Communications - Scientific Letters of the University of Zilina 2022, 24(4):A232-A245 | DOI: 10.26552/com.C.2022.4.A232-A245

Urban Road Transport Network Analysis: Machine Learning and Social Network Approaches

Emre Kuºkapan ORCID...1, M. Yasin Çodur ORCID...2, Ahmet Tortum ORCID...3, Giovanni Tesoriere ORCID...4, Tiziana Campisi ORCID...4, *
1 Department of Civil Engineering, Engineering and Architecture Faculty, Erzurum Technical University, Erzurum, Turkey
2 College of Engineering and Technology, American University of the Middle East, Kuwait
3 Department of Civil Engineering, Engineering Faculty, Ataturk University, Erzurum, Turkey
4 Faculty of Engineering and Architecture, Kore University of Enna, Enna, Italy

Traffic congestion is one of the most significant problems in urban transportation. It has been increasing, especially in regions close to intersections. Several methods have been developed to reduce the traffic congestion. One of the analysis methods is social network analysis (SNA). This method, which has increased use in transportation, can quickly identify the most central intersections in transportation networks. Improvements to central intersections, identified in a road network structure, speed up the traffic flow across the entire network structure. In this study, the Istanbul highway transportation network has been examined and values for a series of network centrality measures have been calculated using the SNA. The accuracy and error scales of the centrality values were compared using a machine learning algorithm. The Bonacich power centrality has been the best performance. Based on the study results the most central intersections in Istanbul have been determined.

Keywords: central intersections, machine learning, transportation planning, urban transportation
Grants and funding:

The authors received no financial support for the research, authorship, and/or publication of this article.

Conflicts of interest:

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Received: May 7, 2022; Revised: October 3, 2022; Accepted: September 20, 2022; Prepublished online: October 17, 2022; Published: October 26, 2022  Show citation

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Kuºkapan, E., Yasin Çodur, M., Tortum, A., Tesoriere, G., & Campisi, T. (2022). Urban Road Transport Network Analysis: Machine Learning and Social Network Approaches. Communications - Scientific Letters of the University of Zilina, 24(4), A232-245. doi: 10.26552/com.C.2022.4.A232-A245
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