Traffic State Estimation Per Lane in Highways with Connected Vehicles

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Journal Title
Journal ISSN
Volume Title
Conference article
Date
2017
Major/Subject
Mcode
Degree programme
Language
en
Pages
921 - 928
Series
Transportation Research Procedia, Volume 27
Abstract
A model-based traffic state estimation approach is developed for per-lane density estimation as well as on-ramp and off-ramp flows estimation for highways in presence of connected vehicles, namely, vehicles that are capable of reporting information to an infrastructure-based system. Three are the basic ingredients of the developed estimation scheme: (1) a data-driven version of the conservation-of-vehicles equation (in its time- and space-discretized form); (2) the utilization of position and speed information from connected vehicles\x92 reports, as well as total flow measurements obtained from a minimum number (sufficient for the observability of the model) of fixed detectors, such as, for example, at the main entry and exit of a given highway stretch; and (3) the employment of a standard Kalman filter. The performance of the estimation scheme is evaluated for various penetration rates of connected vehicles utilizing real microscopic traffic data collected within the Next Generation SIMulation (NGSIM) program. It is shown that the estimation performance is satisfactory, in terms of a suitable metric, even for low penetration rates of connected vehicles. The sensitivity of the estimation performance to variations of the model parameters (two in total) is also quantified, and it is shown that, overall, the estimation scheme is little sensitive to the model parameters.
Description
20th EURO Working Group on Transportation Meeting, EWGT 2017, 4-6 September 2017, Budapest, Hungary
Keywords
Connected vehicles, Traffic state estimation
Other note
Citation
Bekiaris-Liberis , N , Roncoli , C & Papageorgiou , M 2017 , ' Traffic State Estimation Per Lane in Highways with Connected Vehicles ' , Transportation Research Procedia , vol. 27 , pp. 921 - 928 . https://doi.org/10.1016/j.trpro.2017.12.057