A Visual Analytics of Urban Traffic Events Using Social Media Data

doi:10.16182/j.issn1004731x.joss.20-0962

Abstract

Abstract:

Traffic text data in social media can supplement traffic flow information, for which a visual analysis method for traffic events is proposed. A text processing model is designed to process the social media data and extract the description information of traffic event. The vector representation of road node attributes is learned based on graph embedding algorithm, and a road similarity model is estbalished. A prediction model of the traffic event is built based on the road similarity and the kernel density model. An interactive visual analysis system is designed to carry out visual analysis. Though traffic information extraction, road similarity measurement and traffic event interaction prediction, the effectiveness of the proposed method is verified and can assist traffic department management decisions.

Key words: data visualization, information extraction, road similarity, traffic prediction, visual analytics

CLC Number:

TP391.9

Xiangping Wu, Lijun Ping, Dongshi Xu. A Visual Analytics of Urban Traffic Events Using Social Media Data[J]. Journal of System Simulation, 2022, 34(5): 1140-1151.

Figures/Tables 6

References 27

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