Signal and Data Processing Journal A scientific journal officially licensed by the Commission for Scientific Publications of the (MSRT). Publisher: Research Ceter for Developmen of Technologies
Volume 22, Issue 3 (12-2025)
JSDP 2025, 22(3): 19-34 |
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Ahrari V, Afshari R. Analysis of the traffic structure of the roads of Chahar Mahal and Bakhtiari province using data-mining approaches. JSDP 2025; 22 (3) : 2
URL: http://jsdp.rcisp.ac.ir/article-1-1435-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1435-en.html
Assistant Professor, Department of Computer Science, Faculty of Mathematical Sciences, Shahrekord University, Shahrekord, Iran
Abstract: (615 Views)
Road traffic management is a fundamental and multidimensional challenge within transportation systems, exerting a direct impact on public safety, economic efficiency, and environmental sustainability. The province of Chaharmahal and Bakhtiari, due to its strategic geographical location, plays a critical role in connecting various regions of Iran. Therefore, a precise analysis of the traffic structure of this province’s roadways is essential for improving the quality of data-driven planning and decision-making in the transportation sector. In this study, road traffic-counter data collected during September were utilized. These data include key variables such as the average number of vehicles in five different classes, instances of traffic violations (namely speeding, tailgating, and illegal overtaking), as well as the average speed on various road segments. The data were analyzed using three unsupervised learning algorithms: k-means clustering, hierarchical clustering, and autoencoder-based clustering. To assess the accuracy and performance of these algorithms in segment clustering, three well-established evaluation metrics—Silhouette score, Davies-Bouldin index, and Calinski-Harabasz index—were employed. The results demonstrate that the autoencoder and hierarchical clustering models offer a more accurate classification of road segments compared to the conventional k-means method, revealing latent traffic structures more effectively. Based on the findings, the roads in the province were categorized into two distinct clusters: the first cluster includes segments with the highest traffic volume and the highest rates of speeding and tailgating violations—indicative of risky driving behaviors and elevated accident risk. The second cluster encompasses segments characterized by safer traffic patterns. The primary contribution of this research lies in the integrated application of multiple advanced clustering algorithms alongside diverse performance evaluation metrics, which significantly enhance the precision and robustness of the analysis. Furthermore, the combination of technical and behavioral traffic variables within a unified data-driven framework enables the extraction of deeper insights into traffic behavior patterns. The proposed framework is generalizable to other regions and can serve as a novel model for the intelligent management of roadway networks.By providing accurate and actionable analytical tools, this study has the potential to support decision-makers in raising awareness, optimizing resource allocation, designing safety strategies, and ultimately reducing accident rates. To extend and enrich this line of research, future studies are encouraged to incorporate temporal (e.g., seasonal) analyses, integrate human and environmental variables, and develop hybrid predictive models in the transportation domain.
Article number: 2
Keywords: Unsupervised learning, clustering, traffic structure analysis, autoencoder, speeding violation, tailgating, Chaharmahal and Bakhtiari Province.
Type of Study: Research |
Subject:
Paper
Received: 2024/07/30 | Accepted: 2025/07/21 | Published: 2025/12/19 | ePublished: 2025/12/19
Received: 2024/07/30 | Accepted: 2025/07/21 | Published: 2025/12/19 | ePublished: 2025/12/19
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https://doi.org/10.1002/(SICI)1097-0266(199606)17:6<441::AID-SMJ819>3.0.CO;2-G [DOI:10.1002/(SICI)1097-0266(199606)17:63.0.CO;2-G]
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