Millions of people's daily commutes are impacted by serious problems with urban transportation networks, such as congestion, delays, and accidents. Rapid urbanization, poor infrastructure, and insufficient traffic management systems are frequently blamed for these problems. An intelligent Traffic system is essential to solve these issues and improve the effectiveness, and sustainability of transportation systems. This investigation aims to perform an in-depth network analysis using the case of Bengaluru, an urban city of India, utilizing graph analytics techniques. Centrality measures are used to identify critical nodes and road segments that critically contribute to traffic congestion. Further, Community detection algorithms uncover clusters of areas with similar traffic patterns and congestion hotspots. Lastly, link prediction methods forecast future traffic flows and indicate potential bottlenecks. The research findings provide data-driven insights and recommendations for improving the efficiency and sustainability of transportation in Bengaluru. The findings of this study can go beyond Bengaluru, serving as a valuable resource for those making decisions, transportation organizations, and urban planners in quickly expanding urban areas with comparable transportation difficulties.
Intelligent Traffic Systems: A Graph Analytics Approach for Sustainable Transportation Networks Using Bengaluru Traffic
01.09.2023
841233 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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