{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T06:43:13Z","timestamp":1764225793139,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,3,20]],"date-time":"2022-03-20T00:00:00Z","timestamp":1647734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhi Cai","award":["No. 62072016"],"award-info":[{"award-number":["No. 62072016"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>In order to improve the effect of path planning in emergencies, the missing position imputation and velocity restoration in vehicle trajectory provide data support for emergency path planning and analysis. At present, there are many methods to fill in the missing trajectory information, but they basically restore the missing trajectory after analyzing a large number of datasets. However, the trajectory reduction method with few training sets needs to be further explored. For this purpose, a novel trajectory data cube model (TDC) is designed to store time, position, and velocity information hierarchically in the trajectory data. Based on this model, three trajectory Hierarchical Trace-Back algorithms HTB-p, HTB-v, and HTB-KF are proposed in this paper. Finally, experiments verify that conduct in a different number of sample sets, it has a satisfactory performance on information restoration of individual points of the trajectory and information restoration of trajectory segments.<\/jats:p>","DOI":"10.3390\/ijgi11030209","type":"journal-article","created":{"date-parts":[[2022,3,20]],"date-time":"2022-03-20T21:30:14Z","timestamp":1647811814000},"page":"209","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Novel Traffic Flow Reduction Method Based on Incomplete Vehicle History Spatio-Temporal Trajectory Data"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6305-5565","authenticated-orcid":false,"given":"Bowen","family":"Yang","sequence":"first","affiliation":[{"name":"School of Computing, Beijing University of Technology, Beijing 100124, China"}]},{"given":"Zunhao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computing, Beijing University of Technology, Beijing 100124, China"}]},{"given":"Zhi","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Computing, Beijing University of Technology, Beijing 100124, China"}]},{"given":"Dongze","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computing, Beijing University of Technology, Beijing 100124, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4214-3363","authenticated-orcid":false,"given":"Xing","family":"Su","sequence":"additional","affiliation":[{"name":"School of Computing, Beijing University of Technology, Beijing 100124, China"}]},{"given":"Limin","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Computing, Beijing University of Technology, Beijing 100124, China"}]},{"given":"Zhiming","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Computing, Beijing University of Technology, Beijing 100124, China"},{"name":"Institute of Software, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data, Chinese Academy of Sciences, Beijing 100144, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1562","DOI":"10.1109\/TAES.2021.3054690","article-title":"Autonomous Ground Vehicle Path Planning in Urban Environments Using GNSS and Cellular Signals Reliability Maps: Models and Algorithms","volume":"57","author":"Ragothaman","year":"2021","journal-title":"IEEE Trans. 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