Abstract: In large-scale dynamic traffic scenarios,predicting vehicle driving behavior quickly and accurately is one of the most challenging issues in the field of intelligent traffic driving.The prediction of vehicle driving behavior should consider not only the efficiency of communication,but also the historical vehicle trajectory and the interaction between vehicles.Considering the above factors,this paper proposes a communication-efficient distributed machine learning framework based on edge-enhanced graph convolutional neural networks(EGCN-CeDML).Compared with the centralized prediction framework on a single device,EGCN-CeDML is a communication-efficient distributed machine learning framework,which does not need to transmit all the raw data to the cloud server,and directly stores,processes,and computes user data locally.This way of training neural networks on multiple edge devices relieves the pressure of centralized training neural networks,reduces the amount of transmitted data and communication latency,improves data processing efficiency,and preserves user privacy to a certain extent.EGCN-LSTM deployed on each edge device utilizes the edge-enhanced attention mechanism and the feature transfer mechanism of the graph convolutional neural network to promptly extract and transfer the interaction information between vehicles when the number of surrounding vehicles increases to more than a dozen,ensuring more accurate prediction performance and lower time complexity.In addition to vehicle driving behavior prediction,each edge device can flexibly control the type and scale of the neural network according to its own computing and storage capabilities,under the premise of ensuring the performance of the neural network,which is suitable for different application scenarios.The experimental results of EGCN-CeDML on public dataset NGSIMshow that the amount of data to be transmitted by only accounts for 21.56% of the centralized training.And the calculation time and prediction performance of EGCN-CeDML are better than those of previous models regardless of traffic complexity,with an accuracy rate of 0.939 1,a recall rate of 0.955 7,and an F1 score of 0.947 3.When the prediction time is one second,the prediction accuracy reaches 91.21%.Even if the number of vehicles increases,the algorithm maintains a low time complexity and is stable within 0.1 seconds.

Key words: Vehicle driving behavior prediction, Graph convolutional network, Edge enhancement, Attention mechanism, Distributed machine learning