Driver Driving Behavior Analysis Method Based on PSO-NARX Network
Comfortability,punctuality and energy efficiency are important indicators for evaluating the level of automatic driving of high-speed trains.Through continuous learning from the driving behaviors of excellent drivers,the automatic driving performance of trains can be optimized,which can promote the development of high-speed train automatic driving technology.Based on on-site train operation data,this paper proposed a nonlinear auto-regression with exogenous inputs(NARX)network analysis method for analyzing the driving behavior of train drivers.The method constructed an NARX network model with temporal characteristics,and selected multiple parameters that affect driver decisions as inputs.At the same time,the weight and threshold of the network were optimized using particle swarm optimization(PSO),to pre-dict the operation situation of the next train.The results show that the PSO-NARX network analysis model proposed in this paper performs better than the back propagation(BP)neural network,PSO-BP,and NARX.Compared with BP al-gorithm,the iteration steps are reduced by 373 steps,with an error reduction of 8.382％and a correlation coefficient of 90.117％.Through this prediction,the performance indicators of the automatic driving equipment of the train can be op-timized to ensure the punctuality of the train,while improving the comfort of passengers.
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