Short‐term traffic flow prediction based on AST‐MTL‐CNN‐GRU - Zijing Yang; Cheng Wang (IET Intelligent Transport Systems, 2023)
Abstract To address the current objective of short‐term traffic flow prediction on roads, this paper aims to predict not only the future traffic flow on roads, but also the average speed of future roads. Specifically, the authors propose a short‐term traffic flow prediction method based on the Convolutional Neural Network—Gate Recurrent Unit—Multi Task Learning‐time‐separated attention mechanism (AST‐MTL‐CNN‐GRU). Firstly, the authors add a time‐separated attention mechanism that can effectively generate attention matrices independently for different locations and times of the day. Secondly, the three main parameters of traffic flow, average speed and occupancy are combined in pairs as input data while the multi‐task learning is applied to predict the future traffic flow and average speed on the road. Finally, the CNNs are used to extract the spatial features and the GRUs are used to extract temporal features. Compared with the baseline models (diffusion CNNs (DCRNN), 1D‐CNN‐long short‐term memory (1DCNN‐LSTM), convolutional LSTM (ConvLSTM), graph WaveNet and ST‐ResNet), the authors’ proposed model has high performance in terms of accuracy, stability and robustness. A large number of ablation experiments are conducted based on five‐fold cross validation using the Quanzhou city dataset and the California dataset. The experimental results show that the combination of average speed and occupancy for input has higher prediction accuracy. In addition, the authors’ proposed model can outperform the other models being compared in terms of accuracy, stability and robustness even with noise added.