{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T10:08:01Z","timestamp":1773914881833,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,9,10]],"date-time":"2022-09-10T00:00:00Z","timestamp":1662768000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,9,10]],"date-time":"2022-09-10T00:00:00Z","timestamp":1662768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Innovations Syst Softw Eng"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s11334-022-00481-3","type":"journal-article","created":{"date-parts":[[2022,9,10]],"date-time":"2022-09-10T07:02:41Z","timestamp":1662793361000},"page":"65-77","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Parking slot occupancy prediction using LSTM"],"prefix":"10.1007","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3063-5528","authenticated-orcid":false,"given":"Rohit Kumar","family":"Kasera","sequence":"first","affiliation":[]},{"given":"Tapodhir","family":"Acharjee","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,10]]},"reference":[{"key":"481_CR1","unstructured":"McCoy K (2017) Drivers\u00a0spend an average of 17 hours a year searching for parking spots. https:\/\/www.usatoday.com\/story\/money\/2017\/07\/12\/parking-pain-causes-financial-and-personal-strain\/467637001\/. Accessed 25 Nov 2021"},{"key":"481_CR2","doi-asserted-by":"publisher","unstructured":"Zheng Y, Rajasegarar S, Leckie C (2015) Parking availability prediction for sensor-enabled car parks in smart cities. In: 2015 IEEE tenth international conference on intelligent sensors, sensor networks and information processing (ISSNIP), pp 1\u20136. https:\/\/doi.org\/10.1109\/ISSNIP.2015.7106902","DOI":"10.1109\/ISSNIP.2015.7106902"},{"key":"481_CR3","doi-asserted-by":"publisher","unstructured":"Bura H, Lin N, Kumar N, Malekar S, Nagaraj S, Liu K (2018) An edge based smart parking solution using camera networks and deep learning. In: 2018 IEEE international conference on cognitive computing (ICCC), pp 17\u201324. https:\/\/doi.org\/10.1109\/ICCC.2018.00010","DOI":"10.1109\/ICCC.2018.00010"},{"key":"481_CR4","doi-asserted-by":"publisher","unstructured":"Rahim RU, Aslam M, Zoolnurain, Khan MG, Basra IA (2021) Pakistani standard vehicle plates recognition using deep neural networks. In: 2021 International conference on artificial intelligence (ICAI), pp 158\u2013163. https:\/\/doi.org\/10.1109\/ICAI52203.2021.9445199","DOI":"10.1109\/ICAI52203.2021.9445199"},{"key":"481_CR5","doi-asserted-by":"publisher","unstructured":"Bhatta S, Srivastava H, Das SK, Singh P (2021) License plate detection for smart parking management. In: International conference on emerging trends and advances in electrical engineering and renewable energy, vol 690, pp 71\u201379. https:\/\/doi.org\/10.1007\/978-981-15-7504-4_8","DOI":"10.1007\/978-981-15-7504-4_8"},{"key":"481_CR6","doi-asserted-by":"publisher","unstructured":"Garg S, Lohumi P, Agrawal S (2020) Smart parking system to predict occupancy rates using machine learning. In: International conference on information, communication and computing technology (ICICCT), vol 1170, pp 163\u2013171. https:\/\/doi.org\/10.1007\/978-981-15-9671-1_13","DOI":"10.1007\/978-981-15-9671-1_13"},{"key":"481_CR7","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1155\/2020\/5624586","volume":"2020","author":"Z Zhao","year":"2020","unstructured":"Zhao Z, Zhang Y (2020) A comparative study of parking occupancy prediction methods considering parking type and parking scale. J Adv Transp 2020:12. https:\/\/doi.org\/10.1155\/2020\/5624586","journal-title":"J Adv Transp"},{"key":"481_CR8","doi-asserted-by":"publisher","first-page":"100301","DOI":"10.1016\/j.iot.2020.100301","volume":"12","author":"JC Provoost","year":"2020","unstructured":"Provoost JC, Kamilaris A, Wismans LJJ, Van Der Drift SJ, Van Keulen M (2020) Predicting parking occupancy via machine learning in the web of things. Internet of Things 