考虑网联汽车信息安全的交通流短时预测方法

Short-term Traffic Flow Prediction Method Considering Information Security for Connected Vehicles

摘要:

针对智能网联汽车因网络攻击或干扰造成的信息安全及数据缺失问题，提出一种基于数据补全的交通流状态短时预测方法。首先，基于边缘计算任务卸载模型，对智能网联汽车V2X通信过程的异常数据动态辨识；其次，提出一种具有数据补全机制的图嵌入长短期神经网络模型，实现网联汽车缺失数据补全；再次，通过补全后的完整数据集构建神经网络模型，完成短时交通流状态预测；最后，选取北京市典型路段进行实验验证。结果表明，该模型应用后交通流状态短时预测效果显著提高，与其他方法相比预测误差最大降低87.4%，预测效果与实际交通流状态相比准确率达到95％，为智能网联环境下车辆信息安全与交通资源动态优化提供理论支持和技术方案。

Abstract:

Aiming at the information security and data missing caused by network attacks or interference for ICVs （Intelligent Connected Vehicle）， this paper proposes a short-term prediction method of traffic flow state based on data imputation. Firstly， based on the edge computing task offloading model， the abnormal data of the V2X （vehicle to everything， V2X） communication process of ICVs is identified dynamically. Secondly， a graph embedding （GE） with data imputation mechanism and long short-term memory （LSTM） neural network model is proposed to impute the missing data. Thirdly， the neural network model is established based on the complete data sets to realize the short-term traffic flow state prediction. Finally， by the proposed model applied in Beijing for field test， the final results show that the short-term prediction effect of traffic flow state is significantly improved. In comparison with other methods， the prediction error is reduced by 87.4% and the accuracy of the prediction effect is 95% according to the actual traffic flow state， which provides a novel theoretical support and technical solution for vehicular information security and dynamic optimization of traffic resources in the intelligent networked environment.