{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T12:40:46Z","timestamp":1789562446404,"version":"build-2803163510"},"reference-count":36,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,5,22]],"date-time":"2023-05-22T00:00:00Z","timestamp":1684713600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The accurate and rapid prediction of parking availability is helpful for improving parking efficiency and to optimize traffic systems. However, previous studies have suffered from limited training sample sizes and a lack of thorough investigation into the correlations among the factors affecting parking availability. The purpose of this study is to explore a prediction method that can account for multiple factors. Firstly, a dynamic prediction method based on a temporal convolutional network (TCN) model was confirmed to be efficient for ultra-short-term parking availability with an accuracy of 0.96 MSE. Then, an attention-enhanced TCN (A-TCN) model based on spatial attention modules was proposed. This model integrates multiple factors, including related dates, extreme weather, and human control, to predict the daily congestion index of parking lots in the short term, with a prediction period of up to one month. Experimental results on real data demonstrate that the MSE of A-TCN is 0.0061, exhibiting better training efficiency and prediction accuracy than a traditional TCN for the short-term prediction time scale.<\/jats:p>","DOI":"10.3390\/ijgi12050208","type":"journal-article","created":{"date-parts":[[2023,5,23]],"date-time":"2023-05-23T01:36:48Z","timestamp":1684805808000},"page":"208","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Intelligent Short-Term Multiscale Prediction of Parking Space Availability Using an Attention-Enhanced Temporal Convolutional Network"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7609-6609","authenticated-orcid":false,"given":"Ke","family":"Shang","sequence":"first","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeyu","family":"Wan","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yulin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiwei","family":"Cui","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2544-4487","authenticated-orcid":false,"given":"Zihan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenchen","family":"Jiang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feizhou","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ogryzek, M., Krupowicz, W., and Sajn\u00f3g, N. (2021). Public Participation as a Tool for Solving Socio-Spatial Conflicts of Smart Cities and Smart Villages in the Sustainable Transport System. Remote Sens., 13.","DOI":"10.3390\/rs13234821"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"100102","DOI":"10.1007\/s11432-015-5397-4","article-title":"A literature survey on smart cities","volume":"58","author":"Yin","year":"2015","journal-title":"Sci. China Inf. Sci."},{"key":"ref_3","first-page":"143","article-title":"Smart governance in the context of smart cities: A literature review","volume":"23","author":"Pereira","year":"2018","journal-title":"Inf. Policy"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.sbspro.2012.09.856","article-title":"A Novel Architecture of Parking Management for Smart Cities","volume":"53","author":"Siniscalchi","year":"2012","journal-title":"Procedia\u2013Soc. Behav. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1007\/s13748-022-00291-5","article-title":"Exploring how independent variables influence parking occupancy prediction: Toward a model results explanation with SHAP values","volume":"11","author":"Errousso","year":"2022","journal-title":"Prog. Artif. Intell."},{"key":"ref_6","first-page":"851","article-title":"Simulation of prediction for free parking spaces in large parking lots","volume":"36","author":"She","year":"2019","journal-title":"Appl. Res. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, Q., and Li, H. (2022, January 14\u201316). Design of Parking Space Management System Based on Internet of Things Technology. Proceedings of the 2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC 2022), Dalian, China.","DOI":"10.1109\/IPEC54454.2022.9777490"},{"key":"ref_8","first-page":"60","article-title":"Forecasting of effective parking space based on grey-distributed wavelet neural network model","volume":"17","author":"Han","year":"2017","journal-title":"J. Transp. Syst. Eng. Inf. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.adhoc.2012.03.002","article-title":"Finding available parking spaces made easy","volume":"12","author":"Klappenecker","year":"2014","journal-title":"Ad. Hoc. Netw."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5237","DOI":"10.1109\/TSP.2007.898783","article-title":"Autoregressive modeling of temporal envelopes","volume":"55","author":"Athineos","year":"2007","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liu, S., Guan, H., Yan, H., and Yin, H. (2010, January 4\u20138). Unoccupied Parking Space Prediction of Chaotic Time Series. Proceedings of the Tenth International Conference of Chinese Transportation Professionals (ICCTP), Beijing, China.","DOI":"10.1061\/41127(382)228"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1007\/s10489-014-0618-x","article-title":"An optimized classification algorithm by BP neural network based on PLS and HCA","volume":"43","author":"Jia","year":"2015","journal-title":"Appl. Intell."},{"key":"ref_13","first-page":"388","article-title":"Short-term parking space prediction based on wavelet-ELM neural network","volume":"55","author":"Chen","year":"2017","journal-title":"J. Jilin Univ. Sci. Ed."},{"key":"ref_14","unstructured":"Xie, K. (2014). Characteristics and Optimization of Motor Vehicle Parking Space Sharing in the Central Business District of Universities. [Master\u2019s Thesis, Southeast University]. (In Chinese)."},{"key":"ref_15","first-page":"236","article-title":"Prediction of parking guidance space based on BP neural networks","volume":"1","author":"Gao","year":"2017","journal-title":"Comput. Syst. Appl."