都市道路交通リスクと持続可能性評価のためのハイブリッドファジィおよび畳み込みニューラルネットワークフレームワーク

Urban redevelopment is essential to improving sustainability and livability, but traffic congestion is still a problem, not only during rush hour but also during construction, accidents, and other interruptions. Existing research frequently concentrates only on optimizing traffic flow, with little consideration given to stakeholder-driven viewpoints or environmental and anthropogenic risk concerns. Although Geographic Information Systems (GIS) have great promise for gathering and evaluating vast amounts of spatial data, little is known about how they might be used in comprehensive frameworks for traffic risk assessment. To fill these shortcomings, this study suggests a brand-new hybrid framework for evaluating traffic sustainability that combines GIS with a Convolutional Neural Network (CNN) model and a Multi-Criteria Decision-Making (MCDM) technique. By (i) using a fuzzy Delphi method to systematically weight various environmental and anthropogenic criteria, (ii) utilizing oversampling methods to address data imbalances in risk prediction, and (iii) embedding CNN-based modeling in an interactive GIS platform to produce fine-grained, stakeholder-relevant risk maps, the framework significantly improves on previous research. The usefulness of the framework is demonstrated by a case study conducted in Foshan City, Guangdong Province, where road collapse segments were divided into five risk levels. Of these, 7% were categorized as high risk and 4% as extremely high risk, with the majority of these segments being in the eastern and southeastern regions. The training and testing accuracies of the Fuzzy-CNN model were 0.989 and 0.982, respectively, indicating high predictive performance. For municipal governments and urban planners, the generated traffic risk map offers a scalable and data-driven decision-support tool. This research tackles major shortcomings of current models in balancing sustainability, risk management, and urban resilience and advances the state of the art in GIS-based traffic risk analysis by explicitly integrating biodiversity and environmental elements into traffic planning.