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Generic approaches to estimating freeway traffic state and percentage of connected vehicles with fixed and mobile sensing

Zhao Mingming, Roncoli Claudio, Wang Yibing, Bekiaris-Liberis Nikolaos, Guo Jingqiu, Cheng Senlin

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URI: http://purl.tuc.gr/dl/dias/9265BC4D-80B3-469C-9BBF-CA9D24A59D57
Year 2021
Type of Item Peer-Reviewed Journal Publication
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Bibliographic Citation M. Zhao, C. Roncoli, Y. Wang, N. Bekiaris-Liberis, J. Guo and S. Cheng, "Generic approaches to estimating freeway traffic state and percentage of connected vehicles with fixed and mobile sensing," IEEE Trans. Intell. Transp. Syst., early access, doi: 10.1109/TITS.2021.3121181. https://doi.org/10.1109/TITS.2021.3121181
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Summary

Three filtering-based approaches to freeway traffic state estimation are studied using measurements from connected vehicles and also a minimum number of fixed detectors. These approaches are: Method 1 based on EKF and the second-order traffic flow model METANET, Methods 2 and 3 based on KF and the conservation equation that is driven by mean speed data of connected vehicles under a speed-uniformity assumption. Each method is capable of estimating segment traffic flow variables (speeds, densities, and flows) as well as segment market penetration rates (MPRs) of connected vehicles. The three methods are evaluated and compared in depth using NGSIM data with respect to their traffic state estimator design, data requirements, capabilities, limitations in the mixed sensing case. Recommendations are given about the choice of methods over the range of MPR.

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