},{"key":"ref_16","first-page":"44","article-title":"Application research of parking lot free parking number prediction based on back propagation neural network","volume":"2015","author":"Zhao","year":"2015","journal-title":"J. Harbin Univ. Commer. Nat. Sci. Ed."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1049\/iet-its.2013.0184","article-title":"Short-term forecasting of available parking space using wavelet neural network model","volume":"9","author":"Ji","year":"2014","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_18","first-page":"399","article-title":"Short-term forecasting of parking space using particle swarm optimization\u2013wavelet neural network model","volume":"46","author":"Ji","year":"2016","journal-title":"J. Jilin Univ. Eng. Technol. Ed."},{"key":"ref_19","first-page":"480","article-title":"Time series prediction method based on simplified LSTM neural network","volume":"47","author":"Li","year":"2021","journal-title":"J. Beijing Univ. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"127498","DOI":"10.1016\/j.physa.2022.127498","article-title":"Predicting vacant parking space availability: A DWT-Bi-LSTM model","volume":"599","author":"Zeng","year":"2022","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_21","first-page":"1521","article-title":"Short-term traffic flow prediction based on KNN-LSTM","volume":"44","author":"Luo","year":"2018","journal-title":"J. Beijing Univ. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1109\/TNNLS.2020.2985720","article-title":"LSTM-MSNet: Leveraging forecasts on sets of related time series with multiple seasonal patterns","volume":"32","author":"Bandara","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_23","first-page":"124","article-title":"Parking availability prediction with long short term memory model","volume":"Volume 11204","author":"Li","year":"2019","journal-title":"Green, Pervasive, and Cloud Computing (GPC 2018), Lecture Notes in Computer Science"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tian, Y., Wei, C., and Xu, D. (2020, January 20\u201322). Traffic Flow Prediction Based on Stack AutoEncoder and Long Short-Term Memory Network. Proceedings of the 2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE), Shenyang, China.","DOI":"10.1109\/AUTEEE50969.2020.9315723"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, J., Li, J., and Zhang, H. (2018, January 7\u20139). Deep Learning Based Parking Prediction on Cloud Platform. Proceedings of the 2018 4th International Conference on Big Data Computing and Communications (BIGCOM), Chicago, IL, USA.","DOI":"10.1109\/BIGCOM.2018.00028"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1007\/978-3-030-05348-2_32","article-title":"Evolutionary deep learning for car park occupancy prediction in smart cities","volume":"Volume 11353","author":"Battiti","year":"2019","journal-title":"Learning and Intelligent Optimization (LION 12 2018), Lecture Notes in Computer Science"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.trc.2014.01.005","article-title":"Short-term traffic forecasting: Where we are and where we\u2019re going","volume":"43","author":"Vlahogianni","year":"2014","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tanimura, R., Hiromori, A., Umedu, T., Yamaguchi, H., and Higashino, T. (2015, January 15\u201318). Prediction of Deceleration Amount of Vehicle Speed in Snowy Urban Roads Using Weather Information and Traffic Data. Proceedings of the 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Gran Canaria, Spain.","DOI":"10.1109\/ITSC.2015.366"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"04020042","DOI":"10.1061\/JTEPBS.0000347","article-title":"Markov-Based Time Series Modeling Framework for Traffic-Network State Prediction under Various External Conditions","volume":"146","author":"Li","year":"2020","journal-title":"J. Transp. Eng. Part A Syst."},{"key":"ref_30","first-page":"25","article-title":"Traffic flow prediction based on random forest in severe weather conditions","volume":"48","author":"Xu","year":"2020","journal-title":"J. Shaanxi Norm. Univ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1016\/j.aej.2020.09.003","article-title":"An improved deep belief network for traffic prediction considering weather factors","volume":"60","author":"Bao","year":"2021","journal-title":"Alex. Eng. J."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1080\/15472450.2021.1983809","article-title":"Weather impact on macroscopic traffic stream variables prediction using recurrent learning approach","volume":"27","author":"Nigam","year":"2023","journal-title":"J. Intell. Transp. Syst. Technol. Plan. Oper."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1049\/iet-its.2017.0313","article-title":"Combining weather condition data to predict traffic flow: A GRU-based deep learning approach","volume":"12","author":"Zhang","year":"2018","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.trc.2017.02.024","article-title":"Deep learning for short-term traffic flow prediction","volume":"79","author":"Polson","year":"2017","journal-title":"Transp. Research. Part C Emerg. Technol."},{"key":"ref_35","unstructured":"Bai, S., Kolter, J.Z., and Koltun, V. (2018). An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lea, C., Vidal, R., Reiter, A., and Hager, G.D. (2016). Temporal Convolutional Networks: A Unified Approach to Action Segmentation. arXiv.","DOI":"10.1109\/CVPR.2017.113"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/5\/208\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:40:10Z","timestamp":1760125210000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/5\/208"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,22]]},"references-count":36,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["ijgi12050208"],"URL":"https:\/\/doi.org\/10.3390\/ijgi12050208","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,22]]}}}