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• W2894639457 startingPage "1490" @default.
• W2894639457 abstract "Aiming at objectively measuring the realism of virtual traffic flows and evaluating the effectiveness of different traffic simulation techniques, this paper introduces a general, dictionary-based learning method to evaluate the fidelity of any traffic trajectory data. First, a traffic pattern dictionary that characterizes common patterns of real-world traffic behavior is built offline from pre-collected ground truth traffic data. The corresponding learning error is set as the benchmark of the dictionary-based traffic representation. With the aid of the constructed dictionary, the realism of input simulated traffic flow data can be evaluated by comparing its dictionary-based reconstruction error with the dictionary error benchmark. This evaluation metric can be robustly applied to any simulated traffic flow data; in other words, it is independent of how the traffic data are generated. We demonstrated the effectiveness and robustness of this metric through many experiments on real-world traffic data and various simulated traffic data, comparisons with the state-of-the-art entropy-based similarity metric for aggregate crowd motions, and perceptual evaluation studies." @default.
• W2894639457 created "2018-10-12" @default.
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• W2894639457 date "2020-03-01" @default.
• W2894639457 modified "2023-10-17" @default.
• W2894639457 title "Dictionary-based Fidelity Measure for Virtual Traffic" @default.
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• W2894639457 doi "https://doi.org/10.1109/tvcg.2018.2873695" @default.
• W2894639457 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30295621" @default.
• W2894639457 hasPublicationYear "2020" @default.
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