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.
- Albert-Laszlo Barabasi and Reka Albert. 1999. Emergence of Scaling in Random Networks. Science 286, 5439 (1999), 509--512.Google Scholar
- Jan Benes, Alexander Wilkie, and Jaroslav Krivánek. 2014. Procedural Modelling of Urban Road Networks. Comput. Graph. Forum 33, 6 (2014), 132--142.Google ScholarDigital Library
- Guoning Chen, Gregory Esch, Peter Wonka, Pascal Müller, and Eugene Zhang. 2008. Interactive procedural street modeling. ACM Trans. Graph. 27, 3 (2008).Google ScholarDigital Library
- Paul Erdos and Alfred Renyi. 1960. On the evolution of random graphs. Publ. Math. Inst. Hungary. Acad. Sci. 5 (1960), 17--61.Google Scholar
- 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 ScholarDigital Library
- Stefan Hartmann, Michael Weinmann, Raoul Wessel, and Reinhard Klein. 2017. StreetGAN: Towards Road Network Synthesis with Generative Adversarial Networks. In WSCG. 133--142.Google Scholar
- George Kelly and Hugh McCabe. 2007. Citygen: An Interactive System for Procedural City Generation. In GDTW. 8--16.Google Scholar
- Lin Ziwen Kelvin and Anand Bhojan. 2020. Procedural Generation of Roads with Conditional Generative Adversarial Networks. In ACM SIGGRAPH. 12:1--12:2.Google Scholar
- 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 Scholar
- 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 Scholar
- Takashi Owaki and Takashi Machida. 2020. RoadNetGAN: Generating Road Networks in Planar Graph Representation. In ICONIP. 535--543.Google Scholar
- Yoav I. H. Parish and Pascal Müller. 2001. Procedural modeling of cities. In ACM SIGGRAPH. 301--308.Google Scholar
- Przemyslaw Prusinkiewicz and Aristid Lindenmayer. 1990. The algorithmic beauty of plants. Springer.Google Scholar
- Lars Ruthotto and Eldad Haber. 2021. An Introduction to Deep Generative Modeling. CoRR abs/2103.05180 (2021). https://arxiv.org/abs/2103.05180Google Scholar
- Dimitris Sacharidis and Panagiotis Bouros. 2013. Routing directions: keeping it fast and simple. In ACM SIGSPATIAL. 164--173.Google Scholar
- Kaveh Shahabi and John P. Wilson. 2014. CASPER: Intelligent capacity-aware evacuation routing. Comput. Environ. Urban Syst. 46 (2014), 12--24.Google ScholarCross Ref
- 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 ScholarCross Ref
- Duncan J. Watts and Steven H. Strogatz. 1998. Collective dynamics of 'small-world' networks. Nature 393, 6684 (1998), 440--442.Google Scholar
- 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 Scholar
Index Terms
- RODGEN: an interactive interface for road network generation
Recommendations
Towards virtualization of user interfaces based on UsiXML
Web3D '05: Proceedings of the tenth international conference on 3D Web technologyA model-based approach is presented for structuring a development process of virtual user interfaces based on UsiXML, a XML-compliant User Interface Description Language. UsiXML provides a Concrete User Interface description that remains independent ...
Transportable Applications Environment (TAE) Plus user interface designer WorkBench
CHI '92: Proceedings of the SIGCHI Conference on Human Factors in Computing SystemsTAE Plus was built at NASA's Goddard Space Flight Center to support the building of GUI user interfaces for highly interactive applications, such as realtime processing systems and scientific analysis system. TAE Plus is designed as a productivity tool ...
Design issues related to pie menus for 5-way joysticks
Mobility '07: Proceedings of the 4th international conference on mobile technology, applications, and systems and the 1st international symposium on Computer human interaction in mobile technologyAlthough linear textual menus are a widely adopted solution in the mobile phone user interfaces, alternatives that would take smaller amount of screen real estate exist, e.g. toolbars and pie menus. Pie menus also provide access to functions with fewer ...
Comments