12:100301. https:\/\/doi.org\/10.1016\/j.iot.2020.100301","journal-title":"Internet of Things"},{"key":"481_CR9","doi-asserted-by":"publisher","unstructured":"Williams G, Baxter R, He H, Hawkins S, Gu L (2002) A comparative study of RNN for outlier detection in data mining. In: 2002 IEEE international conference on data mining, 2002. Proceedings, pp 709\u2013712. https:\/\/doi.org\/10.1109\/ICDM.2002.1184035","DOI":"10.1109\/ICDM.2002.1184035"},{"key":"481_CR10","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1080\/15472450.2015.1037955","volume":"20","author":"EI Vlahogianni","year":"2016","unstructured":"Vlahogianni EI, Kepaptsoglou K, Tsetsos V, Karlaftis MG (2016) A real-time parking prediction system for smart cities. J Intell Transp Syst 20:192\u2013204. https:\/\/doi.org\/10.1080\/15472450.2015.1037955","journal-title":"J Intell Transp Syst"},{"key":"481_CR11","volume-title":"Predicting parking lots occupancy in Bolzano","author":"M Reinstadler","year":"2013","unstructured":"Reinstadler M, Braunhofer M, Elahi M, Ricci F (2013) Predicting parking lots occupancy in Bolzano. Academic project, Computer Science, Free University of Bolzano Italy, Bolzano"},{"issue":"10","key":"481_CR12","doi-asserted-by":"publisher","first-page":"1696","DOI":"10.3390\/electronics9101696","volume":"9","author":"G Ali","year":"2020","unstructured":"Ali G, Ali T, Irfan M, Draz U, Sohail M, Glowacz A, Sulowicz M, Mielnik R, Faheem ZB, Martis C (2020) IoT based smart parking system using deep long short memory network. Electronics 9(10):1696. https:\/\/doi.org\/10.3390\/electronics9101696","journal-title":"Electronics"},{"issue":"8","key":"481_CR13","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"issue":"7","key":"481_CR14","doi-asserted-by":"publisher","first-page":"1387","DOI":"10.3390\/w11071387","volume":"11","author":"XH Le","year":"2019","unstructured":"Le XH, Ho HV, Lee G, Jung S (2019) Application of long short-term memory (LSTM) neural network for flood forecasting. Water 11(7):1387. https:\/\/doi.org\/10.3390\/w11071387","journal-title":"Water"},{"key":"481_CR15","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1155\/2020\/6622927","volume":"2020","author":"W Lu","year":"2020","unstructured":"Lu W, Li J, Li Y, Sun A, Wang J (2020) A CNN-LSTM-based model to forecast stock prices. Complexity 2020:10. https:\/\/doi.org\/10.1155\/2020\/6622927","journal-title":"Complexity"},{"key":"481_CR16","doi-asserted-by":"publisher","first-page":"44059","DOI":"10.1109\/ACCESS.2018.2864157","volume":"6","author":"C Badii","year":"2018","unstructured":"Badii C, Nesi P, Paoli I (2018) Predicting available parking slots on critical and regular services by exploiting a range of open data. IEEE Access 6:44059\u201344071. https:\/\/doi.org\/10.1109\/ACCESS.2018.2864157","journal-title":"IEEE Access"},{"issue":"1","key":"481_CR17","doi-asserted-by":"publisher","first-page":"322","DOI":"10.3390\/s20010322","volume":"20","author":"FM Awan","year":"2020","unstructured":"Awan FM, Saleem Y, Minerva R, Crespi N (2020) A comparative analysis of machine\/deep learning models for parking space availability prediction. Sensors 20(1):322. https:\/\/doi.org\/10.3390\/s20010322","journal-title":"Sensors"},{"key":"481_CR18","doi-asserted-by":"publisher","unstructured":"Shao W, Zhang Y, Guo B, Qin K, Chan J, Salim FD (2019) Parking availability prediction with long short term memory model. In: International conference on green, pervasive, and cloud computing. Springer, vol 11204, pp 124\u2013137. https:\/\/doi.org\/10.1007\/978-3-030-15093-8_9","DOI":"10.1007\/978-3-030-15093-8_9"},{"key":"481_CR19","doi-asserted-by":"publisher","unstructured":"Mishra A, Deshpande S (2021) Deep learning based parking prediction using LSTM approach. In: International conference on information and communication technology for intelligent systems. Springer, vol 196, pp 