":"TiesyteD.andJensenC. S. Similarity-based prediction of travel times for vehicles traveling on known routes Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems ACM GIS 2008 November 2008 Irvine CA USA ACM 105\u2013114 2-s2.0-70449702195.","DOI":"10.1145\/1463434.1463452"},{"key":"e_1_2_12_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2010.08.004"},{"key":"e_1_2_12_38_2","doi-asserted-by":"crossref","unstructured":"ZhangL. HuT. MinY. WuG. ZhangJ. FengP. GongP. andYeJ. A taxi order dispatch model based on combinatorial optimization Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD 2017 August 2017 Canada 2151\u20132159 2-s2.0-85029094784.","DOI":"10.1145\/3097983.3098138"},{"key":"e_1_2_12_39_2","unstructured":"TangF. ZhuJ. CaoY. MaS. ChenY. HeJ. HuangC. ZhaoG. andTangY. PARecommender: A pattern-based system for route recommendation Proceedings of the 25th International Joint Conference on Artificial Intelligence IJCAI 2016 July 2016 USA 4272\u20134273 2-s2.0-85006141228."},{"key":"e_1_2_12_40_2","article-title":"Dynamic Routing Between Cap- sules","author":"Sabour S.","year":"2017","journal-title":"NIPS Proceedings"},{"key":"e_1_2_12_41_2","volume-title":"Convolutional LSTM Network: A Machine Learning Appro- ach for Precipitation Nowcasting","author":"Shi X.","year":"2015"},{"key":"e_1_2_12_42_2","volume-title":"Urban Computing: Concepts, Methodologies, and Appli- cations , Acm Transactions on Intelligent Systems Technology 5.3:1-55","author":"Zheng Y.","year":"2014"},{"key":"e_1_2_12_43_2","doi-asserted-by":"crossref","unstructured":"ZhengY. LiuY. YuanJ. andXieX. Urban computing with taxicabs Proceedings of the 13th International Conference on Ubiquitous Computing (UbiComp \u203211) September 2011 ACM 89\u201398 https:\/\/doi.org\/10.1145\/2030112.2030126 2-s2.0-80054052524.","DOI":"10.1145\/2030112.2030126"},{"key":"e_1_2_12_44_2","doi-asserted-by":"crossref","unstructured":"YuanJ. ZhengY. andXieX. Discovering regions of different functions in a city using human mobility and POIs Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD \u203212) August 2012 186\u2013194 https:\/\/doi.org\/10.1145\/2339530.2339561 2-s2.0-84866045445.","DOI":"10.1145\/2339530.2339561"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2019\/7643905.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2019\/7643905.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2019\/7643905","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T11:43:33Z","timestamp":1723031013000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2019\/7643905"}},"subtitle":[],"editor":[{"given":"Jianxin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2019,1]]},"references-count":44,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,1]]}},"alternative-id":["10.1155\/2019\/7643905"],"URL":"https:\/\/doi.org\/10.1155\/2019\/7643905","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"value":"1076-2787","type":"print"},{"value":"1099-0526","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1]]},"assertion":[{"value":"2018-07-02","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-10-30","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-01-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"7643905"}}