DS-TFSN-Based Vehicle Travel Time Prediction Method for Digital Twin System of Freeways

Abstract

In digital twin systems for freeways, it is essential to track individual vehicles. When sensing devices cannot fully cover an entire road, it is necessary to accurately predict the travel time of individual vehicles. Therefore, this paper proposes a dual-state traffic factor state network (DS-TFSN), which combines macro traffic states and micro vehicle travel states. Based on the DS-TFSN, a digital twin framework is proposed for freeways. This framework can realize long-distance freeway supervision and vehicle tracking by predicting the travel time of specific vehicles in unsupervised road sections to ascertain their driving process. As the core of digital twin frameworks of freeways, the freeway section travel time prediction model based on the DS-TFSN considers the interactions among macro factors, micro factors, and environmental factors. The model divides the macro traffic state and micro vehicle travel state, and adds them as inputs to the LSTM model. A new vehicle-specific deep learning method is proposed to improve the prediction accuracy in terms of the freeway section travel time. The results show that, for freeways, more accurate prediction results are achieved during both normal hours and holidays. The MAPE of the prediction results using the dual-state traffic factor state network decreases by 6.2%, at most, and the proportion of vehicles with a prediction error of less than 1 second per kilometer increases by 54%, at most.