Geographical Self-Organizing Map Clustering in Large-Scale Urban Networks for Perimeter Control

Maha Elouni, Hesham Ahmed Rakha, Monica Menendez, Hossam Abdelghaffar, Hossam Abdelghaffar

2024

Abstract

Traffic congestion in urban areas presents a major challenge to efficient transportation systems. Recent advancements in traffic management provide promising solutions, with perimeter control emerging as a technique to tackle network-wide congestion. However, it is crucial to identify geographically connected homogeneously congested areas for effective implementation. This research explores the application of clustering techniques, particularly geographical self-organizing maps (GeoSOM), to identify spatially connected and homogeneously congested areas within transportation networks. While GeoSOM has found applications across various domains, its adaptation to transportation networks for congestion clustering is novel. This study introduces and implements an adaptation of the GeoSOM algorithm tailored for the large-scale urban environment of downtown Los Angeles. Its performance is assessed through a comparative evaluation with two other clustering algorithms, namely DBSCAN and K-means. The results demonstrate that GeoSOM surpasses other clustering algorithms, exhibiting improvements of up to 43% in traffic density variance, up to 61% in the spatial quantization error, and 15% in the quantization error. This finding demonstrates that the proposed clustering algorithm is effective in identifying a spatially homogeneous congested area within a large-scale transportation network.

Paper Citation

in Harvard Style

Elouni M., Ahmed Rakha H., Menendez M. and Abdelghaffar H. (2024). Geographical Self-Organizing Map Clustering in Large-Scale Urban Networks for Perimeter Control. In Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS; ISBN 978-989-758-703-0, SciTePress, pages 465-472. DOI: 10.5220/0012729300003702

in Bibtex Style

@conference{vehits24,
author={Maha Elouni and Hesham Ahmed Rakha and Monica Menendez and Hossam Abdelghaffar},
title={Geographical Self-Organizing Map Clustering in Large-Scale Urban Networks for Perimeter Control},
booktitle={Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS},
year={2024},
pages={465-472},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012729300003702},
isbn={978-989-758-703-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS
TI - Geographical Self-Organizing Map Clustering in Large-Scale Urban Networks for Perimeter Control
SN - 978-989-758-703-0
AU - Elouni M.
AU - Ahmed Rakha H.
AU - Menendez M.
AU - Abdelghaffar H.
PY - 2024
SP - 465
EP - 472
DO - 10.5220/0012729300003702
PB - SciTePress