Designing cycling infrastructure requires balancing the competing needs of diverse user groups, yet designers often struggle to anticipate how different cyclists experience the same street environment. We investigate how persona-based evaluation can support cycling infrastructure design by making experiential conflicts explicit during the design process. Informed by a formative study with 12 domain experts and crowdsourced bikeability assessments from 427 cyclists, we present StreetDesignAI, an interactive system that enables designers to (1) ground evaluation in real street context through imagery and map data, (2) receive parallel feedback from simulated cyclist personas spanning confident to cautious users, and (3) iteratively modify designs while the system surfaces conflicts across perspectives. A within-subjects study with 26 transportation professionals comparing StreetDesignAI against a general-purpose AI chatbot demonstrates that structured multi-perspective feedback significantly Broaden designers’ understanding of various cyclists’ perspectives, ability to identify diverse persona needs, and confidence in translating those needs into design decisions.
Key words: Cycling Infrastructure, Multi-Perspective Design, Persona-based Evaluation, Multi-agent System, Generative AI
Wang, Z., Dai, Y., Lyu, D., Nader, M., Chen, S., Ye, W., Ding, Z., Yan, X. (2026). StreetDesignAI: A Multi-Persona Evaluation System for Inclusive Infrastructure Design. In Proceedings of the 2026 ACM Designing Interactive Systems Conference (DIS ’26). ACM. Download Preprint.