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Journal of Advanced Transportation
Volume 2018 (2018), Article ID 4106086, 14 pages
https://doi.org/10.1155/2018/4106086
Research Article

Traffic State Estimation Using Connected Vehicles and Stationary Detectors

1Swedish National Road and Transport Research Institute (VTI), 581 95 Linköping, Sweden
2Department of Science and Technology, Linköping University, 601 74 Norrköping, Sweden

Correspondence should be addressed to Ellen F. Grumert

Received 29 September 2017; Accepted 2 December 2017; Published 10 January 2018

Academic Editor: Fernando García

Copyright © 2018 Ellen F. Grumert and Andreas Tapani. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Real-time traffic state estimation is of importance for efficient traffic management. This is especially the case for traffic management systems that require fast detection of changes in the traffic conditions in order to apply an effective control measure. In this paper, we propose a method for estimating the traffic state and speed and density, by using connected vehicles combined with stationary detectors. The aim is to allow fast and accurate estimation of changes in the traffic conditions. The proposed method does only require information about the speed and the position of connected vehicles and can make use of sparsely located stationary detectors to limit the dependence on the infrastructure equipment. An evaluation of the proposed method is carried out by microscopic traffic simulation. The traffic state estimated using the proposed method is compared to the true simulated traffic state. Further, the density estimates are compared to density estimates from one detector-based method, one combined method, and one connected-vehicle-based method. The results of the study show that the proposed method is a promising alternative for estimating the traffic state in traffic management applications.