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TØIRoads: A Road Data Model Generation Tool

Authors Grunde Haraldsson Wesenberg , Ana Ozaki

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Author Details

Grunde Haraldsson Wesenberg
  • Department of Informatics, University of Bergen, Norway
  • Institute of Transport Economics, Oslo, Norway
Ana Ozaki
  • Department of Informatics, University of Oslo, Norway
  • Department of Informatics, University of Bergen, Norway

Funding

  • Wesenberg, Grunde Haraldsson: The author is supported by the Norwegian Research Council, project 322480.
  • Ozaki, Ana: The author is supported by the Norwegian Research Council, projects 322480, 316022. This work was partly supported by the Research Council of Norway through its Centre of Excellence Integreat - The Norwegian Centre for knowledge-driven machine learning, project number 332645.

Cite As Get BibTex

Grunde Haraldsson Wesenberg and Ana Ozaki. TØIRoads: A Road Data Model Generation Tool. In Special Issue on Resources for Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 2, Issue 2, pp. 6:1-6:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/TGDK.2.2.6

Abstract

We describe road data models which can represent high level features of a road network such as population, points of interest, and road length/cost and capacity, while abstracting from time and geographic location. Such abstraction allows for a simplified traffic usage and congestion analysis that focus on the high level features. We provide theoretical results regarding mass conservation and sufficient conditions for avoiding congestion within the model. We describe a road data model generation tool, which we call "TØI Roads". We also describe several parameters that can be specified by a TØI Roads user to create graph data that can serve as input for training graph neural networks (or another learning approach that receives graph data as input) for predicting congestion within the model. The road data model generation tool allows, for instance, the study of the effects of population growth and how changes in road capacity can mitigate traffic congestion.

Subject Classification

ACM Subject Classification
  • General and reference → Evaluation
Keywords
  • Road Data
  • Transportation
  • Graph Neural Networks
  • Synthetic Dataset Generation

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References

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    Software Heritage Logo archived version
    full metadata available at: https://doi.org/10.4230/artifacts.22621
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