Exploring hybrid models for identifying locations for active mobility pathways using real-time spatial Delphi and GANs

• Published in: European Transport Research Review Vol. 16; no. 1; pp. 61 - 12
• Main Authors: Calleo, Yuri, Giuffrida, Nadia, Pilla, Francesco
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2024; Springer; Springer Nature B.V; SpringerOpen
• Subjects: Accident prevention; Air quality; Air quality management; Artificial intelligence; Automotive Engineering; Bicycles; Cities; Civil Engineering; Community involvement; Complex systems; Complex variables; Complexity; Convergence; Delphi method; Emissions control; Engineering; Forecasts and trends; Generative adversarial networks; Geospatial data; Original Paper; Outdoor air quality; Pedestrian areas; Pedestrians; Public health; Real time; Real variables; Real-time spatial Delphi; Regional/Spatial Science; Road design; Safety regulations; Spatial planning; TRA 2024 Dublin - Transport Transitions (Highlights of the 2024 Transport Research Arena conference); Transportation; Transportation authorities; Urban planning; Urban transportation; Generative adversarial networks; Spatial planning; Real-time spatial Delphi; Artificial intelligence
• ISSN: 1866-8887; 1867-0717; 1866-8887
• DOI: 10.1186/s12544-024-00685-7

The spatial planning process is considered an extremely complex system, as it comprises 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 stakeholder collaboration. 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 artificial intelligence models, different implementations can support the planning process, such as the use of GANs. In this case, GANs can be exploited by adopting pre-existing location images resulting from experts’ judgments to illustrate the proposed intervention’s visual impact. To demonstrate the effectiveness of our hybrid model, we apply it to the city of Dublin. The results showcased how the method helps stakeholders, policymakers, and citizens in visualizing the proposed changes and gauging their potential impact with greater precision.