Araştırma Makalesi

Link Prediction and Maximum Flow in Transportation Network

Cilt: 9 Sayı: Issue: 2 25 Aralık 2024
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Link Prediction and Maximum Flow in Transportation Network

Öz

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.

Anahtar Kelimeler

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Veri Madenciliği ve Bilgi Keşfi, Veri Mühendisliği ve Veri Bilimi, Veri Yönetimi ve Veri Bilimi (Diğer)

Bölüm

Araştırma Makalesi

Erken Görünüm Tarihi

24 Aralık 2024

Yayımlanma Tarihi

25 Aralık 2024

Gönderilme Tarihi

29 Kasım 2024

Kabul Tarihi

21 Aralık 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 9 Sayı: Issue: 2

Kaynak Göster

APA
Çağlar, A., Öztemiz, F., & Yakut, S. (2024). Link Prediction and Maximum Flow in Transportation Network. Computer Science, 9(Issue: 2), 169-177. https://doi.org/10.53070/bbd.1593501
AMA
1.Çağlar A, Öztemiz F, Yakut S. Link Prediction and Maximum Flow in Transportation Network. JCS. 2024;9(Issue: 2):169-177. doi:10.53070/bbd.1593501
Chicago
Çağlar, Akin, Furkan Öztemiz, ve Selman Yakut. 2024. “Link Prediction and Maximum Flow in Transportation Network”. Computer Science 9 (Issue: 2): 169-77. https://doi.org/10.53070/bbd.1593501.
EndNote
Çağlar A, Öztemiz F, Yakut S (01 Aralık 2024) Link Prediction and Maximum Flow in Transportation Network. Computer Science 9 Issue: 2 169–177.
IEEE
[1]A. Çağlar, F. Öztemiz, ve S. Yakut, “Link Prediction and Maximum Flow in Transportation Network”, JCS, c. 9, sy Issue: 2, ss. 169–177, Ara. 2024, doi: 10.53070/bbd.1593501.
ISNAD
Çağlar, Akin - Öztemiz, Furkan - Yakut, Selman. “Link Prediction and Maximum Flow in Transportation Network”. Computer Science 9/Issue: 2 (01 Aralık 2024): 169-177. https://doi.org/10.53070/bbd.1593501.
JAMA
1.Çağlar A, Öztemiz F, Yakut S. Link Prediction and Maximum Flow in Transportation Network. JCS. 2024;9:169–177.
MLA
Çağlar, Akin, vd. “Link Prediction and Maximum Flow in Transportation Network”. Computer Science, c. 9, sy Issue: 2, Aralık 2024, ss. 169-77, doi:10.53070/bbd.1593501.
Vancouver
1.Akin Çağlar, Furkan Öztemiz, Selman Yakut. Link Prediction and Maximum Flow in Transportation Network. JCS. 01 Aralık 2024;9(Issue: 2):169-77. doi:10.53070/bbd.1593501

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