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Vehicle Location Prediction Based on Spatiotemporal Feature Transformation and Hybrid LSTM Neural Network

by Yuelei Xiao 1,2,* and Qing Nian 1
1
School of Modern Posts, Xi’an University of Post and Telecommunications, Xi’an 710061, China
2
Shanxi Provincial Information Engineering Research Institute, Xi’an 710075, China
*
Author to whom correspondence should be addressed.
Information 2020, 11(2), 84; https://doi.org/10.3390/info11020084
Received: 12 December 2019 / Revised: 1 February 2020 / Accepted: 1 February 2020 / Published: 4 February 2020
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. View Full-Text
Keywords: location prediction; spatiotemporal feature shadowing; feature static processing; semantic feature mapping; long short-term memory (LSTM); hybrid LSTM location prediction; spatiotemporal feature shadowing; feature static processing; semantic feature mapping; long short-term memory (LSTM); hybrid LSTM
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Xiao, Y.; Nian, Q. Vehicle Location Prediction Based on Spatiotemporal Feature Transformation and Hybrid LSTM Neural Network. Information 2020, 11, 84.

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