Generating Random Hyperfractal Cities

2025-12-01

AI Summary

Traditional fractal models struggle to capture complex urban structures and enable multi-regional collaborative modeling. To address this, we propose a tile-based hyperfractal urban generation framework. Our method embeds hyperfractal construction into modular, tessellatable geometric primitives (tiles), enabling seamless integration of multiple hyperfractal blocks via randomized parameters and inter-regional topological constraints. By synergistically combining fractal geometry, stochastic graph generation, and structured layout algorithms, the framework automatically constructs urban street networks while preserving global fractal characteristics. Key contributions include: (i) the first modular extension and regional fusion of hyperfractal models, supporting interactive generation of large-scale, highly heterogeneous street networks; and (ii) an intrinsic fractal-driven urban zoning mechanism that enhances structural coherence, interpretability, and scalability of generated outputs.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Generation
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Web data generation and simulation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

This paper focuses on the challenge of interactively modeling street networks. In this work, we extend the simple fractal model, which is particularly useful for describing small cities or individual districts, by constructing random cities based on a tiling structure over which hyperfractals are distributed. This approach enables the connection of multiple hyperfractal districts, providing a more comprehensive urban representation. Furthermore, we demonstrate how this decomposition can be used to segment a city into distinct districts through fractal analysis. Finally, we present tools for the numerical generation of random cities following this model.

Problem

• Extends fractal models to generate random hyperfractal city networks
• Connects multiple hyperfractal districts for comprehensive urban representation
• Provides tools for numerical generation of random cities interactively

Innovation

• Extends fractal model with hyperfractal tiling structure
• Connects multiple hyperfractal districts for urban representation
• Provides tools for numerical generation of random cities