Communications - Scientific Letters of the University of Zilina 2025, 27(2):D67-D75 | DOI: 10.26552/com.C.2025.020

Developing a Speed-Based Congestion Severity Index Using the Clustering Technique for Developing Countries

Malaya Mohanty ORCID..., Satya Ranjan Samal ORCID...*, Kundan Samal ORCID...
School of Civil Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, Odisha, India

A novel approach for traffic congestion assessment has been presented using percentile speeds as key indicators, focusing on urban roads. By evaluating the 98th, 85th, and 15th percentile speeds, authors of the research developed a congestion severity index, offering a more precise and intuitive method for analyzing traffic flow compared to traditional travel time-based indices. Key congestion indices, such as the Planning Time Index (PTI) and Travel Time Index (TTI) were compared to percentile speeds, revealing a significant association with the 15th and 85th percentile speeds. The K-means clustering technique was applied to classify congestion severity into three levels, validated by a high silhouette value indicating the robust clustering. The study's speed-based congestion severity index provides a practical and efficient framework for real-time congestion management, particularly in heterogeneous traffic environments.

Keywords: traffic congestion, percentile speed, k-mean clustering, silhouette value, congestion severity index
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: October 8, 2024; Accepted: January 31, 2025; Prepublished online: February 17, 2025; Published: April 1, 2025  Show citation

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Mohanty, M., Ranjan Samal, S., & Samal, K. (2025). Developing a Speed-Based Congestion Severity Index Using the Clustering Technique for Developing Countries. Communications - Scientific Letters of the University of Zilina, 27(2), D67-75. doi: 10.26552/com.C.2025.020
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