Abstract
Accurate vehicle speed prediction is of great significance to the urban traffic intelligent control system. However, in terms of traffic speed prediction, the modules that integrate temporal and spatial features in the existing traffic speed prediction methods are effective in short-term prediction, but the medium-term or long-term prediction errors are relatively large. In order to reduce the errors of existing methods in short-term prediction and predict the medium-term and long-term traffic speed, this paper proposes a traffic speed prediction method that combines attention and Spatial–temporal features, referred to as ASTCN. Specifically, unlike previous methods, ASTCN can use the temporal attention convolutional network (ATCN) to separately extract temporal features from the traffic speed features collected by each sensor, and use the spatial attention mechanism to extract spatial features and then perform spatial–temporal feature fusion. Experiments on three real-world datasets show that the proposed ASTCN model outperforms the state-of-the-art baselines.
Highlights
Transportation plays a vital role in everyday life
In order to address the above problems, we proposed the ASTCN method, the main contributions are as follows: 1. In traffic speed prediction, the length of historical time steps and future time steps are regarded as significant factors, and Temporal attention convolutional networks are used to extract the traffic speed features observed by each observation device
We use three metrics to evaluate the prediction performance of different traffic speed prediction models. They are the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), which are represented by Formula 9, Formula 10, and Formula 11
Summary
Transportation plays a vital role in everyday life. According to a 2015 survey, the average driving time of American drivers is about 48 minutes per day[1]. Due to complex temporal and spatial features, accurate traffic speed prediction is a challenging problem. Traditional machine learning methods were developed to model more complex data, but they are still difficult to consider the spatial-temporal correlation of high-dimensional traffic data at the same time. Many researchers have been using some deep learning methods to process high-dimensional spatial-temporal data, that is, convolutional neural network (CNN) is used to extract spatial features of grid data effectively; Graph convolution neural network (GCN) is used to describe the spatial correlation of graph-based data. Yu et al.[8] proposed a new deep learning framework spatial-temporal graph convolution network (STGCN) to solve the problem of time series prediction in the field of transportation. This section introduces the transportation network structure, the description of the traffic speed prediction problem and the structure of the input and output data
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