{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T09:28:35Z","timestamp":1758274115834,"version":"3.40.5"},"reference-count":25,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,4,29]],"date-time":"2022-04-29T00:00:00Z","timestamp":1651190400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Sensors"],"published-print":{"date-parts":[[2022,4,29]]},"abstract":"<jats:p>Under the social background of the rapid development of big data, human beings have gradually moved towards a comprehensive digital era, and people have begun to apply digital technology in various fields. Therefore, it is necessary to study methods that can efficiently process big data and solve problems such as data intelligence. Intelligent products and applications that we use everywhere in our lives are changing people\u2019s lives. Among them, location service is one of the important technical supports. No matter which software is opened on the mobile phone, it will prompt us to open location service. Location prediction is an important part of location-based services and plays an indispensable role in the recommendation system and urban resource planning. At present, GPS-based trajectory data is widely concerned in position prediction task. GPS trajectory data belongs to spatiotemporal series data, which not only contains time and position information but also contains abundant context information in trajectory sequence. Most of the traditional position prediction methods focus on the position sequence in GPS trajectory but do not fully mine the context information in the trajectory, which leads to poor prediction results. The intelligent optimization algorithm greatly enriches the optimization technology and provides a feasible solution for those combinatorial optimization problems which are difficult to deal with by traditional optimization technology. GPS trajectory data has the advantages of wide coverage, quick update, easy collection, and low cost, and it also implies abundant road network information. As a result, GPS trajectory data of users has gradually become a new data source for automatic construction of urban road network and has also become a research hotspot of many scholars. In this paper, the idea of swarm intelligence algorithm and swarm intelligence algorithm are used to design and implement a data analysis and behavior prediction system for calculating GPS user trajectory, aiming at solving the shortcomings of existing GPS tracking and positioning.<\/jats:p>","DOI":"10.1155\/2022\/7554560","type":"journal-article","created":{"date-parts":[[2022,4,29]],"date-time":"2022-04-29T14:52:11Z","timestamp":1651243931000},"page":"1-11","source":"Crossref","is-referenced-by-count":4,"title":["Research on GPS User Trajectory Analysis and Behavior Prediction Based on Swarm Intelligence Algorithm"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6510-612X","authenticated-orcid":true,"given":"Guangyong","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Civil Engineering, Guangdong Construction Polytechnic, Guangzhou 510440, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"issue":"4","key":"1","first-page":"697","article-title":"Intelligent swarm optimization filtering algorithm for target tracking","volume":"14","author":"X. 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