{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T05:37:19Z","timestamp":1775626639247,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T00:00:00Z","timestamp":1612224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100017159","name":"ISCTE \u2013 Instituto Universit\u00e1rio de Lisboa","doi-asserted-by":"publisher","award":["ISTAR-Iscte plurianual fund"],"award-info":[{"award-number":["ISTAR-Iscte plurianual fund"]}],"id":[{"id":"10.13039\/100017159","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Cities are moving towards new mobility strategies to tackle smart cities\u2019 challenges such as carbon emission reduction, urban transport multimodality and mitigation of pandemic hazards, emphasising on the implementation of shared modes, such as bike-sharing systems. This paper poses a research question and introduces a corresponding systematic literature review, focusing on machine learning techniques\u2019 contributions applied to bike-sharing systems to improve cities\u2019 mobility. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was adopted to identify specific factors that influence bike-sharing systems, resulting in an analysis of 35 papers published between 2015 and 2019, creating an outline for future research. By means of systematic literature review and bibliometric analysis, machine learning algorithms were identified in two groups: classification and prediction.<\/jats:p>","DOI":"10.3390\/ijgi10020062","type":"journal-article","created":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T05:44:42Z","timestamp":1612244682000},"page":"62","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Machine Learning Approaches to Bike-Sharing Systems: A Systematic Literature Review"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9684-968X","authenticated-orcid":false,"given":"Vit\u00f3ria","family":"Albuquerque","sequence":"first","affiliation":[{"name":"NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1445-2695","authenticated-orcid":false,"given":"Miguel","family":"Sales Dias","sequence":"additional","affiliation":[{"name":"NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal"},{"name":"Instituto Universit\u00e1rio de Lisboa (ISCTE-IUL), ISTAR, 1649-026 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0834-0275","authenticated-orcid":false,"given":"Fernando","family":"Bacao","sequence":"additional","affiliation":[{"name":"NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,2]]},"reference":[{"key":"ref_1","unstructured":"(2020, June 07). Inter-Agency and Expert Group on Sustainable Development Goal Indicators, Report of the Inter-Agency and Expert Group on Sustainable Development Goal Indicators (E\/CN.3\/2016\/2\/Rev.1), Annex IV. Available online: https:\/\/sustainabledevelopment.un.org\/content\/documents\/11803Official-List-of-Proposed-SDG-Indicators.pdf."},{"key":"ref_2","unstructured":"United Nations (2020, June 07). Habitat III New Urban Agenda: Quito Declaration on Sustainable Cities and Human Settlements for All. Habitat III Conference, no. October 2016; p. 24. Available online: http:\/\/www.eukn.eu\/news\/detail\/agreed-final-draft-of-the-new-urban-agenda-is-now-available\/."},{"key":"ref_3","unstructured":"OECD (2011). Greening Household Behaviour, OECD."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.cities.2016.12.019","article-title":"The urban sustainable development goal: Indicators, complexity and the politics of measuring cities","volume":"63","author":"Klopp","year":"2017","journal-title":"Cities"},{"key":"ref_5","unstructured":"Meddin, R., and DeMaio, P.J. (2020, October 03). The Meddin Bike-Sharing World Map. Google Maps. Available online: https:\/\/bikesharingworldmap.com\/#\/all\/2.3\/-1.57\/33.92\/."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.trc.2016.04.005","article-title":"The promises of big data and small data for travel behavior (aka human mobility) analysis","volume":"68","author":"Chen","year":"2016","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G., and The PRISMA Group (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med., 6.","DOI":"10.1371\/journal.pmed.1000097"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1080\/07294360.2013.841651","article-title":"The benefits of publishing systematic quantitative literature reviews for PhD candidates and other early-career researchers","volume":"33","author":"Pickering","year":"2014","journal-title":"High. Educ. Res. Dev."},{"key":"ref_9","unstructured":"Petticrew, M., and Roberts, H. (2008). Systematic Reviews in the Social Sciences: A Practical Guide, Blackwell Publishing Ltd."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Roscoe, P.B., Mead, M., and Mead, M. (2019). Supporting Materials. The Mountain Arapesh, Routledge.","DOI":"10.4324\/9781351319928"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s11192-009-0146-3","article-title":"Software survey: VOSviewer, a computer program for bibliometric mapping","volume":"84","author":"Waltman","year":"2010","journal-title":"Scientometrics"},{"key":"ref_12","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, Heidelberg, Germany.","DOI":"10.1145\/2971648.2971652"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, J., Li, Q., Qu, M., Chen, W., Yang, J., Xiong, H., Zhong, H., and Fu, Y. (2015, January 14\u201317). Station Site Optimization in Bike Sharing Systems. Proceedings of the IEEE International Conference on Data Mining, ICDM, Atlantic City, NJ, USA.","DOI":"10.1109\/ICDM.2015.99"},{"key":"ref_14","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":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ashqar, H.I., Elhenawy, M., Almannaa, M.H., Ghanem, A., Rakha, H.A., and House, L. (2017, January 26\u201328). Modeling Bike Availability in A Bike-Sharing System Using Machine Learning. Proceedings of the 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017, Naples, Italy.","DOI":"10.1109\/MTITS.2017.8005700"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yang, Z., Hu, J., Shu, Y., Cheng, P., Chen, J., and Moscibroda, T. (2016, January 26\u201330). Mobility Modeling and Prediction in Bike-Sharing Systems. Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services\u2014MobiSys \u201916, Singapore.","DOI":"10.1145\/2906388.2906408"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"117550","DOI":"10.1016\/j.jclepro.2019.07.025","article-title":"Optimizing fleet size and scheduling of feeder transit services considering the influence of bike-sharing systems","volume":"236","author":"Liu","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1007\/s11704-016-6006-4","article-title":"Understanding bike trip patterns leveraging bike sharing system open data","volume":"11","author":"Chen","year":"2017","journal-title":"Front. Comput. