A framework for human-computer interactive street network design based on a multi-stage deep learning approach. (September 2022) - Fang, Zhou
Abstract: Limited attention has been given to human-computer interactions in the plan-making process to capitalize on the relative strengths of both. This paper proposes a methodological framework for an interactive street network design that complements user-driven (i.e., procedural-based tools) and example-driven (i.e., learning-based tools) approaches in urban planning and design. The proposed framework consists of three components: (1) a data preparation module to link open-source road networks with human-labeled planning guidance, (2) a multi-stage deep learning (MSDL) model to reinforce the user-defined guidance in the automatic generation of street networks, and (3) a human-computer interaction (HCI) interface to enable the progressive design process. The performance and the working mechanism of the proposed framework were examined through experiments in four European cities (i.e., Amsterdam, Barcelona, Berlin, and Prague). The experiments demonstrate that the proposed MSDL model can achieve a better predictive performance compared to benchmark models, particularly when limited planning guidance is given. These finding are revealed using either computer vision- or street network-related metrics. With less than 40% of the ground truth planning guidance used as an input, the MSDL model can perform as well as other models using 100% of the information. Furthermore, when embedded within an HCI system for user trials, the model can facilitate a human-computer collaborative design process. This advantage is derived from the model's ability to provide initial prototypes, timely responses to changed guidance, and quantifiable evaluations of the generated proposals. Suitable for professionals and laypersons, the proposed tool can inform plan-making and public engagement by offering realistic, enriched, and human-centered spatial proposal alternatives for comparison. Highlights: Complement the procedural- and learning-based computer-aided frameworks for street network design. Improve conventional, end-to-end deep-learning structure to a multi-stage one. Develop an HCI system for interactive and progressive street network design. Provide more realistic planning options and enrich alternatives in the early stages of urban planning and design.