Exploring hybrid models for identifying locations for active mobility pathways using Real-Time Spatial Delphi and GANs

Abstract

Abstract The spatial planning process is considered an extremely complex system, as it is made up of different variables that interrelate and interact with each other. Effectively addressing this spatial complexity necessitates a multidisciplinary approach, as unified methodologies may prove insufficient. Specifically, in urban planning, it is increasingly crucial to prioritize bike lanes, bike stations, and pedestrian zones, for functional transportation infrastructures. This approach can enhance cities by improving air quality, reducing emissions, and boosting public health and safety through physical activity and accident prevention. However, implementing these changes requires careful planning, community engagement, and collaboration with stakeholders. This paper proposes a hybrid model for identifying optimal locations for bike lanes, bike stations, and pedestrian zones adopting Real-Time Spatial Delphi and Generative Adversarial Networks (GANs). The Real-Time Spatial Delphi is a modified version of the traditional Delphi method that incorporates real-time feedback and visualization of group response in real-time, aiming to achieve a convergence of opinions among experts on the territory. Nevertheless, these judgments are a spatial representation not visible in reality, and with the spread of AI models, different implementations can support the planning process, such as the use of Generative Adversarial Networks (GANs). In this case, the GANs models can be exploited by adopting pre-existing location images resulting from experts’ judgments to illustrate the proposed intervention’s visual impact. This approach can help stakeholders, policymakers and citizens visualize the proposed changes and assess their potential impact more accurately. To demonstrate the effectiveness of our hybrid model, we apply it to the city of Dublin.