Optimization of Forest Park Road Alignment using Genetic Algorithm

Forest park roads are integral to connecting functional zones, preserving ecological balance, and enhancing visitor experience. However, in mountainous areas, the complex terrain and diverse user requirements present challenges for achieving optimal road alignment. To address these challenges, this study introduces a parametric road alignment optimization method for forest parks, integrating generative AI with a visual programming model in Grasshopper. By using the Analytic Hierarchy Process (AHP) for determining multi-factor weights, and incorporating digital elevation data alongside user preference analysis, the method utilizes a genetic algorithm to refine road alignment. Applied to Qipanshan Forest Park as a case study, the results indicate that the optimized route reduces total road length by approximately <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$7.2\%$</tex> compared to the existing route, optimizing both the length and slope. This not only improves travel efficiency and safety but also enhances the park's connectivity and the overall visitor experience. The proposed method offers a scientifically grounded approach to road alignment design in small- and medium-scale forest parks, providing a practical reference for future designs.