Fuzzy Spatiotemporal Centrality for Urban Resilience

Traffic congestion is a growing concern in the world's most populated metropolitan areas. Over the past decades, several approaches have been developed to better understand and control urban traffic management, such as artificial intelligence, video streaming, fuzzy logic, and complex networks. In this study, we apply a combination of fuzzy logic and complex network analysis to better understand the dynamic structure of urban traffic networks. When studying the dynamic processes of transportation networks, the exploration of the critical entities plays a crucial role in exploring and analyzing the resilience and management of urban traffic systems. Therefore, we propose measures of flow and travel time variability as well as dynamic road saturation factors to develop fuzzy measures of dynamic centrality. Also, the fuzzy temporal spectral centrality is also considered for this purpose. Our proposal is a fundamental building component for intelligent traffic monitoring and provides real support for the resilience of urban networks. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.