{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T05:29:35Z","timestamp":1772774975475,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T00:00:00Z","timestamp":1724976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Collaborative Innovation Center for Natural Resources Planning and Marine Technology of Guangzhou","award":["2023B04J0301"],"award-info":[{"award-number":["2023B04J0301"]}]},{"name":"Collaborative Innovation Center for Natural Resources Planning and Marine Technology of Guangzhou","award":["2020B12120219"],"award-info":[{"award-number":["2020B12120219"]}]},{"name":"Collaborative Innovation Center for Natural Resources Planning and Marine Technology of Guangzhou","award":["2022YFB3904105"],"award-info":[{"award-number":["2022YFB3904105"]}]},{"name":"Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning","award":["2023B04J0301"],"award-info":[{"award-number":["2023B04J0301"]}]},{"name":"Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning","award":["2020B12120219"],"award-info":[{"award-number":["2020B12120219"]}]},{"name":"Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning","award":["2022YFB3904105"],"award-info":[{"award-number":["2022YFB3904105"]}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2023B04J0301"],"award-info":[{"award-number":["2023B04J0301"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2020B12120219"],"award-info":[{"award-number":["2020B12120219"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2022YFB3904105"],"award-info":[{"award-number":["2022YFB3904105"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>A fine-grained metro trip contains complete information on user mobility, including the original station, destination station, departure time, arrival time, transfer station(s), and corresponding transfer time during the metro journey. Understanding such detailed trip information within a city is crucial for various smart city applications, such as effective urban planning and public transportation system optimization. In this work, we study the problem of detecting fine-grained metro trips from cellular trajectory data. Existing trip-detection approaches designed for GPS trajectories are often not applicable to cellular data due to the issues of location noise and irregular data sampling in cellular data. Moreover, most cellular data-based methods focus on identifying coarse-grained transportation modes, failing to detect fine-grained metro trips accurately. To address the limitations of existing works, we propose a novel and efficient fine-grained metro-trip detection (FGMTD) model in this work. By considering both the local and global spatial\u2013temporal characteristics of a trajectory and the metro network, FGMTD can effectively mitigate the effects of location noise and irregular data sampling, ultimately improving the accuracy and reliability of the detection process. In particular, FGMTD employs a spatial\u2013temporal hidden Markov model with efficient index strategies to capture local spatial\u2013temporal characteristics from individual positions and metro stations, and a weighted trip-route similarity measure to consider global spatial\u2013temporal characteristics from the entire trajectory and metro route. We conduct extensive experiments on two real datasets to evaluate the effectiveness and efficiency of our proposed approaches. The first dataset contains cellular data from 30 volunteers, including their actual trip details, while the second dataset consists of data from 4 million users. The experiments illustrate the significant accuracy of our approach (with a precision of 87.80% and a recall of 84.28%). Moreover, we demonstrate that FGMTD is efficient in detecting fine-grained trips from a large amount of cellular data, achieving this task within 90 min of processing a day\u2019s data from 4 million users.<\/jats:p>","DOI":"10.3390\/ijgi13090314","type":"journal-article","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T09:39:32Z","timestamp":1725010772000},"page":"314","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Fine-Grained Metro-Trip Detection from Cellular Trajectory Data Using Local and Global Spatial\u2013Temporal Characteristics"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3950-9360","authenticated-orcid":false,"given":"Guanyao","family":"Li","sequence":"first","affiliation":[{"name":"School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China"},{"name":"Guangzhou Urban Planning & Design Survey Research Institute Co., Ltd., Guangzhou 510060, China"},{"name":"Collaborative Innovation Center for Natural Resources Planning and Marine Technology of Guangzhou, Guangzhou 510060, China"},{"name":"Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruyu","family":"Xu","sequence":"additional","affiliation":[{"name":"Transportation College, Jilin University, Changchun 130000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingyan","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Art and Science, New York University, New York, NY 10012, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingdong","family":"Deng","sequence":"additional","affiliation":[{"name":"Guangzhou Urban Planning & Design Survey Research Institute Co., Ltd., Guangzhou 510060, China"},{"name":"Collaborative Innovation Center for Natural Resources Planning and Marine Technology of Guangzhou, Guangzhou 510060, China"},{"name":"Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangzhou Urban Planning & Design Survey Research Institute Co., Ltd., Guangzhou 510060, China"},{"name":"Collaborative Innovation Center for Natural Resources Planning and Marine Technology of Guangzhou, Guangzhou 510060, China"},{"name":"Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deshi","family":"Di","sequence":"additional","affiliation":[{"name":"Guangzhou Urban Planning & Design Survey Research Institute Co., Ltd., Guangzhou 510060, China"},{"name":"Collaborative Innovation Center for Natural Resources Planning and Marine Technology of Guangzhou, Guangzhou 510060, China"},{"name":"Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuanbao","family":"Zhao","sequence":"additional","affiliation":[{"name":"Guangzhou Urban Planning & Design Survey Research Institute Co., Ltd., Guangzhou 510060, China"},{"name":"Collaborative Innovation Center for Natural Resources Planning and Marine Technology of Guangzhou, Guangzhou 510060, China"},{"name":"Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guochao","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangzhou Urban Planning & Design Survey Research Institute Co., Ltd., Guangzhou 510060, China"},{"name":"Collaborative Innovation Center for Natural Resources Planning and Marine Technology of Guangzhou, Guangzhou 510060, China"},{"name":"Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cai, Z., Wang, J., Li, T., Yang, B., Su, X., Guo, L., and Ding, Z. 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