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• W4313127911 endingPage "131075" @default.
• W4313127911 startingPage "131061" @default.
• W4313127911 abstract "Drastic changes into city road traffic may impact in large portions of the city, then hypothetical scenarios have to be analyzed to identify the best solutions to maintain high quality of city services. In this paper, a solution for unexpected or planned events is proposed and validated with the major focus on traffic flow fields. In order to mitigate the effects on wide area, assessments are needed to evaluate the city changes impact on traffic flow in short time. The proposed solution takes into account static, historical, real-time/dynamic, and forecasting information, with long terms and range of Traffic Flow Reconstructions (multiple simulations, predictions and data transformations) integrated with a specific assessment model to provide support for decision makers. Such a solution dynamically reshapes the road network with many connected critical areas and it automatically computes multiple traffic reconstructions in consecutive time slots, while considering the evolution of traffic flow data according to the related traffic re-distribution at junctions, solving their indeterminacy. Each scenario can be grounded for different road graph solutions, and each solution is evaluated by means of specific indicators taking into account traffic flow criticisms, and topological road graph features. The solution herein presented has been developed into the Snap4City framework for Sii-Mobility national mobility and transport action of the Italian Ministry of Innovation and Research." @default.
• W4313127911 created "2023-01-06" @default.
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• W4313127911 date "2022-01-01" @default.
• W4313127911 modified "2023-10-14" @default.
• W4313127911 title "Vehicular Traffic Flow Reconstruction Analysis to Mitigate Scenarios With Large City Changes" @default.
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• W4313127911 doi "https://doi.org/10.1109/access.2022.3229183" @default.
• W4313127911 hasPublicationYear "2022" @default.
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