{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T03:10:50Z","timestamp":1777605050625,"version":"3.51.4"},"reference-count":56,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T00:00:00Z","timestamp":1620691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The prediction of taxi demand service has become a recently attractive area of research along with large-scale and potential applications in the intelligent transportation system. The demand process is divided into two main parts: Picking-up and dropping-off demand based on passenger habit. Taxi demand prediction is a great concept for drivers and passengers, and is designed platforms for ride-hailing and municipal managers. The majority of research has focused on forecasting the pick-up part of demand service and specifying the interconnection of spatial and temporal correlations. In this study, the main focus is to overcome the access point of non-registered users for having fake transactions using taxi services and predicting taxi demand pick-up and drop-off information. The integration of machine learning techniques and blockchain framework is considered a possible solution for this problem. The blockchain technique was selected as an effective technique for protecting and controlling the real-time system. Historical data analysis was processed by extracting the three higher related sections for the intervening time, namely closeness and trend. Next, the pick-up and drop-off taxi prediction task was processed based on constructing the components of multi-task learning and spatiotemporal feature extraction. The combination of feature embedding performance and Long Short-Term Memory (LSTM) obtain the pick-up and drop-off correlation by fusing the historical data spatiotemporal features. Finally, the taxi demand pick-up and drop-off prediction were processed based on the combination of the external factors. The experimental result is based on a real dataset in Jeju Island, South Korea, to show the proposed system\u2019s efficacy and performance compared with other state-of-art models.<\/jats:p>","DOI":"10.3390\/s21103314","type":"journal-article","created":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T11:30:16Z","timestamp":1620732616000},"page":"3314","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A Framework of Vehicular Security and Demand Service Prediction Based on Data Analysis Integrated with Blockchain Approach"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1520-1799","authenticated-orcid":false,"given":"Zeinab","family":"Shahbazi","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Institute of Information Science Technology, Jeju National University, Jejusi 63243, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1107-9941","authenticated-orcid":false,"given":"Yung-Cheol","family":"Byun","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Institute of Information Science Technology, Jeju National University, Jejusi 63243, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"58254","DOI":"10.1109\/ACCESS.2018.2873905","article-title":"The vehicular social network (VSN)-based sharing of downloaded geo data using the credit-based clustering scheme","volume":"6","author":"Huang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3874","DOI":"10.1109\/TVT.2017.2788441","article-title":"Mobility dataset generation for vehicular social networks based on floating car data","volume":"67","author":"Kong","year":"2018","journal-title":"IEEE Trans. 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