Real-Time Traffic Speed Estimation With Graph Convolutional Generative Autoencoder

Real-time traffic speed estimation is an essential component of intelligent transportation system (ITS) technologies. It is the foundation of modern transportation control and management applications. However, the existing traffic speed acquisition systems can only provide real-time speed measuremen...

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Bibliographic Details
Published inIEEE transactions on intelligent transportation systems Vol. 20; no. 10; pp. 3940 - 3951
Main Authors Yu, James Jian Qiao, Gu, Jiatao
Format Journal Article
LanguageEnglish
Published New York IEEE 01.10.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Computer simulation
Data models
data-driven model
deep learning
Estimation
generative adversarial network
Global Positioning System
graph convolutional network
Intelligent transportation systems
Learning
Real time
Real-time systems
Roads
Sensors
Speed limits
Traffic estimation
Traffic information
Traffic speed
Transportation networks
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Summary:Real-time traffic speed estimation is an essential component of intelligent transportation system (ITS) technologies. It is the foundation of modern transportation control and management applications. However, the existing traffic speed acquisition systems can only provide real-time speed measurements of a small number of roads with stationary speed sensors and crowdsourcing vehicles. How to utilize this information to provide traffic speed maps for transportation networks is becoming a key problem in ITSs. In this paper, we present a novel deep-learning model called graph convolutional generative autoencoder to fully address the real-time traffic speed estimation problem. The proposed model incorporates the recent development in deep-learning techniques to extract the spatial correlation of the transportation network from the input incomplete historical data. To evaluate the proposed speed estimation technique, we conduct comprehensive case studies on a real-world transportation network and vehicular traces. The simulation results demonstrate that the proposed technique can notably outperform existing traffic speed estimation and deep-learning techniques. In addition, the impact of dataset properties and control parameters is investigated.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2019.2910560