Highlights We provide a novel digraph-based representation of hybrid urban traffic networks. We introduce Graph Fourier Transform towards the digraph convolution method. We construct sub-graphs through spanning trees for congestion recognition. Experimental results show that the model achieves the state-of-the-art performance.
Abstract Congestion recognition is the prerequisite for traffic control and management, vehicle routing, and many other applications in intelligent transportation systems. Different types of roads with traffic facilities provide multi-source heterogeneous field traffic data, which contain the fundamental information and distinct features for congestion recognition. To exploit these traffic big data, in this paper, we propose a machine learning-based framework to tackle the congestion recognition problem. It can be divided in two parts, a digraph-based representation for hybrid urban traffic network and a Dirgraph Convolutional Neural Network (DGCN)-based learning model. At first, the representation incorporates the fundamental traffic variables with the correlation of different traffic flows, and partially decouples the global network topology from local traffic information. And then, to proceed with digraph-based samples, a new type of graph feature extraction method is introduced and the graph Fourier transform is defined accordingly. This distinguishes the proposed model from the conventional graph convolutional networks. Comprehensive experiments are conducted based on real traffic data. The results demonstrate the advantages of the proposed framework over the existing congestion recognition methods.
Congestion recognition for hybrid urban road systems via digraph convolutional network
2020-11-02
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
CHARACTERISING CONGESTION ON URBAN ROAD NETWORKS
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