Prediction of the vehicle lane‐changing distance in an urban inter‐tunnel weaving section based on wavelet transform and dual‐channel neural network

Abstract

AbstractVehicle lane‐changing behaviour is often regarded as transient traffic behaviour while ignoring behavioural characteristics of the lane‐changing process. A combined prediction model based on wavelet transform (WT) and dual‐channel neural network (DCNN) is proposed to explore the selection behaviour of lane‐changing distance by taking lane‐changing behaviour in an urban inter‐tunnel weaving section. Firstly, the extracted lane‐changing data are analysed for correlation and noise reduction, and the main factors affecting lane‐changing distance are taken as input variables of the model. The trajectory data of the inter‐tunnel weaving section of the “Jiuhuashan‐Xi'anmen” tunnel in Nanjing, China, are used to improve the prediction of vehicle lane‐changing distance by training the model. The results show that the proposed WT‐DCNN model has high prediction performance when compared with existing artificial neural network (ANN), DCNN and wavelet neural network (WNN) models. The characterization and study of the typical lane‐changing behaviour in the weaving section can lay the theoretical foundation for the development of an urban inter‐tunnel weaving section management scheme.