{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T20:12:08Z","timestamp":1773778328730,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T00:00:00Z","timestamp":1675987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"RIE2020 Industry Alignment Fund\u2014Industry Collaboration Projects (IAF-ICP) Funding Initiative","award":["I1901E0046"],"award-info":[{"award-number":["I1901E0046"]}]},{"name":"A*STAR under its Industry Alignment Fund","award":["I1901E0046"],"award-info":[{"award-number":["I1901E0046"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Electric Vehicle (EV) charging demand and charging station availability forecasting is one of the challenges in the intelligent transportation system. With accurate EV station availability prediction, suitable charging behaviors can be scheduled in advance to relieve range anxiety. Many existing deep learning methods have been proposed to address this issue; however, due to the complex road network structure and complex external factors, such as points of interest (POIs) and weather effects, many commonly used algorithms can only extract the historical usage information and do not consider the comprehensive influence of external factors. To enhance the prediction accuracy and interpretability, the Attribute-Augmented Spatiotemporal Graph Informer (AST-GIN) structure is proposed in this study by combining the Graph Convolutional Network (GCN) layer and the Informer layer to extract both the external and internal spatiotemporal dependence of relevant transportation data. The external factors are modeled as dynamic attributes by the attributeaugmented encoder for training. The AST-GIN model was tested on the data collected in Dundee City, and the experimental results showed the effectiveness of our model considering external factors\u2019 influence on various horizon settings compared with other baselines.<\/jats:p>","DOI":"10.3390\/s23041975","type":"journal-article","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T02:09:59Z","timestamp":1675994999000},"page":"1975","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["AST-GIN: Attribute-Augmented Spatiotemporal Graph Informer Network for Electric Vehicle Charging Station Availability Forecasting"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0939-4275","authenticated-orcid":false,"given":"Ruikang","family":"Luo","sequence":"first","affiliation":[{"name":"School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9300-2205","authenticated-orcid":false,"given":"Yaofeng","family":"Song","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9111-8383","authenticated-orcid":false,"given":"Liping","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore"}]},{"given":"Yicheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore"}]},{"given":"Rong","family":"Su","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Luo, R., and Su, R. (2020, January 13\u201315). Traffic signal transition time prediction based on aerial captures during peak hours. Proceedings of the 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), Shenzhen, China.","DOI":"10.1109\/ICARCV50220.2020.9305382"},{"key":"ref_2","unstructured":"Irle, R. (2022, October 10). Global Plug-In Vehicle Sales Reached over 3, 2 Million in 2020. EV Volumes. Available online: https:\/\/www.ev-volumes.com."},{"key":"ref_3","unstructured":"Storandt, S., and Funke, S. (2012, January 8\u201312). Cruising with a battery-powered vehicle and not getting stranded. Proceedings of the AAAI Conference on Artificial Intelligence, Stanford, CA, USA."},{"key":"ref_4","unstructured":"Qian, K., Zhou, C., Allan, M., and Yuan, Y. (2010, January 24\u201328). Load model for prediction of electric vehicle charging demand. Proceedings of the 2010 International Conference on Power System Technology, Hangzhou, China."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kondo, Y., Kato, H., Ando, R., Suzuki, T., and Karakama, Y. (2013, January 17\u201320). To what extent can speed management alleviate the range anxiety of EV?. Proceedings of the 2013 World Electric Vehicle Symposium and Exhibition (EVS27), Barcelona, Spain.","DOI":"10.1109\/EVS.2013.6914838"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1109\/TSG.2011.2159816","article-title":"Real-time coordination of plug-in electric vehicle charging in smart grids to minimize power losses and improve voltage profile","volume":"2","author":"Deilami","year":"2011","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2493","DOI":"10.1109\/TITS.2017.2754382","article-title":"Optimal electric vehicle fast charging station placement based on game theoretical framework","volume":"19","author":"Xiong","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Alface, G., Ferreira, J.C., and Pereira, R. (2019). Electric vehicle charging process and parking guidance app. Energies, 12.","DOI":"10.3390\/en12112123"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1016\/j.renene.2020.03.175","article-title":"EV charging load simulation and forecasting considering traffic jam and weather to support the integration of renewables and EVs","volume":"159","author":"Yan","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"35973","DOI":"10.1109\/ACCESS.2021.3062114","article-title":"AST-GCN: Attribute-augmented spatiotemporal graph convolutional network for traffic forecasting","volume":"9","author":"Zhu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Huang, C.W., Chiang, C.T., and Li, Q. (2017, January 8\u201313). A study of deep learning networks on mobile traffic forecasting. Proceedings of the 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Montreal, QC, Canada.","DOI":"10.1109\/PIMRC.2017.8292737"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Siami-Namini, S., Tavakoli, N., and Namin, A.S. (2018, January 17\u201320). A comparison of ARIMA and LSTM in forecasting time series. Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA.","DOI":"10.1109\/ICMLA.2018.00227"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jiang, W., and Luo, J. (2021). Graph neural network for traffic forecasting: A survey. arXiv.","DOI":"10.1016\/j.eswa.2022.117921"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yang, J., Chen, H., Xu, Y., Shi, Z., Luo, R., Xie, L., and Su, R. (2020, January 13\u201315). Domain adaptation for degraded remote scene classification. Proceedings of the 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), Shenzhen, China.","DOI":"10.1109\/ICARCV50220.2020.9305483"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Amara-Ouali, Y., Goude, Y., Massart, P., Poggi, J.M., and Yan, H. (2021). A review of electric vehicle load open data and models. Energies, 14.","DOI":"10.1145\/3447555.3466568"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bikcora, C., Refa, N., Verheijen, L., and Weiland, S. (2016, January 16\u201320). Prediction of availability and charging rate at charging stations for electric vehicles. Proceedings of the 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Beijing, China.","DOI":"10.1109\/PMAPS.2016.7764216"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.energy.2019.05.230","article-title":"Predicting residential energy consumption using CNN-LSTM neural networks","volume":"182","author":"Kim","year":"2019","journal-title":"Energy"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lee, Z.J., Li, T., and Low, S.H. (2019, January 25\u201328). ACN-Data: Analysis and applications of an open EV charging dataset. Proceedings of the Tenth ACM International Conference on Future Energy Systems, Phoenix, AZ, USA.","DOI":"10.1145\/3307772.3328313"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.epsr.2018.09.022","article-title":"Statistical characterisation of the real transaction data gathered from electric vehicle charging stations","volume":"166","author":"Flammini","year":"2019","journal-title":"Electr. Power Syst. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.apenergy.2015.10.184","article-title":"Forecasting the EV charging load based on customer profile or station measurement?","volume":"163","author":"Majidpour","year":"2016","journal-title":"Appl. Energy"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"123217","DOI":"10.1016\/j.energy.2022.123217","article-title":"Multistep electric vehicle charging station occupancy prediction using hybrid LSTM neural networks","volume":"244","author":"Ma","year":"2022","journal-title":"Energy"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"412","DOI":"10.35833\/MPCE.2018.000802","article-title":"A review on plug-in electric vehicles: Introduction, current status, and load modeling techniques","volume":"8","author":"Ahmadian","year":"2020","journal-title":"J. Mod. Power Syst. Clean Energy"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.isatra.2019.08.011","article-title":"Internet of Things based real-time electric vehicle load forecasting and charging station recommendation","volume":"97","author":"Savari","year":"2020","journal-title":"Isa Trans."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"108404","DOI":"10.1016\/j.ijepes.2022.108404","article-title":"Multinodes interval electric vehicle day-ahead charging load forecasting based on joint adversarial generation","volume":"143","author":"Huang","year":"2022","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"102084","DOI":"10.1016\/j.scs.2020.102084","article-title":"A systematic methodology for mid-and-long term electric vehicle charging load forecasting: The case study of Shenzhen, China","volume":"56","author":"Zheng","year":"2020","journal-title":"Sustain. Cities Soc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3984","DOI":"10.1109\/TKDE.2020.3034140","article-title":"Semi-Supervised City-Wide Parking Availability Prediction via Hierarchical Recurrent Graph Neural Network","volume":"34","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.trc.2019.08.010","article-title":"A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources","volume":"107","author":"Yang","year":"2019","journal-title":"Transp. Res. Part Emerg. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1287\/trsc.2021.1058","article-title":"A node-charge graph-based online carshare rebalancing policy with capacitated electric charging","volume":"56","author":"Pantelidis","year":"2021","journal-title":"Transp. Sci."