USE OF AN AI-BASED DIGITAL PREDICTION MODEL FOR THE EVALUATION OF URBAN INFRASTRUCTURE IN TERMS OF ACCESSIBILITY AND EFFICIENT URBAN MOVEMENT FOR PEOPLE WITH DISABILITIES.

Purpose: Ensuring accessible urban infrastructure remains a challenge to inclusive societies and equal participation of people with disabilities in economic, cultural & social life and is thus a stunting factor in economic development. This paper proposes using an Artificial Intelligence-based model for evaluating accessibility in urban infrastructure towards identifying & predicting problematic areas in the existing or future built environment. The objective is to describe a reliable and extensible model capable of detecting mobility-problematic areas, evaluating the quality of urban infrastructure, proposing alternative routes and creating the base of a holistic detection and evaluation digital tool for better urban planning and efficient application of European Social Policies. Methodology: The research identifies obstacle and difficulty components useful within a Digital AI system via structured interviews performed with members of 2 key organizations in social development and inclusion in Eastern Macedonia and Thrace, Greece. Findings: The set of obstacles and difficulties is aggregated in a vector of solvable difficulties suitable for an AI system. Additionally, we propose methodologies for collecting and comparing data from predefined pilot routes between people with disabilities and the general population to build an initial training dataset for a continuous decision-making and evaluation AI system. Originality: Research originality is derived from combining Artificial Intelligence with the sector of computational evaluation of material infrastructure, as perceived by humans with disabilities, and as a tool of increased economic activity. It additionally defines key obstacles perceived by PwDs that are sufficiently measurable and subsequently solvable by AI. [ABSTRACT FROM AUTHOR]