StreetDesignAI: A Multi-Persona Evaluation System for Inclusive Infrastructure Design
- URL: http://arxiv.org/abs/2601.15671v1
- Date: Thu, 22 Jan 2026 05:53:05 GMT
- Title: StreetDesignAI: A Multi-Persona Evaluation System for Inclusive Infrastructure Design
- Authors: Ziyi Wang, Yilong Dai, Duanya Lyu, Mateo Nader, Sihan Chen, Wanghao Ye, Zjian Ding, Xiang Yan,
- Abstract summary: We present StreetDesignAI, an interactive system that enables designers to ground evaluation in street context through imagery and map data.<n>A study with 26 transportation professionals demonstrates that structured multi-perspective feedback significantly improves designers' understanding of diverse user perspectives.
- Score: 8.314136104243735
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Designing inclusive cycling infrastructure requires balancing competing needs of diverse user groups, yet designers often struggle to anticipate how different cyclists experience the same street. We investigate how persona-based multi-agent evaluation can support inclusive design by making experiential conflicts explicit. We present StreetDesignAI, an interactive system that enables designers to (1) ground evaluation in street context through imagery and map data, (2) receive parallel feedback from cyclist personas spanning confident to cautious users, and (3) iteratively modify designs while surfacing conflicts across perspectives. A within-subjects study with 26 transportation professionals demonstrates that structured multi-perspective feedback significantly improves designers' understanding of diverse user perspectives, ability to identify persona needs, and confidence in translating them into design decisions, with higher satisfaction and stronger intention for professional adoption. Qualitative findings reveal how conflict surfacing transforms design exploration from single-perspective optimization toward deliberate trade-off reasoning. We discuss implications for AI tools that scaffold inclusive design through disagreement as an interaction primitive.
Related papers
- Persona-aware and Explainable Bikeability Assessment: A Vision-Language Model Approach [8.652496663871172]
This paper proposes a persona-aware Vision-Language Model framework for bikeability assessment.<n>We developed a panoramic image-based crowdsourcing system and collected 12,400 persona-conditioned assessments from 427 cyclists.<n>Experiment results show that the proposed framework offers competitive bikeability rating prediction.
arXiv Detail & Related papers (2026-01-07T02:46:51Z) - From Image Generation to Infrastructure Design: a Multi-agent Pipeline for Street Design Generation [9.255248190497515]
We introduce a multi-agent system that edits and redesigns bicycle facilities directly on real-world street-view imagery.<n>The framework integrates lane localization, prompt optimization, design generation, and automated evaluation to synthesize realistic, contextually appropriate designs.
arXiv Detail & Related papers (2025-09-05T19:49:36Z) - FeedQUAC: Quick Unobtrusive AI-Generated Commentary [8.057486493973304]
We introduce FeedQUAC, a design companion that delivers real-time AI-generated commentary from a variety of perspectives.<n>We discuss the role of AI feedback, its strengths and limitations, and how to integrate it into existing design.<n>Our findings suggest that ambient interaction is a valuable consideration for both the design and evaluation of future creativity support systems.
arXiv Detail & Related papers (2025-04-23T04:48:00Z) - Using customized GPT to develop prompting proficiency in architectural AI-generated images [0.40964539027092906]
This research investigates the use of customized GPT models to enhance prompting proficiency among architecture students when generating AI-driven images.<n>ANOVA results indicated statistically significant improvements in word count, similarity, and concreteness, especially in the group supported by AI personas and structured prompting guides.
arXiv Detail & Related papers (2025-04-16T07:03:18Z) - Empowering Clients: Transformation of Design Processes Due to Generative AI [1.4003044924094596]
The study reveals that AI can disrupt the ideation phase by enabling clients to engage in the design process through rapid visualization of their own ideas.
Our study shows that while AI can provide valuable feedback on designs, it might fail to generate such designs, allowing for interesting connections to foundations in computer science.
Our study also reveals that there is uncertainty among architects about the interpretative sovereignty of architecture and loss of meaning and identity when AI increasingly takes over authorship in the design process.
arXiv Detail & Related papers (2024-11-22T16:48:15Z) - Freeview Sketching: View-Aware Fine-Grained Sketch-Based Image Retrieval [85.73149096516543]
We address the choice of viewpoint during sketch creation in Fine-Grained Sketch-Based Image Retrieval (FG-SBIR)
A pilot study highlights the system's struggle when query-sketches differ in viewpoint from target instances.
To reconcile this, we advocate for a view-aware system, seamlessly accommodating both view-agnostic and view-specific tasks.
arXiv Detail & Related papers (2024-07-01T21:20:44Z) - I-Design: Personalized LLM Interior Designer [57.00412237555167]
I-Design is a personalized interior designer that allows users to generate and visualize their design goals through natural language communication.
I-Design starts with a team of large language model agents that engage in dialogues and logical reasoning with one another.
The final design is then constructed in 3D by retrieving and integrating assets from an existing object database.
arXiv Detail & Related papers (2024-04-03T16:17:53Z) - Re-mine, Learn and Reason: Exploring the Cross-modal Semantic
Correlations for Language-guided HOI detection [57.13665112065285]
Human-Object Interaction (HOI) detection is a challenging computer vision task.
We present a framework that enhances HOI detection by incorporating structured text knowledge.
arXiv Detail & Related papers (2023-07-25T14:20:52Z) - Design Space Exploration and Explanation via Conditional Variational
Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges [52.77024349608834]
This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE)
The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland.
arXiv Detail & Related papers (2022-11-29T17:28:31Z) - Investigating Positive and Negative Qualities of Human-in-the-Loop
Optimization for Designing Interaction Techniques [55.492211642128446]
Designers reportedly struggle with design optimization tasks where they are asked to find a combination of design parameters that maximizes a given set of objectives.
Model-based computational design algorithms assist designers by generating design examples during design.
Black box methods for assistance, on the other hand, can work with any design problem.
arXiv Detail & Related papers (2022-04-15T20:40:43Z) - Congestion-aware Multi-agent Trajectory Prediction for Collision
Avoidance [110.63037190641414]
We propose to learn congestion patterns explicitly and devise a novel "Sense--Learn--Reason--Predict" framework.
By decomposing the learning phases into two stages, a "student" can learn contextual cues from a "teacher" while generating collision-free trajectories.
In experiments, we demonstrate that the proposed model is able to generate collision-free trajectory predictions in a synthetic dataset.
arXiv Detail & Related papers (2021-03-26T02:42:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.