{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T03:09:50Z","timestamp":1790305790700,"version":"4.1.0"},"reference-count":46,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,2,4]],"date-time":"2020-02-04T00:00:00Z","timestamp":1580774400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61741216, 61402367"],"award-info":[{"award-number":["61741216, 61402367"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shaanxi Science and Technology Co-ordination and Innovation Project","award":["2016KTTSGY01-03"],"award-info":[{"award-number":["2016KTTSGY01-03"]}]},{"name":"Special Scientific Research Project of Education Department of Shaanxi Province","award":["17JK0704"],"award-info":[{"award-number":["17JK0704"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Location prediction has attracted much attention due to its important role in many location-based services. The existing location prediction methods have large trajectory information loss and low prediction accuracy. Hence, they are unsuitable for vehicle location prediction of the intelligent transportation system, which needs small trajectory information loss and high prediction accuracy. To solve the problem, a vehicle location prediction algorithm was proposed in this paper, which is based on a spatiotemporal feature transformation method and a hybrid long short-term memory (LSTM) neural network model. In the algorithm, the transformation method is used to convert a vehicle trajectory into an appropriate input of the neural network model, and then the vehicle location at the next time is predicted by the neural network model. The experimental results show that the trajectory information of an original taxi trajectory is basically reserved by its shadowed taxi trajectory, and the trajectory points of the predicted taxi trajectory are close to those of the shadowed taxi trajectory. It proves that our proposed algorithm effectively reduces the information loss of vehicle trajectory and improves the accuracy of vehicle location prediction. Furthermore, the experimental results also show that the algorithm has a higher distance percentage and a shorter average distance than the other predication models. Therefore, our proposed algorithm is better than the other prediction models in the accuracy of vehicle location predication.<\/jats:p>","DOI":"10.3390\/info11020084","type":"journal-article","created":{"date-parts":[[2020,2,5]],"date-time":"2020-02-05T03:18:48Z","timestamp":1580872728000},"page":"84","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Vehicle Location Prediction Based on Spatiotemporal Feature Transformation and Hybrid LSTM Neural Network"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9516-1947","authenticated-orcid":false,"given":"Yuelei","family":"Xiao","sequence":"first","affiliation":[{"name":"School of Modern Posts, Xi\u2019an University of Post and Telecommunications, Xi\u2019an 710061, China"},{"name":"Shanxi Provincial Information Engineering Research Institute, Xi\u2019an 710075, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Nian","sequence":"additional","affiliation":[{"name":"School of Modern Posts, Xi\u2019an University of Post and Telecommunications, Xi\u2019an 710061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kolodziej, K.W., and Hjelm, J. 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