Bayonet-corpus: a trajectory prediction method based on bayonet context and bidirectional GRU

Abstract

Predicting travel trajectory of vehicles can not only provide personalized services to users, but also have a certain effect on traffic guidance and traffic control. In this paper, we build a Bayonet-Corpus based on the context of traffic intersections, and use it to model a traffic network. Besides, Bidirectional Gated Recurrent Unit (Bi-GRU) is used to predict the sequence of traffic intersections in one single trajectory. Firstly, considering that real traffic networks are usually complex and disorder and cannot reflect the higher dimensional relationship among traffic intersections, this paper proposes a new traffic network modeling algorithm based on the context of traffic intersections: inspired by the probabilistic language model, a Bayonet-Corpus is constructed from traffic intersections in real trajectory sequence, so the high-dimensional similarity between corpus nodes can be used to measure the semantic relation of real traffic intersections. This algorithm maps vehicle trajectory nodes into a high-dimensional space vector, blocking complex structure of real traffic network and reconstructing the traffic network space. Then, the bayonets sequence in real traffic network is mapped into a matrix. Considering the trajectories sequence is bidirectional, and Bi-GRU can handle information from forward and backward simultaneously, we use Bi-GRU to bidirectionally model the trajectory matrix for the purpose of prediction.