{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T16:04:21Z","timestamp":1783526661110,"version":"3.55.0"},"reference-count":40,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T00:00:00Z","timestamp":1672790400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Small and Medium-sized Enterprises (SMEs) and Startups (MSS), Korea","award":["S3246057"],"award-info":[{"award-number":["S3246057"]}]},{"name":"Ministry of Small and Medium-sized Enterprises (SMEs) and Startups (MSS), Korea","award":["P0016977"],"award-info":[{"award-number":["P0016977"]}]},{"DOI":"10.13039\/501100003661","name":"Korea Institute for Advancement of Technology (KIAT)","doi-asserted-by":"publisher","award":["S3246057"],"award-info":[{"award-number":["S3246057"]}],"id":[{"id":"10.13039\/501100003661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003661","name":"Korea Institute for Advancement of Technology (KIAT)","doi-asserted-by":"publisher","award":["P0016977"],"award-info":[{"award-number":["P0016977"]}],"id":[{"id":"10.13039\/501100003661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In terms of electric vehicles (EVs), electric kickboards are crucial elements of smart transportation networks for short-distance travel that is risk-free, economical, and environmentally friendly. Forecasting the daily demand can improve the local service provider\u2019s access to information and help them manage their short-term supply more effectively. This study developed the forecasting model using real-time data and weather information from Jeju Island, South Korea. Cluster analysis under the rental pattern of the electric kickboard is a component of the forecasting processes. We cannot achieve noticeable results at first because of the low amount of training data. We require a lot of data to produce a solid prediction result. For the sake of the subsequent experimental procedure, we created synthetic time-series data using a generative adversarial networks (GAN) approach and combined the synthetic data with the original data. The outcomes have shown how the GAN-based synthetic data generation approach has the potential to enhance prediction accuracy. We employ an ensemble model to improve prediction results that cannot be achieved using a single regressor model. It is a weighted combination of several base regression models to one meta-regressor. To anticipate the daily demand in this study, we create an ensemble model by merging three separate base machine learning algorithms, namely CatBoost, Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The effectiveness of the suggested strategies was assessed using some evaluation indicators. The forecasting outcomes demonstrate that mixing synthetic data with original data improves the robustness of daily demand forecasting and outperforms other models by generating more agreeable values for suggested assessment measures. The outcomes further show that applying ensemble techniques can reasonably increase the forecasting model\u2019s accuracy for daily electric kickboard demand.<\/jats:p>","DOI":"10.3390\/s23020594","type":"journal-article","created":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T02:28:53Z","timestamp":1672885733000},"page":"594","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["A Synthetic Data Generation Technique for Enhancement of Prediction Accuracy of Electric Vehicles Demand"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1648-6095","authenticated-orcid":false,"given":"Subhajit","family":"Chatterjee","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Jeju National University, Jeju 63243, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1107-9941","authenticated-orcid":false,"given":"Yung-Cheol","family":"Byun","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Major of Electronic Engineering, Jeju National University, Institute of Information Science & Technology, Jeju 63243, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1108\/MEQ-03-2018-0049","article-title":"The implementation of the concept of environmental carrying capacity into spatial management of cities: A review","volume":"29","year":"2018","journal-title":"Manag. 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