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In order to learn the vehicle travel characteristics in the city of Ruian, we developed a common methodology for structuring travelers\u2019 complete information using the travel time threshold to recognize a single trip based on the automatic license plate reader data and built a trajectory reconstruction model integrated into the technique for order preference by similarity to an ideal solution and depth-first search to manage the vehicles\u2019 incomplete records phenomenon. In order to increase the practicability of the model, we introduced two speed indicators associated with actual data and verified the model\u2019s reliability through experiments. Our results show that the method would be affected by the number of missing records. The model and results of this work will allow us to further study vehicles\u2019 commuting characteristics and explore hot trajectories. <\/jats:p>","DOI":"10.1177\/1550147718755637","type":"journal-article","created":{"date-parts":[[2018,2,5]],"date-time":"2018-02-05T10:34:59Z","timestamp":1517826899000},"page":"155014771875563","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":31,"title":["Vehicle trajectory reconstruction from automatic license plate reader data"],"prefix":"10.1177","volume":"14","author":[{"given":"Haiyang","family":"Yu","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, School of Transportation Science and Engineering, Beihang University, Beijing, People\u2019s Republic of China"},{"name":"Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing, People\u2019s Republic of 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