An approach to urban traffic state estimation by fusing multisource information

Authors:
Qing-Jie Kong;Zhipeng Li;Yikai Chen;Yuncai Liu
Affiliations:
Department of Automation, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China;Key Laboratory of Embedded System and Service Computing, Tongji University, Shanghai, China;Northwestern University, Chicago, IL;Department of Automation, School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
Venue:
IEEE Transactions on Intelligent Transportation Systems
Year:
2009

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Abstract

This paper presents an information-fusion-based approach to the estimation of urban traffic states. The approach can fuse online data from underground loop detectors and global positioning system (GPS)-equipped probe vehicles to more accurately and completely obtain traffic state estimation than using either of them alone. In this approach, three parts of the algorithms are developed for fusion computing and the data processing of loop detectors and GPS probe vehicles. First, a fusion algorithm, which integrates the federated Kalman filter and evidence theory (ET), is proposed to prepare a robust, credible, and extensible fusion platform for the fusion of multisensor data. After that, a novel algorithm based on the traffic wave theory is employed to estimate the link mean speed using single-loop detectors buried at the end of links. With the GPS data, a series of technologies are combined with the Geographic Information Systems for Transportation (GIS-T) map to compute another linkmean speed. These two speeds are taken as the inputs of the proposed fusion platform. Finally, tests on the accuracy, conflict resistance, robustness, and operation speed by real-world traffic data illustrate that the proposed approach can well be used in urban traffic applications on a large scale.