Long-term urban traffic flow forecasting based on feature fusion and S-T transformer  

作　　者：Zhang Xijun Cui Yong Zhang Hong Xia Ziyao 

机构地区：[1]School of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,China

出　　处：《The Journal of China Universities of Posts and Telecommunications》2025年第1期61-73,共13页中国邮电高校学报(英文版)

基　　金：supported by the National Natural Science Foundation of China (62162040);the Gansu Province Higher Education Innovation Fund-Funded Project (2021A-028);the Gansu Province Science and Technology Program Funding Project (21ZD4GA028);the Gansu Provincial Science and Technology Plan Funding Key Project of Natural Science Foundation of China (22JR5RA226)。

摘　　要：As a fundamental component of intelligent transportation systems, existing urban traffic flow forecasting models tend to overlook the spatio-temporal and long-term time-dependent patterns that characterize transportation networks. Among these, the long sequence time-series forecasting(LSTF) model is susceptible to the issue of gradient disappearance, which can be attributed to the influence of a multitude of intricate factors. Accordingly, in this paper, the standpoint of multi-feature fusion was studied, and a traffic flow forecasting network model based on feature fusion and spatio-temporal transformer(S-T transformer)(STFFN) was proposed. The model combined predictive recurrent neural network(Pred RNN) and S-T transformer to dynamically capture the spatio-temporal dependence and long-term time-dependence of traffic flow, thereby achieving a certain degree of model interpretability. A novel gated residual network-2(GRN-2) was proposed to investigate the potential relationship between multivariate features and target values. Furthermore, a hybrid quantile loss function was devised to alleviate the gradient disappearance in LSTF problems effectively. In extensive real experiments, the rationality and effectiveness of each network of the model were demonstrated, and the superior forecasting performance was verified in comparison to existing benchmark models.

关 键 词：traffic flow forecasting multi-feature fusion TRANSFORMER INTERPRETABILITY 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]