SpatioTemporal Position Prediction Model for Mobile Users Based on LSTM In Mobile Edge Computing (MEC), the services that a user receives change dynamically with location due to the useru0027s mobility. If we mine the useru0027s location data, predicting the useru0027s next location, we can get the useru0027s services to be used. It is convenient for the edge server to preload the useru0027s services. When users reaches predicted location, the edge servers near users provide timely services. Therefore, this paper proposes a Spatiotemporal Position Prediction Model (SPPM) for Mobile Users Based on LSTM (Long ShortTerm Memory) model in the mobile edge computing. Firstly, the time series feature extraction method is used to preprocess the historical location data of the mobile user. Next, the model uses the PCA data dimensionality reduction algorithm to process the data and then uses the LSTM model to predict the next spatiotemporal trajectory point of the mobile user. Finally, using the 17621 user trajectory data of the Geolife GPS trajectory data set, the algorithm is tested and verified. The experimental results show that the SPPM model proposed in this paper has higher prediction accuracy and more accurate prediction position.

Vehicle Location Prediction Based on Spatiotemporal Feature Transformation and Hybrid LSTM Neural Network Location prediction has attracted much attention due to its important role in many locationbased 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 shortterm 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.