Ulaşım Ağında Bağlantı Tahmini ve Maksimum Akış

Year 2024, Volume: 9 Issue: Issue: 2, 169 - 177

Akin Çağlar , Furkan Öztemiz , Selman Yakut

https://doi.org/10.53070/bbd.1593501

Abstract

Bu çalışmada gerçek bir ulaşım ağı verisi üzerinde alternatif yol çıkarımları için kullanılan link prediction ve trafiği akışı hakkında önemli bilgiler sunan maximum flow analizi gerçekleştirilmiştir. Analiz için kullanılan veri seti bu analiz için özgün olarak üretilmiştir. Veri seti için Malatya ili kent merkezinde 54 kavşak noktasında kurulu bluetooth araç sayım cihazları kullanılmıştır. Analiz için yaklaşım 50 milyon araç geçisi verisi ile ulaşım ağı çizgesinin ağırlıklandırılmasında kullanılmıştır. Maximum flow analizi için Ford-Fulkerson yöntemi, link prediction analizi için jaccard similarity metriği kullanılmıştır. Çizgenin oluşturulması ve tüm analiz süreçlerinde R dili ve igraph çizge kütüphanesi kullanılmıştır. Elde edilen analiz sonuçları neticesinde ulaşım ağında alternatif yol güzergahları ve yolların taşıyabileceği maksimum trafik hakkında önemli bilgilere ulaşılmıştır. Bu sayede kritik noktaların ve potansiyel tıkanıklıkların belirlenmesinede olanak tanınmıştır. Elde edilen sonuçların, ulaşım ağının verimliliğini artırmak ve trafik yönetim stratejilerini geliştirmek için önemli bir katkı sunması beklenmektedir.

Keywords

Ulaşım ağları, Bağlantı tahmini, Maksimum akış

Link Prediction and Maximum Flow in Transportation Network

Abstract

This study conducted link prediction analysis and maximum flow analysis, which provide critical insights into alternative route inferences and traffic flow, based on real transportation network data. The dataset used in the analysis was specifically generated for this purpose. Data collection involved Bluetooth vehicle counting devices installed at 54 intersection points in the city center of Malatya, Turkey. The methodology leveraged approximately 50 million vehicle transition records to weight the transportation network graph. The Ford-Fulkerson method was utilized for the maximum flow analysis, while the Jaccard similarity metric was employed for the link prediction analysis. The graph construction and all analysis processes were carried out using the R programming language and the igraph graph library. The results of the analyses provided significant insights into alternative route corridors within the transportation network and the maximum traffic capacity of the roads. Consequently, the findings enabled the identification of critical points and potential congestion areas. The outcomes are expected to make a substantial contribution to enhancing the efficiency of the transportation network and improving traffic management strategies.

Keywords

Transportation Networks, Maximum flow, Link predection.

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