skip to main content
10.1145/3557915.3560989acmconferencesArticle/Chapter ViewAbstractPublication PagesgisConference Proceedingsconference-collections
demonstration

RODGEN: an interactive interface for road network generation

Published:22 November 2022Publication History

ABSTRACT

We present RODGEN, an interactive, graphical user interface for generating road networks that adopts the growth-based model. The first step in the generation process is to construct the backbone of the network by either choosing between a grid-based and a ring-based predefined topology or allowing the users to define a custom one. The backbone divides the space into a number of areas, called neighborhoods. The user can populate neighborhoods either by importing existing road networks or adding roads by hand. Besides generating road networks, our interface also provides a platform for analysis. For this purpose, we employ a general-purpose graph analytics library, which allows the users to compute graph statistics, perform connectivity analysis and execute basic routing tasks.

References

  1. Albert-Laszlo Barabasi and Reka Albert. 1999. Emergence of Scaling in Random Networks. Science 286, 5439 (1999), 509--512.Google ScholarGoogle Scholar
  2. Jan Benes, Alexander Wilkie, and Jaroslav Krivánek. 2014. Procedural Modelling of Urban Road Networks. Comput. Graph. Forum 33, 6 (2014), 132--142.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Guoning Chen, Gregory Esch, Peter Wonka, Pascal Müller, and Eugene Zhang. 2008. Interactive procedural street modeling. ACM Trans. Graph. 27, 3 (2008).Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Paul Erdos and Alfred Renyi. 1960. On the evolution of random graphs. Publ. Math. Inst. Hungary. Acad. Sci. 5 (1960), 17--61.Google ScholarGoogle Scholar
  5. Robert Geisberger, Peter Sanders, Dominik Schultes, and Daniel Delling. 2008. Contraction Hierarchies: Faster and Simpler Hierarchical Routing in Road Networks. In WEA. 319--333.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Stefan Hartmann, Michael Weinmann, Raoul Wessel, and Reinhard Klein. 2017. StreetGAN: Towards Road Network Synthesis with Generative Adversarial Networks. In WSCG. 133--142.Google ScholarGoogle Scholar
  7. George Kelly and Hugh McCabe. 2007. Citygen: An Interactive System for Procedural City Generation. In GDTW. 8--16.Google ScholarGoogle Scholar
  8. Lin Ziwen Kelvin and Anand Bhojan. 2020. Procedural Generation of Roads with Conditional Generative Adversarial Networks. In ACM SIGGRAPH. 12:1--12:2.Google ScholarGoogle Scholar
  9. Jon M. Kleinberg, Ravi Kumar, Prabhakar Raghavan, Sridhar Rajagopalan, and Andrew Tomkins. 1999. The Web as a Graph: Measurements, Models, and Methods. In COCOON. 1--17.Google ScholarGoogle Scholar
  10. Jure Leskovec, Deepayan Chakrabarti, Jon M. Kleinberg, and Christos Faloutsos. 2005. Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication. In PKDD. 133--145.Google ScholarGoogle Scholar
  11. Takashi Owaki and Takashi Machida. 2020. RoadNetGAN: Generating Road Networks in Planar Graph Representation. In ICONIP. 535--543.Google ScholarGoogle Scholar
  12. Yoav I. H. Parish and Pascal Müller. 2001. Procedural modeling of cities. In ACM SIGGRAPH. 301--308.Google ScholarGoogle Scholar
  13. Przemyslaw Prusinkiewicz and Aristid Lindenmayer. 1990. The algorithmic beauty of plants. Springer.Google ScholarGoogle Scholar
  14. Lars Ruthotto and Eldad Haber. 2021. An Introduction to Deep Generative Modeling. CoRR abs/2103.05180 (2021). https://arxiv.org/abs/2103.05180Google ScholarGoogle Scholar
  15. Dimitris Sacharidis and Panagiotis Bouros. 2013. Routing directions: keeping it fast and simple. In ACM SIGSPATIAL. 164--173.Google ScholarGoogle Scholar
  16. Kaveh Shahabi and John P. Wilson. 2014. CASPER: Intelligent capacity-aware evacuation routing. Comput. Environ. Urban Syst. 46 (2014), 12--24.Google ScholarGoogle ScholarCross RefCross Ref
  17. Philippe Y. R. Sohouenou, Panayotis Christidis, Aris Christodoulou, Luis A. C. Neves, and Davide Lo Presti. 2020. Using a random road graph model to understand road networks robustness to link failures. Int. J. Crit. Infrastructure Prot. 29 (2020), 100353.Google ScholarGoogle ScholarCross RefCross Ref
  18. Duncan J. Watts and Steven H. Strogatz. 1998. Collective dynamics of 'small-world' networks. Nature 393, 6684 (1998), 440--442.Google ScholarGoogle Scholar
  19. Huichu Zhang, Siyuan Feng, Chang Liu, Yaoyao Ding, Yichen Zhu, Zihan Zhou, Weinan Zhang, Yong Yu, Haiming Jin, and Zhenhui Li. 2019. CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario. In ACM WWW. 3620--3624.Google ScholarGoogle Scholar

Index Terms

  1. RODGEN: an interactive interface for road network generation

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Conferences
          SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems
          November 2022
          806 pages
          ISBN:9781450395298
          DOI:10.1145/3557915

          Copyright © 2022 Owner/Author

          Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 22 November 2022

          Check for updates

          Qualifiers

          • demonstration

          Acceptance Rates

          Overall Acceptance Rate220of1,116submissions,20%
        • Article Metrics

          • Downloads (Last 12 months)56
          • Downloads (Last 6 weeks)4

          Other Metrics

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader