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Erschienen in:
2016 | OriginalPaper | Buchkapitel
Leveraging Spatial Abstraction in Traffic Analysis and Forecasting with Visual Analytics
verfasst von : Natalia Andrienko, Gennady Andrienko, Salvatore Rinzivillo
Erschienen in: Machine Learning and Knowledge Discovery in Databases
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Abstract
By applying spatio-temporal aggregation to traffic data consisting of vehicle trajectories, we generate a spatially abstracted transportation network, which is a directed graph where nodes stand for territory compartments (areas in geographic space) and links (edges) are abstractions of the possible paths between neighboring areas. From time series of traffic characteristics obtained for the links, we reconstruct mathematical models of the interdependencies between the traffic intensity (a.k.a. traffic flow or flux) and mean velocity. Graphical representations of these interdependencies have the same shape as the fundamental diagram of traffic flow through a physical street segment, which is known in transportation science. This key finding substantiates our approach to traffic analysis, forecasting, and simulation leveraging spatial abstraction. We present the process of data-driven generation of traffic forecasting and simulation models, in which each step is supported by visual analytics techniques.