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Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Articles

ANALYSIS OF REAL RELATIVE ASYMMETRY IN URBAN TRANSPORTATION NETWORK PROBLEMS USING SPACE SYNTAX, REDS, AND MACHINE LEARNING CONCEPTS

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  • Robiatul Adawiyah,
  • Fitriyatul Mardiyah,
  • Dafik Dafik,
  • Ika Hesti Agustin,
  • Excelsa Suli Wildhatul Jannah,
  • Marsidi Marsidi
Robiatul Adawiyah
Department of Mathematics Education, Faculty of Teacher Training and Education, Universitas Jember, Indonesia
Fitriyatul Mardiyah
Department of Mathematics Education, Faculty of Teacher Training and Education, Universitas Jember, Indonesia
Dafik Dafik
Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Jember, Indonesia
Ika Hesti Agustin
Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Jember, Indonesia
Excelsa Suli Wildhatul Jannah
PUI-PT Combinatorics and Graph, CGANT, Universitas Jember, Indonesia
Marsidi Marsidi
Department of Mathematics Education, Universitas PGRI Argopuro Jember, Indonesia
DOI: https://doi.org/10.30598/barekengvol19iss3pp1865-1878
Published July 1, 2025
Keywords
  • Machine Learning,
  • REDS,
  • Space Syntax,
  • Urban Transportation
How to Cite
[1]
R. Adawiyah, F. Mardiyah, D. Dafik, I. H. Agustin, E. S. W. Jannah, and M. Marsidi, “ANALYSIS OF REAL RELATIVE ASYMMETRY IN URBAN TRANSPORTATION NETWORK PROBLEMS USING SPACE SYNTAX, REDS, AND MACHINE LEARNING CONCEPTS”, BAREKENG: J. Math. & App., vol. 19, no. 3, pp. 1865-1878, Jul. 2025.

Copyright (c) 2025 Robiatul Adawiyah, Fitriyatul Mardiyah, Dafik Dafik, Ika Hesti Agustin, Excelsa Suli Wildhatul Jannah, Marsidi Marsidi

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Abstract

In the context of urban growth and increasing population density, urban transportation networks face significant challenges such as traffic congestion, infrastructure limitations, and traffic law violations. This study integrates three analytical approaches—Space Syntax, Resolving Efficient Dominating Set (REDS), and Graph Neural Networks (GNN)—to identify strategic locations for the deployment of mobile Electronic Traffic Law Enforcement (ETLE) units and to forecast potential traffic violations. The research focuses on Malang City, Indonesia, and utilizes spatial data and ETLE violation records. Results show that Laksamana Martadinata Street, which has the lowest Real Relative Asymmetry (RRA) value, is a key strategic location for monitoring. The REDS analysis yields a resolving efficient domination number that determines the optimal quantity and placement of mobile ETLE units. GNN-based multi-step time series forecasting successfully predicts traffic violation trends across 29 road segments with a Mean Squared Error (MSE) equal to 0.0173. The novelty of this research lies in the integration of spatial configuration analysis, graph theoretical optimization, and machine learning-based forecasting, offering a comprehensive approach not previously combined in related studies. However, limitations include the use of a single urban case study and constraints in the availability and granularity of violation data, which may affect the generalizability of the findings.

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