A Hybrid LSTM Network for Long-Range Vehicle Trajectory Prediction Based on Adaptive Chirp Mode Decomposition

Vehicle trajectory prediction is essential for intelligent transportation system and smart city, but the prediction of long-range trajectory is still challenging. Since the speed of a moving vehicle is relatively high, coordinates of discrete trajectory points may vary from 0 to 10km in long-range trajectory prediction. Moreover, the vehicle trajectory contains random noises due to the instability of GPS signals in urban areas and the error accumulation effect of the other sensors for the vehicle positioning. To address this issue, the discrete trajectory data is treated as signal and multiple signal components are extracted from the original data through the adaptive chirp mode decomposition (ACMD) algorithm to capture spatial information at different spatial scales and filter out chaotic noises caused by other assisted positioning sensors simultaneously. To overcome error propagation of LSTM, since the quality of long-range predictions relies on short-range prediction accuracies, a Hybrid LSTM model is proposed based on a combination network of forward LSTM, BILSTM and backward LSTM. Taking advantage of an Improved Whale Optimization Algorithm (IWOA) to optimize the hyperparameters of the Hybrid-LSTM model, the embedding layer decomposes the input trajectories and further learns and trains through the Hybrid LSTM layer to effectively overcome the large prediction errors. Extensive experiments based on our dataset and a public taxi trajectory dataset show that the Hybrid LSTM model based on ACMD and IWOA outperforms the existing state-of-the-art methods in terms of accuracy and stability.