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Duan, Y., and Wu, J. (2019, January 10\u201313). Optimizing Rebalance Scheme for Dock-Less Bike Sharing Systems with Adaptive User Incentive. Proceedings of the IEEE International Conference on Mobile Data Management, Hong Kong, China.","DOI":"10.1109\/MDM.2019.00-59"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jiang, J., Lin, F., Fan, J., Lv, H., and Wu, J. (2019). A Destination Prediction Network Based on Spatiotemporal Data for Bike-Sharing. Complexity, 2019.","DOI":"10.1155\/2019\/7643905"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","unstructured":"Chen, P.-C., Hsieh, H.-Y., Sigalingging, X.K., Chen, Y.-R., and Leu, J.-S. (2017, January 4\u20137). Prediction of Station Level Demand in a Bike Sharing System Using Recurrent Neural Networks. Proceedings of the 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), Sydney, Australia.","DOI":"10.1109\/VTCSpring.2017.8108575"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhou, Y., and Huang, Y. (2018, January 10\u201313). Context Aware Flow Prediction of Bike Sharing Systems. Proceedings of the 2018 IEEE International Conference on Big Data, Big Data 2018, Seattle, WA, USA.","DOI":"10.1109\/BigData.2018.8621918"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.procs.2019.08.055","article-title":"Multi features and multi-time steps LSTM based methodology for bike sharing availability prediction","volume":"155","author":"Liu","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhou, X. (2015). Understanding Spatiotemporal Patterns of Biking Behavior by Analyzing Massive Bike Sharing Data in Chicago. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0137922"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.trc.2018.01.001","article-title":"A modeling framework for the dynamic management of free-floating bike-sharing systems","volume":"87","author":"Caggiani","year":"2018","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1613\/jair.5308","article-title":"Dynamic repositioning to reduce lost demand in bike sharing systems","volume":"58","author":"Ghosh","year":"2017","journal-title":"J. Artif. Intell. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1007\/s11116-015-9599-9","article-title":"Comparing cities\u2019 cycling patterns using online shared bicycle maps","volume":"42","author":"Sarkar","year":"2015","journal-title":"Transportation"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4488","DOI":"10.1109\/TITS.2018.2886456","article-title":"Mobility Modeling and Data-Driven Closed-Loop Prediction in Bike-Sharing Systems","volume":"20","author":"Yang","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.trc.2018.10.011","article-title":"Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach","volume":"97","author":"Lin","year":"2018","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_32","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_33","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 IEEE Conference on Intelligent Transportation Systems, Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569726"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.trc.2019.04.006","article-title":"A model framework for discovering the spatio-temporal usage patterns of public free-floating bike-sharing system","volume":"103","author":"Du","year":"2019","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Dai, L., Peng, L., Song, Y., and Zhou, Z. (2019, January 21\u201325). Analysis of Spatial Distribution of China\u2019s Station-Free Bike-Sharing by Clustering Algorithms. Proceedings of the ACM International Conference Proceeding Series, Nice, France.","DOI":"10.1145\/3325730.3325748"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"177856","DOI":"10.1109\/ACCESS.2019.2958378","article-title":"Time-Series Representation and Clustering Approaches for Sharing Bike Usage Mining","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"71060","DOI":"10.1109\/ACCESS.2018.2878857","article-title":"Station Function Discovery: Exploring Trip Records in Urban Public Bike-Sharing System","volume":"6","author":"Guo","year":"2018","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Feng, Y., and Wang, S. (2017, January 24\u201326). A Forecast for Bicycle Rental Demand Based on Random Forests and Multiple Linear Regression. Proceedings of the16th IEEE\/ACIS International Conference on Computer and Information Science, ICIS 2017, Wuhan, China.","DOI":"10.1109\/ICIS.2017.7959977"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.jlamp.2016.11.002","article-title":"An experience in using machine learning for short-term predictions in smart transportation systems","volume":"87","author":"Bacciu","year":"2017","journal-title":"J. Log. Algebr. Methods Program."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.cstp.2019.02.011","article-title":"Modeling bike counts in a bike-sharing system considering the effect of weather conditions","volume":"7","author":"Ashqar","year":"2019","journal-title":"Case Stud. Transp. Policy"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/978-981-13-8683-1_3","article-title":"Station-Level Hourly Bike Demand Prediction for Dynamic Repositioning in Bike Sharing Systems","volume":"149","author":"Wu","year":"2019","journal-title":"Smart Innov. Syst. Technol."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ma, X., Yu, H., Wang, Y., and Wang, Y. (2015). Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0119044"},{"key":"ref_43","first-page":"68","article-title":"LSTM network: A deep learning approach for short-term traffic forecast","volume":"11","author":"Zhao","year":"2017","journal-title":"IET Image Process."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1016\/j.cstp.2020.03.002","article-title":"Build it and give \u2018em bikes, and they will come: The effects of cycling infrastructure and bike-sharing system in Lisbon","volume":"8","author":"Cambra","year":"2020","journal-title":"Case Stud. Transp. Policy"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.trc.2016.05.011","article-title":"Modeling duration choice in space-time multi-state supernetworks for individual activity-travel scheduling","volume":"69","author":"Liao","year":"2016","journal-title":"Transp. Res. Part C Emerg. Technol."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/2\/62\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:18:38Z","timestamp":1760159918000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/2\/62"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,2]]},"references-count":45,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["ijgi10020062"],"URL":"https:\/\/doi.org\/10.3390\/ijgi10020062","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,2]]}}}