Centrality Analysis of Malatya City Transportation Network Intersection Points

Year 2022, Volume: 37 Issue: 1, 511 - 528, 10.11.2021

Furkan Öztemiz Ali Karci

https://doi.org/10.17341/gazimmfd.834255

Cited By: 2

Abstract

The most key areas of transportation networks are the intersections that form the intersection points of the roads. The location of the intersections and the roads that they connect to are one of the most important factors affecting the traffic flow. In this study, the importance of the intersection points on the streets and boulevards in the downtown of Malatya province in the transportation system will be determined. Population information has been calculated according to the city's land use plan. Intersection serving more than 90 percent of the population have been selected. Graph modeling made for Malatya downtown consists of 151 intersection points and 258 roads connecting these intersection points. The actual distance lengths of the roads are included in the graph modeling in meters. Pagerank, Eigenvector centrality, Closeness Centrality, Betweenness Centrality algorithms were applied on the weighted graph created. The properties and results of each algorithm are examined in detail. A homogeneous combining approach that contains all the unique features of these 4 algorithms has been applied and successful results have been obtained. The centrality values of the intersection points and the most effective intersection point are shown on the graph according to the results of all algorithms. R programming language was used for all analysis and visual operations.

Keywords

Graph Theory, Centrality Algorithm, Intersection Dominance, Transportation Intersection Network

Malatya İli ulaşım ağı kavşak noktalarının merkezlilik analizi

Abstract

Ulaşım ağlarının en kilit bölgeleri yolların kesişim noktalarını oluşturan kavşaklardır. Kavşakların konumu ve bağlandığı yollar trafik akışını etkileyen en önemli etmenlerden birisidir. Bu çalışmada Malatya ili kent merkezi içerisinde bulunan caddeler ve bulvarlar üzerinde bulunan kavşak noktalarının ulaşım sistemi içerisindeki önemi belirlenecektir. Kentin imar planına göre nüfus bilgileri hesaplanmıştır. Nüfusun yüzde 90’ından fazlasına hizmet veren kavşak noktaları seçilmiştir. Malatya kent merkezi için yapılan graf modellemesi 151 kavşak noktası ve bu kavşak noktalarını birleştiren 258 yoldan oluşmaktadır. Graf modellemesine yolların gerçek mesafe uzunlukları metre cinsinden dâhil edilmiştir. Oluşturulan ağırlıklı graf üzerinde Pagerank, Eigenvector centrality, Closeness Centrality, Betweenness Centrality algoritmaları uygulanmıştır. Her algoritmanın kendisine ait özellikleri ve sonuçları ayrıntılı olarak incelenmiştir. Bu 4 algoritmanın kendine özgü özelliklerinin tamamını barındıran homojen birleştirici bir yaklaşım uygulanmış ve başarılı sonuçlar alınmıştır. Bütün algoritmaların sonuçlarına göre kavşak noktalarının merkezlilik değerleri ve en etkili kavşak noktası graf üzerinde gösterilmiştir. Bütün analiz ve görsel işlemleri için R programlama dili kullanılmıştır.

Keywords

Çizge Teorisi, Merkezlilik Algoritmaları, Kavşak Baskınlık, Ulaşım Kavşak Ağı

References

• [1] Wijnands JS, Zhao H, Nice KA, Thompson J, Scully K, Guo J, Stevenson M., Identifying safe intersection design through unsupervised feature extraction from satellite imagery, Comput Aided Civ Inf, 1– 16, 2020.
• [2] Xing W., Ghorbani A., Weighted PageRank algorithm, Proceedings, Second Annual Conference on Communication Networks and Services Research, Fredericton, NB, Canada, 305-314, 2004.
• [3] Brandes U., Borgatti S.P., Freeman L.C., Maintaining the duality of closeness and betweenness centrality , Social Networks, 44, 153-159, 2016.
• [4] Ando H., Bell M., Kurauchi F., Wong K. I., Cheung K.-F., Connectivity evaluation of large road network by capacity-weighted Eigenvector centrality analysis, Transportmetrica A: Transport Science, 2020
• [5] Xu M., Wu J., Liu M., Xiao Y., Wang H., Hu D., Discovery of Critical Nodes in Road Networks Through Mining From Vehicle Trajectories, IEEE Transactions on Intelligent Transportation Systems, 20(2), 583-593, 2019.
• [6] Wang J., Mo H., Wang F., Jin F., Exploring the network structure and nodal centrality of China’s air transport network: A complex network approach , Journal of Transport Geography, 19(4), 712-721, 2011.
• [7] Rui Y., Ban Y., Exploring the relationship between street centrality and land use in Stockholm, International Journal of Geographical Information Science, 28(7) , 2014.
• [8] Liu W., Li X., Liu T., Liu B., Approximating Betweenness Centrality to Identify Key Nodes in a Weighted Urban Complex Transportation Network, Journal of Advanced Transportation, Volume 2019, 2019.
• [9] El-adaway I. H., Abotaleb I., Vechan E., Identifying the most critical transportation intersections using social network analysis, Transportation Planning and Technology, 41(4), 353-374, 2018
• [10] Wang F., Antipova A., Porta S., Street centrality and land use intensity in Baton Rouge, Louisiana, Journal of Transport Geography, 19(2), 285-293, 2011
• [11] Porta S., Strano E., Iacoviello V., Messora R., Latora V., Cardillo A., Wang F., Scellato S., Street Centrality and Densities of Retail and Services in Bologna, Italy, Environment and Planning B: Planning and Design, 36(3), 450-465, 2019.
• [12] Derrible S., Network Centrality of Metro Systems, PLoS ONE, 7(7), 2012
• [13] Wang K., Fu X., Research on centrality of urban transport network nodes, AIP Conference Proceedings, 1839(1), (2017).
• [14] Öztemi̇z F., Karcı A., Akademik Yazarların Yayınları Arasındaki İlişkinin Sosyal Ağ Benzerlik Yöntemleri ile Tespit Edilmesi, Uludağ University Journal of The Faculty of Engineering, 25(1), 591-608, 2020
• [15] Bader D. A., Madduri K., Parallel Algorithms for Evaluating Centrality Indices in Real-world Networks, International Conference on Parallel Processing (ICPP'06), Columbus-OH, 539-550, August, 2006
• [16] Salman C., A New Network Centrality Measure: Relative Edge Importance Method, Master Thesis, Department of Industrial Engineering, Hacettepe University, Ankara, 2018
• [17] İnce K., Karcı A., Modelling and statistical analysis of academic collaborations as a new graph type, Journal of the Faculty of Engineering and Architecture of Gazi University, 34(1), 439-459, 2019
• [18] – World Trade Map. https://www.trademap.org/ . Erişim Tarihi Kasım 11, 2020.
• [19] You K., Tempo R., Qiu L., Distributed Algorithms for Computation of Centrality Measures in Complex Networks, IEEE Transactions on Automatic Control, 62(5), 2080-2094, 2017
• [20] Yan E., Ding Y., Discovering author impact: A PageRank perspective, Information Processing & Management, 47(1), 125-134, 2011.
• [21] Sariyüce A. E., Kaya K., Saule E., Çatalyiirek Ü. V., Incremental algorithms for closeness centrality, IEEE International Conference on Big Data, Silicon Valley, CA, 487-492, October 2013.
• [22] Bonacich P., Some unique properties of eigenvector centrality, Social Networks, 29(4), 555-564, 2007
• [23] Brandes U., On variants of shortest-path betweenness centrality and their generic computation, Social Networks, 30(2), 136-145, 2008.