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- W2567651889 abstract "In recent literature, the description and understanding of traffic conditions in urban networks on an aggregated level has been popularized. A promising framework is the macro fundamental diagram (MFD), relating average flow and average density in a more or less homogeneous urban network. The application of the MFD in traffic management and control are manifold. It has been shown that it can be used for an efficient perimeter control at city entries or for an evaluation of traffic policies at an aggregated level. However, its implementation requires an accurate estimation with the data sources available. Especially loop detectors data (LDD) and floating car data (FCD), were examined separately in respect to their accuracy estimating the MFD. One important factor is the network coverage of the source considered. Only certain streets have loop detectors installed and only certain vehicles (or drivers) are equipped with devices sending information about their whereabouts. Having less data of that one source decreases the accuracy of the MFD. This master?s thesis proposes to estimate the MFD based on both data sources simultaneously. Thereby successful fusion methods were developed, where the LDD and the FCD are fused in respect to their accuracy and their network coverage. The most promising fusion always reduces the estimation error significantly, also if the network coverage of the FCD needs to be estimated with LDD. It separates the urban network into two sub-network, one with loop detectors and one without. The information from the LDD and the FCD is then weighted according to the sub-networks and the square root of the network coverage of the FCD." @default.
- W2567651889 created "2017-01-06" @default.
- W2567651889 creator A5039842682 @default.
- W2567651889 date "2015-01-01" @default.
- W2567651889 modified "2023-09-27" @default.
- W2567651889 title "Fusion methods for an MFD estimation" @default.
- W2567651889 hasPublicationYear "2015" @default.
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