A Sequence and Network Embedding Method for Bus Arrival Time Prediction Using GPS Trajectory Data Only

Abstract

Recently, achieving high accuracy in bus arrival time prediction has become a hot research topic. Extensive research has been conducted around this topic, among which fusion of multi-source heterogeneous information is the mainstream. However, using only GPS trajectory data to predict the arrival time of buses is meaningful for small cities since they do not have easy access to external sources of data. Therefore, we propose a novel deep-learning model based on sequence and network(i.e., graph) embedding (SGE-net) which adopts only GPS trajectories as input and achieves accurate bus arrival time prediction without external data. Specifically, the sequence embedding part, composed of the Echo State Network (ESN), the Auto-Encoder (AE), and the k-means cluster method, is constructed to extract the complex and potential sequence patterns from a large number of trajectory sequences, and cluster different patterns of trajectories. Then, the network embedding part with the node2vec algorithm is proposed to capture the spatial correlation among bus stops on different lines under the condition of limited data. Finally, a fusion prediction part based on the specifically designed parallel Gated Recurrent Unit (GRU) networks is introduced to combine sequence and network embedding vectors with hidden outputs of the GRUs and then get the prediction. Both real-world offline and online experiments are conducted based on GPS data of bus lines in QiTaiHe city and ShiJiaZhuang city. The results clearly show that the SGE-net outperforms the baseline models, and validate the effectiveness and robustness of the SGE-net.