589\u2013597. https:\/\/doi.org\/10.1007\/978-981-15-7062-9_59","DOI":"10.1007\/978-981-15-7062-9_59"},{"issue":"11","key":"481_CR20","doi-asserted-by":"publisher","first-page":"5350","DOI":"10.1109\/TIP.2018.2857407","volume":"27","author":"L Zhang","year":"2018","unstructured":"Zhang L, Huang J, Li X, Xiong L (2018) Vision-based parking-slot detection: a DCNN-based approach and a large-scale benchmark dataset. IEEE Trans Image Process 27(11):5350\u20135364. https:\/\/doi.org\/10.1109\/TIP.2018.2857407","journal-title":"IEEE Trans Image Process"},{"key":"481_CR21","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1016\/j.trc.2019.08.010","volume":"107","author":"S Yang","year":"2019","unstructured":"Yang S, Ma W, Pi X, Qian S (2019) A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources. Transportation Research Part C: Emerging Technologies 107:248\u2013265. https:\/\/doi.org\/10.1016\/j.trc.2019.08.010","journal-title":"Transportation Research Part C: Emerging Technologies"},{"key":"481_CR22","unstructured":"Chen X (2014) Parking occupancy prediction and pattern analysis. Department of Computer Science, Stanford University, Stanford, CA, USA. Technical report CS229-2014"},{"key":"481_CR23","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.trb.2018.04.001","volume":"112","author":"J Xiao","year":"2018","unstructured":"Xiao J, Lou Y, Frisby J (2018) How likely am I to find parking?\u2014a practical model-based framework for predicting parking availability. Transp Res Part B Methodol 112:19\u201339. https:\/\/doi.org\/10.1016\/j.trb.2018.04.001","journal-title":"Transp Res Part B Methodol"},{"issue":"9","key":"481_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-021-00875-3","volume":"3","author":"RK Kasera","year":"2022","unstructured":"Kasera RK, Acharjee T (2022) A smart indoor parking system. SN Comput Sci 3(9):1\u201317. https:\/\/doi.org\/10.1007\/s42979-021-00875-3","journal-title":"SN Comput Sci"},{"issue":"5","key":"481_CR25","doi-asserted-by":"publisher","first-page":"052037","DOI":"10.1088\/1757-899X\/569\/5\/052037","volume":"569","author":"X Zhang","year":"2019","unstructured":"Zhang X, Liang X, Zhiyuli A, Zhang S, Xu R, Wu B (2019) AT-LSTM: an attention-based LSTM model for financial time series prediction. IOP Conf Ser Mater Sci Eng 569(5):052037. https:\/\/doi.org\/10.1088\/1757-899X\/569\/5\/052037","journal-title":"IOP Conf Ser Mater Sci Eng"},{"key":"481_CR26","doi-asserted-by":"publisher","unstructured":"Jin X, Yu X, Wang X, Bai Y, Su T, Kong J (2020) Prediction for time series with CNN and LSTM. In: Proceedings of the 11th international conference on modelling, identification and control (ICMIC2019). Lecture notes in electrical engineering, vol 582, pp 631\u2013641. https:\/\/doi.org\/10.1007\/978-981-15-0474-7_59","DOI":"10.1007\/978-981-15-0474-7_59"},{"key":"481_CR27","doi-asserted-by":"publisher","first-page":"123217","DOI":"10.1016\/j.energy.2022.123217","volume":"244","author":"T-Y Ma","year":"2022","unstructured":"Ma T-Y, Faye S (2022) Multistep electric vehicle charging station occupancy prediction using hybrid LSTM neural networks. Energy 244:123217. https:\/\/doi.org\/10.1016\/j.energy.2022.123217","journal-title":"Energy"},{"key":"481_CR28","doi-asserted-by":"publisher","unstructured":"Hung BT, Chakrabarti P (2022) Parking lot occupancy detection using hybrid deep learning CNN-LSTM approach. In: Proceedings of 2nd international conference on artificial intelligence: advances and applications, pp 501\u2013509. https:\/\/doi.org\/10.1007\/978-981-16-6332-1_43","DOI":"10.1007\/978-981-16-6332-1_43"},{"key":"481_CR29","doi-asserted-by":"publisher","unstructured":"Siami-Namini S, Tavakoli N, Siami Namin A (2018) A comparison of ARIMA and LSTM in forecasting time series. In: 2018 17th IEEE international conference on machine learning and applications (ICMLA), pp 1394\u20131401. https:\/\/doi.org\/10.1109\/ICMLA.2018.00227","DOI":"10.1109\/ICMLA.2018.00227"},{"key":"481_CR30","doi-asserted-by":"publisher","first-page":"47361","DOI":"10.1109\/ACCESS.2022.3171330","volume":"10","author":"C