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Exploring Road Traffic Accidents Hotspots Using clustering algorithms and GIS-Based Spatial Analysis

IEEE ACCESS 2025年 13卷 60944-60954页

作者： Kamh, Hussien Alyami, Saleh H. Khattak, Afaq Alyami, Mana Almujibah, Hamad Najran Univ Coll Comp Sci & Informat Syst Informat Syst Dept Najran 55461 Saudi Arabia Najran Univ Coll Engn Dept Civil Engn Najran 55461 Saudi Arabia Tongji Univ Coll Transportat Engn Shanghai 200070 Peoples R China Taif Univ Coll Engn Dept Civil Engn Taif 21974 Saudi Arabia

This study conducts a comprehensive spatial analysis of road traffic accidents (RTAs) in Najran, a city emblematic of rapid urbanization in Saudi Arabia, which is facing significant public safety challenges due to an increase in vehicular traffic. By means of a dataset from 2022, we explore the spatial distribution of RTAs across the city's districts by employing advanced clustering algorithms, including Density-based Spatial clustering of Applications with Noise (DBSCAN) and Hierarchical Agglomerative clustering (HAC), as well as GIS-based density analysis, proximity analysis, and spatial interpolation, to unveil accident hotspots and disparities in emergency service coverage. Our findings reveal that (1) the HAC model, based on the Silhouette and Calinski-Harabasz Scores, performs better in identifying accident hotspots;(2) significant concentrations of accidents are observed along major highways and arterial roads, pinpointing critical hotspots within the city's fabric;(3) proximity analysis indicates gaps in the coverage of ambulance services and public hospitals relative to high-incident areas;(4) through spatial interpolation, detailed visualizations of RTA distributions are provided, revealing diverse accident patterns across Najran. The study highlights the critical role of spatial analysis in identifying high-risk areas and provides valuable insights for transport planners and public safety officials, supporting the development of targeted strategies to improve road safety and enhance emergency service responses.

关键词： Urban areas Accidents clustering algorithms Road traffic Public security Spatial resolution Road traffic accidents spatial analysis Najran Saudi Arabia

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The distance and entropy measures-based intuitionistic fuzzy C-means and similarity matrix clustering algorithms and their applications

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APPLIED SOFT COMPUTING 2025年 169卷

作者： Zhang, Yueyue Huang, Han-Liang Minnan Normal Univ Sch Math & Stat Zhangzhou 363000 Fujian Peoples R China Fujian Key Lab Granular Comp & Applicat Zhangzhou 363000 Peoples R China

Determining the similarity between datasets by distance/similarity measures is important inmost clustering algorithms. Therefore, this paper characterizes two clustering algorithms with improved distance measures. In the similarity matrix clustering algorithm, the mixed correlation coefficient and two probability parameters are suggested into the weighted probabilistic Euclidean distance (WPED) measure, which expresses the randomness and fuzziness of intuitionistic fuzzy sets. Second, based on the characteristics of dynamic time warping (DTW), a DTW-based probabilistic Euclidean distance (PED-DTW) method is introduced. The attribute ordering rule is developed with the help of entropy, which is beneficial for PED-DTW in achieving optimal path matching. Furthermore, a DTW-based intuitionistic fuzzy C-means algorithm is constructed. Finally, we performed experiments using car datasets and 7 UCI datasets. Compared with the existing method, the similarity matrix clustering algorithm is feasible. The DTW-based intuitionistic fuzzy C-means algorithms is compared with six clustering methods on four performance metrics, and the performance of our algorithm is analyzed. Our algorithm performs well on four performance metrics. Experimental results indicate that the clustering algorithms proposed in this paper are practical and outperform their existing counterparts. Also, the proposed algorithm gives good results in image segmentation.

