An Integrated LSTM Prediction Method Based on Multi-scale Trajectory Space

Aiming at the low prediction accuracy caused by instability of trajectory such as multiple path choices, local abnormal path and flexible step length, an integrated LSTM prediction method based on multi-scale trajectory space (MILSTM) is proposed to predict the coordinate of latitude and longitude. Firstly, the multi-scale fuzzy trajectory space is constructed with the sharing information of similar trajectory to reduce restriction of the road network, and highlight the trajectory intention, meanwhile fuzzy the behavior details in different scales. Then the LSTM models in all scales are integrated by the optimal weight matrix to predict the final coordinates. And the simulation results on trajectory data of Shanghai verified that compared with the classic LSTM model, the expansion of the dataset caused by the fuzzy scale can reduce the prediction error by about 10%, and the multi-scale and integration can effectively suppress the prediction error caused by the trajectory instability, with the increasing instability, the error is reduced by between 10% and 25%.