Explanatory prediction of traffic congestion propagation mode: A self-attention based approach

Short-term traffic flow forecasting, an important component of intelligent transportation systems (ITS), is a challenging research direction as forecasting itself is affected by a series of complex factors. As more and more attention is paid to the data itself, deep learning methods have attained mainstream popularity for accomplishing traffic flow prediction tasks. In recent years, the attention mechanism has been widely used in various fields thanks to its excellent result interpretation ability and its capability to improve the performance of neural network models. In terms of time series data prediction, LSTM has demonstrated its powerful time feature extraction capability. Because of its ability to efficiently and quickly extract spatial-temporal features, CNN is often used in combination with LSTM and attention mechanisms to obtain accurate traffic flow prediction forecast results. In this paper, we propose a short-term traffic flow prediction model based on self-attention, and test the performance of the model experimentally with real data. The model can achieve the best prediction results compared with other classical models. In addition, the temporal and spatial features extracted by the model have certain physical characteristics making results easier to interpret. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

Short-term traffic flow forecasting is a challenging research direction in intelligent transportation systems. Deep learning methods, attention mechanisms, LSTM, and CNN can be used together to improve prediction accuracy. This paper proposes a prediction model based on self-attention, which achieved the best results compared to other classical models in experimental tests.