ML Anthology

Leveraging Spatial Abstraction in Traffic Analysis and Forecasting with Visual Analytics

Natalia V. Andrienko, Gennady L. Andrienko, Salvatore Rinzivillo
ECML-PKDD 2016 pp. 32-35
doi:10.1007/978-3-319-46131-1_7 /ecmlpkdd/2016/andrienko2016ecmlpkdd-leveraging/

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.

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Cite

Text

Andrienko et al. "Leveraging Spatial Abstraction in Traffic Analysis and Forecasting with Visual Analytics." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016. doi:10.1007/978-3-319-46131-1_7

Markdown

[Andrienko et al. "Leveraging Spatial Abstraction in Traffic Analysis and Forecasting with Visual Analytics." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016.](https://mlanthology.org/ecmlpkdd/2016/andrienko2016ecmlpkdd-leveraging/) doi:10.1007/978-3-319-46131-1_7

BibTeX

@inproceedings{andrienko2016ecmlpkdd-leveraging,
  title     = {{Leveraging Spatial Abstraction in Traffic Analysis and Forecasting with Visual Analytics}},
  author    = {Andrienko, Natalia V. and Andrienko, Gennady L. and Rinzivillo, Salvatore},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2016},
  pages     = {32-35},
  doi       = {10.1007/978-3-319-46131-1_7},
  url       = {https://mlanthology.org/ecmlpkdd/2016/andrienko2016ecmlpkdd-leveraging/}
}