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Technol."],"published-print":{"date-parts":[[2024,10,31]]},"abstract":"<jats:p>The growing adoption of electric vehicles (EVs) has resulted in an increased demand for public EV charging infrastructure. Currently, the collaboration between these stations has become vital for efficient charging scheduling and cost reduction. However, most existing scheduling methods primarily focus on recommending charging stations without considering users\u2019 charging preferences. Adopting these strategies may require considerable modifications to how people charge their EVs, which could lead to a reluctance to follow the scheduling plan from charging services in real-world situations. To address these challenges, we propose the POSKID framework in this article. It focuses on spatial-temporal charging scheduling, aiming to recommend a feasible charging arrangement, including a charging station and a charging time slot, to each EV user while minimizing overall operating costs and ensuring users\u2019 charging satisfaction. The framework adopts an online charging mechanism that provides recommendations without prior knowledge of future electricity information or charging requests. To enhance users\u2019 willingness to accept the recommendations, POSKID incorporates an incentive strategy and a novel embedding method combined with Bayesian personalized analysis. These techniques reveal users\u2019 implicit charging preferences, enhancing the success probability of the charging scheduling task. Furthermore, POSKID integrates an online candidate arrangement selection and an explore-exploit strategy to improve the charging arrangement recommendations based on users\u2019 feedback. Experimental results using real-world datasets validate the effectiveness of POSKID in optimizing charging management, surpassing other strategies. The results demonstrate that POSKID benefits each charging station while ensuring user charging satisfaction.<\/jats:p>","DOI":"10.1145\/3678180","type":"journal-article","created":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T15:47:49Z","timestamp":1721231269000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Online Spatial-Temporal EV Charging Scheduling with Incentive Promotion"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8141-9633","authenticated-orcid":false,"given":"Lo Pang-Yun","family":"Ting","sequence":"first","affiliation":[{"name":"National Cheng Kung University, Tainan, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1194-8233","authenticated-orcid":false,"given":"Huan-Yang","family":"Wang","sequence":"additional","affiliation":[{"name":"National Cheng Kung University, Tainan, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2407-9833","authenticated-orcid":false,"given":"Jhe-Yun","family":"Jhang","sequence":"additional","affiliation":[{"name":"National Cheng Kung University, Tainan, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3914-8550","authenticated-orcid":false,"given":"Kun-Ta","family":"Chuang","sequence":"additional","affiliation":[{"name":"National Cheng Kung University, Tainan, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,11,5]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2887194"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSUSC.2020.2979854"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSYST.2018.2851140"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2016.2613600"},{"key":"e_1_3_2_6_2","volume-title":"Proceedings of the 8th International Conference on Future Energy Systems","author":"Chen Dong","year":"2017","unstructured":"Dong Chen and David E. 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