A traffic conflict prediction method for merging areas based on trajectory pre-diction with graph neural network
To ensure the traffic safety and reduce traffic conflicts in this section of the highway,a traffic conflict prediction method was proposed for the merging area based on the trajectory prediction with graph neural network.The method included a trajectory prediction method based on spatio-temporal graph convolutional neural network and a traffic conflict prediction method based on pre-dicted trajectories.The Mirror-Traffic dataset was utilized for traffic conflict indicator threshold calculation.Applicable data were ob-tained through certain trajectory data processing methods for network model training and validation.The results showed that the PET threshold for severe conflicts was 2.0 s and that for minor conflicts was 5.36 s.The trajectory prediction method had an average dis-placement error of 1.5 m,a final displacement error of 2.1 m,and a time cost of 0.59 s.Compared with the other methods,the trajec-tory prediction of the proposed method had the best overall effect.For traffic conflict prediction,accuracy,precision,recall and F1 were used to evaluate the traffic conflict prediction model,and the results showed that the traffic conflict prediction was effective.The proposed method ensured the correctness of the prediction,enhanced the safety of traveling under the warning system,and improved the efficiency of the merging area under the warning system.
NETL
NSTL
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