Zeng","year":"2022","unstructured":"Zeng C, Ma C, Wang Ke, Cui Z (2022) Parking occupancy prediction method based on multi factors and stacked GRU-LSTM. IEEE Access 10:47361\u201347370. https:\/\/doi.org\/10.1109\/ACCESS.2022.3171330","journal-title":"IEEE Access"},{"key":"481_CR31","unstructured":"Brownlee J (2018) How to develop LSTM models for time series forecasting https:\/\/machinelearningmastery.com\/how-to-develop-lstm-models-for-time-series-forecasting\/. Accessed 28 Aug 2020"},{"issue":"1","key":"481_CR32","doi-asserted-by":"publisher","first-page":"012049","DOI":"10.1088\/1757-899X\/324\/1\/012049\/meta","volume":"324","author":"W Wang","year":"2018","unstructured":"Wang W, Lu Y (2018) Analysis of the mean absolute error (MAE) and the root mean square error (RMSE) in assessing rounding model. IOP Conf Ser Mater Sci Eng 324(1):012049. https:\/\/doi.org\/10.1088\/1757-899X\/324\/1\/012049\/meta","journal-title":"IOP Conf Ser Mater Sci Eng"},{"key":"481_CR33","doi-asserted-by":"publisher","unstructured":"Sarbishei O, Radecka K (2011) Analysis of mean-square-error (MSE) for fixed-point FFT units. In: 2011 IEEE international symposium of circuits and systems (ISCAS), pp 1732\u20131735. https:\/\/doi.org\/10.1109\/ISCAS.2011.5937917","DOI":"10.1109\/ISCAS.2011.5937917"},{"issue":"1","key":"481_CR34","doi-asserted-by":"publisher","first-page":"79","DOI":"10.3354\/cr030079","volume":"30","author":"CJ Willmott","year":"2005","unstructured":"Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim Res 30(1):79\u201382","journal-title":"Clim Res"},{"issue":"1","key":"481_CR35","doi-asserted-by":"publisher","first-page":"012035","DOI":"10.1088\/1757-899X\/1176\/1\/012035","volume":"1176","author":"MH Ismail","year":"2021","unstructured":"Ismail MH, Razak TR, Gining RAJM, Fauzi SSM, Abdul-Aziz A (2021) Predicting vehicle parking space availability using multilayer perceptron neural network. IOP Conf Ser Mater Sci Eng 1176(1):012035","journal-title":"IOP Conf Ser Mater Sci Eng"},{"key":"481_CR36","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1016\/j.trpro.2020.03.113","volume":"47","author":"J Arjona","year":"2020","unstructured":"Arjona J, Linares M\u00aaPaz, Casanovas-Garcia J, V\u00e1zquez JJ (2020) Improving parking availability information using deep learning techniques. Transp Res Procedia 47:385\u2013392. https:\/\/doi.org\/10.1016\/j.trpro.2020.03.113","journal-title":"Transp Res Procedia"},{"key":"481_CR37","doi-asserted-by":"publisher","unstructured":"Jose A, Vidya V (2021) A stacked long short-term memory neural networks for parking occupancy rate prediction. In: 10th IEEE international conference on communication systems and network technologies (CSNT), pp 522\u2013525. https:\/\/doi.org\/10.1109\/CSNT51715.2021.9509621","DOI":"10.1109\/CSNT51715.2021.9509621"}],"container-title":["Innovations in Systems and Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11334-022-00481-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11334-022-00481-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11334-022-00481-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,8]],"date-time":"2025-03-08T02:41:30Z","timestamp":1741401690000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11334-022-00481-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,10]]},"references-count":37,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["481"],"URL":"https:\/\/doi.org\/10.1007\/s11334-022-00481-3","relation":{},"ISSN":["1614-5046","1614-5054"],"issn-type":[{"value":"1614-5046","type":"print"},{"value":"1614-5054","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,10]]},"assertion":[{"value":"7 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There are no conflicts of interest related to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}