Abstract
Aiming at the problems that current predicting models are incapable of extracting the inner rule of the traffic flow sequence in traffic big data, and unable to make full use of the spatio-temporal relationship of the traffic flow to improve the accuracy of prediction, a Bi-directional Regression Neural Network (BRNN) is proposed in this paper, which can fully apply the context information of road intersections both in the past and the future to predict the traffic volume, and further to make up the deficiency that the current models can only predict the next-moment output according to the time series information in the previous moment. Meanwhile, a vectorized code to screen out the intersections related to the predicting point in the road network and to train and predict through inputting the track data of the selected intersections into BRNN, is designed. In addition, the model is testified through the true traffic data in partial area of Shen Zhen. The results indicate that, compared with current traffic predicting models, the model in this paper is capable of providing the necessary evidence for traffic guidance and control due to its excellent performance in extracting the spatio-temporal feature of the traffic flow series, which can enhance the accuracy by 16.298% on average.
Highlights
In many cities of China, frequent traffic congestion in the main road intersections, especially during the rush hours, has brought an increasingly severe challenge to the urban road traffic system [1,2].To improve traffic safety and efficiency, some researchers are connecting vehicles to each other and to the road infrastructure [3]
Chen Xiaobo et al analyzed the urban traffic road network through a model constructed by combining the Least Squares Support Vector Regression (SVR) (LSSVR) and the Genetic Algorithm (GA), which can simulate the variation rules to accomplish the prediction of traffic flow [15]
This paper, according to the TensorFlow platform, designed the vectorized code to extract the spatial feature of the intersections and to screen out the intersections related closely to the predicting point, as well as to train and predict through inputting the track vector matrix of those intersections into the Bi-directional Regression Neural Network (BRNN)
Summary
In many cities of China, frequent traffic congestion in the main road intersections, especially during the rush hours, has brought an increasingly severe challenge to the urban road traffic system [1,2]. Chen Xiaobo et al analyzed the urban traffic road network through a model constructed by combining the Least Squares SVR (LSSVR) and the Genetic Algorithm (GA), which can simulate the variation rules to accomplish the prediction of traffic flow [15]. Those mixed models above have succeeded in integrating the advantages and to some extent optimized the prediction, the enormous difficulty in combination and different results from various combinations have negatively impacted the actual effect.
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