Short-Term Traffic State Prediction Based on the Critical Road Selection Optimization in Transportation Networks.

Tian Ma,Yilong Ren,Guanghong Gong,Jianli Liu

doi:10.1155/2021/9966382

Tian Ma, Yilong Ren + Show 2 more

Open Access

https://doi.org/10.1155/2021/9966382

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Abstract

Short-term traffic prediction under corrupted or missing data for large-scale transportation networks has become an important and challenging topic in recent decades. Since the critical roads have predictive power on their adjacent roads, this paper proposes a novel hybrid short-term traffic state prediction method based on critical road selection optimization. First, the utility function of the quality of service (QoS) for the critical roads in a large-scale road network is proposed based on the coverage and the data score. Then, the critical road selection optimization model in the transportation networks is presented by selecting an appropriate set of critical roads with the maximum proportion of the total calculation resources to maximize the utility value of the QoS. Also, an innovative critical road selection method is introduced, which is considering the topological structure and the mobility of the urban road network. Subsequently, the traffic speed of the critical roads is regarded as the input of the convolutional long short-term memory neural network to predict the future traffic states of the entire network. Experiment results on the Beijing traffic network indicate that the proposed method outperforms prevailing DL approaches in the case of considering critical road sections.

Highlights

Real-time traffic state prediction plays a vital role in traffic management and public service
The traffic speed of the critical roads is regarded as the input of the convolutional long short-term memory neural network to predict the future traffic states of the entire network
The utility function of the quality of service (QoS) for the critical roads in a large-scale road network is proposed based on the coverage and the data score. en, the critical road selection optimization in the transportation networks is presented by selecting an appropriate set of critical roads with the maximum proportion of the total calculation resources to maximize the utility value of the QoS

Summary

Introduction

Real-time traffic state prediction plays a vital role in traffic management and public service. Ese methods include support vector machine (SVM), Bayesian network, and neural network Among all these datadriven methods, deep learning approaches have proven effective in traffic state prediction. The deep learning approaches have highlighted the data quality in short-term traffic state prediction [5]. We propose a novel hybrid short-term traffic state prediction method based on critical road selection optimization. The traffic speed of the critical roads is regarded as the input of the convolutional long short-term memory neural network to predict the future traffic states of the entire network. To demonstrate the effectiveness of the proposed method, the numerical experiments using the traffic states depicted from GPS trajectory data in Beijing. E last section concludes the study and discusses future work

Literature Review

Case Study

Performance between Different Critical Road Selection

Summary and Conclusions