Road traffic accident prediction based on GWO-LSTM model
KONG Weilin
LI Wendong
YANG Lizhu
ZHANG Luyu
WANG Qingbin
Abstract：In order to reduce the road traffic accident rate and minimize accident losses,the gray wolf optimizer(GWO)algorithm,which has strong global traversal and convergence properties,is used to optimize the initial learning rate,hidden layer nodes,regularization coefficient and other parameters in the long short term memory(LSTM)neural network.The GWO-LSTM road traffic accident prediction model is constructed.Taking the road traffic fatal accident data from 2000 to 2019 as the sample data,the traffic accident data is divided into monthly granularity,weekly granularity,and hourly granularity.The road traffic accident prediction results of the GWO-LSTM model,autoregressive moving average(ARMA)model,back propagation neural network(BPNN)model,and standard LSTM model are compared and analyzed.The results show that under the three time granularities,the GWO-LSTM model has the smallest average absolute percentage error and root mean square error,and high prediction accuracy.It can be used for road traffic accident prediction.
Keywords：traffic accidentLSTM neural networkGWO algorithmtime granularity
Publication Date：2023-12-30
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：8( 60-67 )