Caglar, AkinOztemiz, Furkan2026-04-042026-04-042024979-833153149-2https://doi.org/10.1109/IDAP64064.2024.10710664https://hdl.handle.net/11616/1080448th International Artificial Intelligence and Data Processing Symposium, IDAP 2024 -- 21 September 2024 through 22 September 2024 -- Malatya -- 203423This study aims to identify critical intersections and green wave corridors in an urban transportation network by analyzing vehicle flows. The dataset used consists of Bluetooth data collected from 56 intersections in the city of Malatya. The transportation network and dataset are uniquely prepared for this study. For the centrality analysis of intersections, PageRank, Closeness, and Betweenness centrality methods were applied. To identify green wave corridors, Walktrap and Optimal community detection methods were utilized. The results of both applications are presented visually and numerically in the study. The analysis and visualization phases were carried out using the R programming language with the igraph package. The findings will serve as a crucial decision support system for improving urban traffic planning and identifying congested intersections. © 2024 IEEE.trinfo:eu-repo/semantics/closedAccessCentrality MeasuresCommunity DetectionGraph TheoryTraffic Data AnalysisConference Object10.1109/IDAP64064.2024.107106642-s2.0-85207952689N/A