关键词： Mixed correlation coefficient Distance measures Entropy Dynamic time warping clustering algorithms

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Optimization of Financial Asset Allocation and Risk Management Strategies Combining Internet of Things and clustering algorithms

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IEEE INTERNET OF THINGS JOURNAL 2025年第4期12卷 3654-3669页

作者： Lyu, Sijun Jiao, Zhi St Marys Univ Econ Dept Halifax NS B3H 3C3 Canada Univ Sydney Econ Sydney NSW 2008 Australia Hubei Hongtai Grp Fac Dept Financial Dev Wuhan 430071 Hubei Peoples R China

This study proposes a new method for optimizing household financial asset allocation (FAA) and developing scientific risk control strategies by integrating Internet of Things (IoT) data and clustering algorithms. IoT technology is utilized to collect real-time data, including macroeconomic indicators, market dynamics, and investor behavior. The data comes from national statistical offices, financial market trading platforms, and social media. The optimized K-means++ clustering algorithm is used for asset classification. Moreover, its performance is evaluated through indexes, such as the silhouette coefficient, adjusted rand index (ARI), Davies-Bouldin index (DBI), and mutual information (MI). The results show that the K-means++ algorithm outperforms these indexes, especially in terms of silhouette coefficient (0.62), ARI (0.75), and MI (0.80). In terms of asset allocation optimization, the proportion of stock products and wealth management products has increased to 4.94% and 12.34%, indicating an improvement in asset diversification and risk diversification. Risk assessment reveals that Internet financial behavior and economic development level have a marked impact on asset allocation. For example, the asset allocation of households with active Internet financial behavior is more diversified. The cash deposit ratio in the eastern region is 79.32%, lower than the 86.08% in the western region. This study provides effective methods for managing household financial assets, which can help improve household wealth management, enhance risk management capabilities, and promote the development of financial technology.

关键词： Resource management clustering algorithms Internet of Things Risk management Real-time systems Classification algorithms Heuristic algorithms Investment Accuracy Optimization Asset optimization clustering algorithms financial asset allocation (FAA) Internet of Things (IoT) data risk management

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Web-Based Framework for Evaluating clustering algorithms Across Multiple Datasets

Web-Based Framework for Evaluating Clustering Algorithms Acr...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Tiwari, Gyanendar Kumar Singh, Kshitij Dorathi Jayaseeli, J.D. Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India

ISBN: (纸本)9798331509828

This research study develops a web-based framework to compare clustering algorithms based on multiple datasets, assess the performance of algorithmic based on dataset characteristics and clustering objectives. The framework designed to support such widely used algorithms such as KM, DBSCAN and Hierarchical clustering, offers visual and quantitative analysis for performance comparison. This platform was designed to make it easier for users to try out various algorithms and different parameter settings and thus, help them to choose the best clustering methods for a range of data types. This work impacts that greater understanding of how effective clustering is based on different data structures and application contexts by providing a useful framework for data driven research and analysis. © 2025 IEEE.

关键词： clustering algorithms DBSCAN Hierarchical clustering K-Means Web-based framework

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A review on clustering algorithms for spatiotemporal seismicity analysis

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Artificial Intelligence Review 2025年第8期58卷 1-61页

作者： Vijay, Rahul Kumar Nanda, Satyasai Jagannath Sharma, Ashish Department of Computer Science Banasthali Vidyapith Rajasthan Tonk304022 India Department of Electronics and Communication Engineering Malaviya National Institute of Technology Jaipur Rajasthan Jaipur302017 India Department of Electronics and Communication Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

Spatiotemporal seismicity analysis has been conducted for a long time, yet significant effort is still needed to mitigate the adverse effects of earthquakes. Seismicity analysis also encompasses fundamental research into seismic patterns, for understanding the frequency, magnitude, temporal and spatial distribution of seismic events. Over the past few decades, it has been carried out through empirical relations, physics-based approaches, stochastic modeling, various machine learning algorithms, and deep learning algorithms for any given seismically active region. clustering is an essential aspect of seismicity analysis, making it more complex, difficult, and challenging due to significant deviation from the stochastic phenomenon. In this paper, a comprehensive review of all potential data-driven earthquake clustering algorithms, models, and mechanisms are encapsulated for a variety of applications in seismology. The paper also describes the importance of an earthquake catalog with a short review of the fundamental empirical laws frequently used in statistical seismology. This paper also highlights the problem of seismicity declustering and reviews all the available algorithms to deal with it. © The Author(s) 2025.

