基于门控循环单元的车辆跟驰行为仿真模型

GRU-Based Car-Following Behavior Simulation Model

驾驶员记忆效应能有效提高车辆跟驰行为中的加速度预测准确率,结合General Motors(GM)跟驰模型与门控循环单元网络建立新的车辆跟驰模型。通过数据预处理获得有相似驾驶行为的小型车间车辆跟驰数据,校准新模型,从而确定模型的最优参数与结构,依据车辆跟驰特性通过仿真验证了模型有效性,与神经网络、支持向量回归进行对比,仿真结果证明,结合了L-BFGS优化的GRU车辆跟驰模型比仅考虑前导车与跟驰车间瞬时相互作用的车辆跟驰模型,能得到更高的仿真精度和稳定性。

The accuracy of acceleration prediction can be effectively improved by the driver’s memory in car-following behavior.A new car-following model based on the General Motors(GM)and the gate control unit(GRU)is proposed.The car-following data between small vehicles with similar driving behavior are obtained by data preprocessing.The established model is calibrated by the car-following data,and the optimal parameters and structure of the model are determined.According to car-following characteristics,the effectiveness of model is verified by simulation.It is confirmed that the model has high robustness and improved simulation accuracy comparing with the traditional models.