Abstract
Traffic congestion in the adjacent region between the highway and urban expressway is becoming more and more serious. This paper proposes a traffic speed forecast method based on the Macroscopic Fundamental Diagram (MFD) and Gated Recurrent Unit (GRU) model to provide the necessary traffic guidance information for travelers in this region. Firstly, considering that the road traffic speed is affected by the macroscopic traffic state, the adjacent region between the highway and expressway is divided into subareas based on the MFD. Secondly, the spatial-temporal correlation coefficient is proposed to measure the correlation between subareas. Then, the matrix of regional traffic speed data is constructed. Thirdly, the matrix is input into the GRU prediction model to get the predicted traffic speed. The proposed algorithm’s prediction performance is verified based on the GPS data collected from the adjacent region between Beijing Highways and Expressway.
Highlights
With the continuous growth of the scale of China’s highway network and traffic volume, the traffic load of the intercity highway in some developed cities is increasing
Due to the restrictions of traffic management measures, trucks are not allowed to enter the urban area in fixed hours. ey can only drive on adjacent regions between the highway and urban expressway, which caused the trucks to accumulate, resulting in low road capacity and service level during peak hours, severe traffic congestion, and frequent traffic accidents
It is essential to conduct research on traffic states predicting at the intersection of highways and urban expressways and publish accurate traffic guidance information to travelers to alleviate traffic congestion
Summary
With the continuous growth of the scale of China’s highway network and traffic volume, the traffic load of the intercity highway in some developed cities is increasing. Luo et al proposed a short-term traffic flow prediction model based on deep learning combining the features of convolutional neural network and support vector regression classifier [16]. In this paper, based on the average roadway speed and flow data extracted from the truck GPS data, a short-time traffic flow prediction method combining MFD and GRU is proposed. E test results of real traffic flow data show that the method proposed in this paper has lower prediction errors and higher accuracy than the existing prediction models. It is a reasonable and effective method to predict short-time traffic flow.
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