关键词： clustering algorithms

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Big Data clustering algorithms: Improving Efficiency and Scalability for Massive Data Mining and Pattern Recognition

Big Data Clustering Algorithms: Improving Efficiency and Sca...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Shanmugam Muthu G Ramesh Kalyan V. Samuthira Pandi Shobana D Lakshmi Priya J Jaishika S Shipt Inc Birmingham USA Department of Computer Science and Engineering SNS College of Technology Coimbatore Tamilnadu India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of Mechatronics Engineering Rajalakshmi Engineering College Chennai Department of ECE Saveetha Engineering College Chennai Department of Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The emergence of big data has completely changed the way data analysis is done. As a result, there is a pressing need for effective clustering algorithms that can handle large datasets from different fields. Efficiency, scalability, and finding meaningful patterns in enormous data are some of the important difficulties that this research tackles in relation to big data clustering. We thoroughly examine and evaluate a range of clustering algorithms, from more conventional ones like hierarchical and K-Means clustering to more cutting-edge ones like density-based spatial clustering (DBSCAN) and model-based clustering. To further improve the performance and scalability of clustering algorithms, we investigate new methods that make use of distributed computing frameworks like Apache Spark and MapReduce. We offer a thorough assessment of their efficacy in terms of clustering quality, computational efficiency, and resource use by using and evaluating these methods on large-scale datasets. While conventional clustering algorithms may fail miserably when faced with extremely big data sets, our research shows that newer methods that make use of optimization techniques and distributed processing greatly outperform their predecessors. The effect of feature selection and dimensionality reduction techniques on improving clustering results is also shown. By shedding light on how to choose and implement clustering algorithms for effective data mining and pattern identification, this study adds to the continuing endeavors in the domain of big data analytics. Progress in areas like customer segmentation, anomaly detection, and social network analysis can be achieved with the help of adaptive algorithms that can handle large datasets while keeping clustering results accurate and dependable, according to the study.

关键词： Dimensionality reduction Machine learning algorithms Accuracy Social networking (online) Scalability Heuristic algorithms clustering algorithms Adaptive algorithms Big Data Computational efficiency

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Web-Based Framework for Evaluating clustering algorithms Across Multiple Datasets

Web-Based Framework for Evaluating Clustering Algorithms Acr...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Gyanendar Kumar Tiwari Kshitij Singh Dorathi Jayaseeli J D Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

关键词： Measurement Visualization Statistical analysis Heuristic algorithms Industry applications clustering algorithms Data visualization Real-time systems Optimization Gaussian mixture model

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Autoencoders and Multidimensional clustering algorithms in the Identification of Engineering-Geological Elements

Autoencoders and Multidimensional Clustering Algorithms in t...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Nikita Tishin Andrey Proletarsky Oleg Ozmidov Bauman Moscow State Technical University Moscow Russia JSC MOSTDORGEOTREST Moscow Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The article investigates the use of modern machine learning techniques in engineering geological analysis. Key methods include autoencoders for dimensionality reduction and uncovering hidden patterns, alongside clustering algorithms (k-means, fuzzy c-means, DBSCAN, GMM, and Self-Organizing Map (SOM)) to identify engineering geological elements (EGEs). Indicator kriging is utilized for spatial interpolation of categorical data. The study analyzes a dataset of 90 soil samples, focusing on parameters such as soil density, modulus of deformation, and internal friction angle, using PCA, Variance Threshold, and correlation analysis for feature selection. Among clustering algorithms, k-means demonstrated the best results (silhouette score = 0.635, Davies-Bouldin index = 0.454), making it ideal for soil transitional layers. Fuzzy c-means and DBSCAN showed advantages for transitional zones and irregular densities, respectively. Indicator kriging produced detailed spatial cross-sections using an exponential variogram, significantly reducing modeling uncertainty. Autoencoders without intermediate layers achieved high reconstruction accuracy (R2 = 0.72, MSE = 0.02), effectively supporting clustering in latent space. The study emphasizes the importance of proper normalization, particularly for heterogeneous datasets, and highlights the potential of machine learning to improve classification, interpolation, and spatial modeling of EGEs. Future research should address autoencoder sensitivity to normalization and explore feature selection techniques, such as random forests, to enhance robustness and scalability.

