文章摘要
Mengyang Huang,Menggang Zhu,Yunpeng Xiao,Yanbing Liu.[J].重庆邮电大学新办英文刊,2021,7(1):72-81
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Bayonet-corpus: a trajectory prediction method based on bayonet context and bidirectional GRU
Received: January 24, 2019  Revised: June 10, 2019
DOI:https://doi.org/10.1016/j.dcan.2020.03.002
中文关键词: 
英文关键词: Trajectory prediction;Bayonet-corpus;Traffic network modeling;Bidirectional gated recurrent unit
基金项目:This research is partially supported by the National Natural Science Foundation of China(Grant No.61772098), Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No.KJZD-K201900603, KJQN201900629) and Chongqing Graduate Education Teaching Reform Project (No.yjg183081).
AuthorInstitutionE-mail
Mengyang Huang School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China  
Menggang Zhu School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China  
Yunpeng Xiao School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China xiaoyp@cqupt.edu.cn 
Yanbing Liu School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China  
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中文摘要:
      
英文摘要:
      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.