EP-LSTM: Novel prediction algorithm for moving object destination

Abstract

Predicting the destination of a moving object is a popular research subject in location-based services. By predicting destinations, suggestions can be offered to people regarding their trips. At present, there are problems such as data sparsity and long-term dependence based on historical trajectory prediction methods, which affect the accuracy of prediction. To solve data sparsity problem, this paper has devised an improved minimum description length method, which incorporates weighting parameters and optimizes the partitioning of trajectories with undirected complete graph. Long–short-term memory is a trajectory-prediction model that solves the problem of long-term dependence, but the model tends to have vanishing gradient issues when used to process longer sequences. This is because the hidden layers of long–short-term memory is largely affected by the lengths of sequences. Using embedded technology, the authors convert trajectory sequences into embedded vector sequences, and thus propose a deep-learning prediction model, EP-LSTM (Embedded Processing - Long Short Term Memory), which integrates embedded technology and long–short-term memory. The authors have conducted a great amount of testing with real data sets, comparing EP-LSTM with currently available predicting methods. The results have shown that EP-LSTM not only effectively solves data sparsity and long-term dependence but also achieves a high degree of prediction accuracy.