},{"key":"ref_30","unstructured":"Ma, T.Y., Pantelidis, T., and Chow, J.Y. (2021). Optimal queueing-based rebalancing for one-way electric carsharing systems with stochastic demand. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhao, H., Yang, H., Wang, Y., Wang, D., and Su, R. (2020, January 20\u201323). Attention Based Graph Bi-LSTM Networks for Traffic Forecasting. Proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece.","DOI":"10.1109\/ITSC45102.2020.9294470"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6461450","DOI":"10.1155\/2019\/6461450","article-title":"Traffic flow prediction during the holidays based on DFT and SVR","volume":"2019","author":"Luo","year":"2019","journal-title":"J. Sensors"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhao, H., Yang, H., Wang, Y., Su, R., and Wang, D. (2021, January 19\u201322). Domain-Adversarial-based Temporal Graph Convolutional Network for Traffic Flow Prediction Problem. Proceedings of the 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA.","DOI":"10.1109\/ITSC48978.2021.9564998"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yang, H., Wang, Y., Zhao, H., Zhu, J., and Wang, D. (2020, January 20\u201323). Real-time Traffic Incident Detection Using an Autoencoder Model. Proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece.","DOI":"10.1109\/ITSC45102.2020.9294455"},{"key":"ref_35","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_36","first-page":"802","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","volume":"28","author":"Shi","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_37","first-page":"6000","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Luo, R., Zhang, Y., Zhou, Y., Chen, H., Yang, L., Yang, J., and Su, R. (2021, January 19\u201322). Deep Learning Approach for Long-Term Prediction of Electric Vehicle (EV) Charging Station Availability. Proceedings of the 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA.","DOI":"10.1109\/ITSC48978.2021.9564633"},{"key":"ref_39","unstructured":"Wagner, S., G\u00f6tzinger, M., and Neumann, D. (2013, January 15\u201318). Optimal location of charging stations in smart cities: A points of interest based approach. Proceedings of the 34th International Conference on Information Systems, Milan, Italy."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"88205","DOI":"10.1109\/ACCESS.2021.3090534","article-title":"A probabilistic methodology to quantify the impacts of cold weather on electric vehicle demand: A case study in the UK","volume":"9","author":"Koncar","year":"2021","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Liao, B., Zhang, J., Wu, C., McIlwraith, D., Chen, T., Yang, S., Guo, Y., and Wu, F. (2018, January 19\u201323). Deep sequence learning with auxiliary information for traffic prediction. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK.","DOI":"10.1145\/3219819.3219895"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","article-title":"T-gcn: A temporal graph convolutional network for traffic prediction","volume":"21","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Quir\u00f3s-Tort\u00f3s, J., Ochoa, L.F., and Lees, B. (2015, January 5\u20137). A statistical analysis of EV charging behavior in the UK. Proceedings of the 2015 IEEE PES Innovative Smart Grid Technologies Latin America (ISGT LATAM), Montevideo, Uruguay.","DOI":"10.1109\/ISGT-LA.2015.7381196"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y., and Wang, Y. (2017). Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction. Sensors, 17.","DOI":"10.3390\/s17040818"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"81606","DOI":"10.1109\/ACCESS.2020.2991462","article-title":"City-wide traffic congestion prediction based on CNN, LSTM and transpose CNN","volume":"8","author":"Ranjan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_46","unstructured":"Kipf, T.N., and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Tsai, Y.H.H., Bai, S., Yamada, M., Morency, L.P., and Salakhutdinov, R. (2019). Transformer Dissection: A Unified Understanding of Transformer\u2019s Attention via the Lens of Kernel. arXiv.","DOI":"10.18653\/v1\/D19-1443"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., and Zhang, W. (2021, January 2\u20139). Informer: Beyond efficient transformer for long sequence time series forecasting. Proceedings of the AAAI, Virtually.","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1002\/jmri.24365","article-title":"Fast image reconstruction with L2-regularization","volume":"40","author":"Bilgic","year":"2014","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_50","unstructured":"Xu, M., Dai, W., Liu, C., Gao, X., Lin, W., Qi, G.J., and Xiong, H. (2020). Spatial-temporal transformer networks for traffic flow forecasting. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/1975\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:29:42Z","timestamp":1760120982000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/1975"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,10]]},"references-count":50,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23041975"],"URL":"https:\/\/doi.org\/10.3390\/s23041975","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,10]]}}}