Integrating AI for sustainable architectural space optimization and heritage-conscious street design

The paper presents an AI-powered framework designed to optimize the layout of historical and cultural street spaces while preserving heritage values. Combining deep learning, graph-based techniques, and reinforcement learning, this framework addresses common urban challenges such as pedestrian accessibility, traffic congestion, and environmental sustainability in historic districts. Unlike traditional urban planning methods, the framework offers a dynamic, data-driven approach that adapts to real-time changes in pedestrian flow, traffic, and environmental conditions. By integrating Convolutional Neural Networks (CNNs) for spatial feature extraction, Graph Neural Networks (GNNs) for network connectivity analysis, and Reinforcement Learning (RL) for dynamic layout adjustments, the system ensures efficient and sustainable urban designs. The proposed model was tested in cities with historical significance and demonstrated significant improvements: a 22.5% reduction in congestion, a 27.4% increase in green space, and a 31.8% improvement in pedestrian accessibility compared to traditional methods. The results validate the effectiveness of AI in urban planning, showing that it can balance the need for modernization with the preservation of historical authenticity.