关键词： Self-organizing feature maps Interpolation Machine learning algorithms Uncertainty Sensitivity Geology Autoencoders clustering algorithms Soil Feature extraction

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Optimizing Energy-Efficient clustering algorithms for Prolonged Lifetime in WSN-IoT Deployments

Optimizing Energy-Efficient Clustering Algorithms for Prolon...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： G Sunil Gaurav Tuteja Parul Nasra Haayder M. Abbas School of Computer Science and Artificial Intelligence SR University Telangana India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

They noticed that energy efficiency is still a difficult problem in wireless WSNs that act in IoT conditions. The importance of clustering algorithms has raised as the approach to extending the lifetime of networks and the utilization of resources. This research aims to discover a range of new clustering techniques and the consequences which are presented by such procedures for energy-efficient placement of WSN-IoT. These algorithms are judged for energy consumption, computational intensity, and flexibility and scalability of the networks. In this paper, several applications such as environmental monitoring, precision agriculture, and smart cities are analyzed. The study also aimed at pointing at the pros and cons of the different methodologies which are being used. From the results of the present work, we get the understanding of the tendencies in clustering approaches evolution to meet the IoT demands and future research prospects will concern the development of hybrid models and adaptive algorithms.

关键词： Wireless communication Wireless sensor networks Adaptation models Technological innovation Protocols Smart cities Scalability clustering algorithms Energy efficiency Internet of Things

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Integrating clustering algorithms and machine learning to optimize regional snapshot municipal solid waste management for achieving sustainable development goals

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Environmental Advances 2025年 19卷

作者： Taweesan, Achara Kanabkaew, Thongchai Surinkul, Nawatch Polprasert, Chongrak Department of Environmental Science Faculty of Science Ramkhamhaeng University Bangkok Thailand Occupational and Environmental Health Program Faculty of Public Health Thammasat University Pathumthani Thailand Department of Civil and Environmental Engineering Faculty of Engineering Mahidol University Thailand Department of Civil Engineering Faculty of Engineering Thammasat University Pathumthani Thailand

Effective municipal solid waste management (MSWM) is essential for sustainable urban development, significantly impacting environmental health, economic efficiency, and social well-being. It aligns with Sustainable Development Goal 11 (SDG11), which aims to make cities inclusive, safe, resilient, and sustainable by ensuring waste is safely managed from collection to disposal. This study evaluates the performance of MSWM practices across various cities in different regions, updated as of March 2023, to assess the achievement of SDG11. By reviewing published literature and conducting questionnaire surveys and key informant interviews with government authorities and service providers, data on access to managed MSW facilities were collected from 470 cities across various income-level regions to monitor MSWM progress toward SDG11 targets. clustering algorithms and machine learning were employed to identify patterns and enhance MSWM practices across these regions, focusing on achieving SDG11. Hierarchical cluster analysis identified four clusters based on MSWM performance: high-income, upper-middle-income, lower-middle-income, and low-income regions. The developed decision-tree models provided a comprehensive analysis of effective MSW collection and disposal, achieving accuracies of approximately 75 % and 73 %, respectively. The models highlighted the importance of high collection coverage, proper disposal facilities, and robust institutional frameworks in achieving effective MSWM. The findings emphasize the need for targeted interventions and advanced analytical methods to enhance regional MSWM strategies, supporting the attainment of SDG11. © 2024 The Author(s)

关键词： clustering algorithms Collection efficiency Machine learning Municipal solid waste management Sustainable development goal Treatment efficiency Waste management practices

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