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Journal articles on the topic 'Traffic matrix'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Medina, A., N. Taft, K. Salamatian, S. Bhattacharyya, and C. Diot. "Traffic matrix estimation." ACM SIGCOMM Computer Communication Review 32, no. 4 (2002): 161–74. http://dx.doi.org/10.1145/964725.633041.

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2

Adhikari, Vijay Kumar, Sourabh Jain, and Zhi-Li Zhang. "From traffic matrix to routing matrix." ACM SIGMETRICS Performance Evaluation Review 38, no. 3 (2011): 49–54. http://dx.doi.org/10.1145/1925019.1925029.

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3

Tune, Paul, and Matthew Roughan. "Spatiotemporal Traffic Matrix Synthesis." ACM SIGCOMM Computer Communication Review 45, no. 4 (2015): 579–92. http://dx.doi.org/10.1145/2829988.2787471.

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4

He, Hui, Ming Chang, Xing Wang, Wen Juan Li, Hong Li Zhang, and Hong Mei Ma. "The Quantification of Overlay Network Congestion Based on Compressive Sensing." Advanced Materials Research 268-270 (July 2011): 1564–67. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1564.

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To obtain overlay network traffic and delay information between two hosts is important for network management, monitoring, design, planning and assessment. Traffic matrix and delay matrix represent the traffic and delay information between two hosts, so introduce the concept of the overlay network traffic matrix and delay matrix. Compressive sensing theory restores traffic matrix and delay matrix but is not suitable for overlay network. This paper improves compressive sensing algorithm to make it more applicable to overlay network traffic matrix and delay matrix restoration. After calculating

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5

ZHOU, Jing-Jing. "Research on Traffic Matrix Estimation." Journal of Software 18, no. 11 (2007): 2669. http://dx.doi.org/10.1360/jos182669.

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6

Tune, Paul, and Matthew Roughan. "Maximum entropy traffic matrix synthesis." ACM SIGMETRICS Performance Evaluation Review 42, no. 2 (2014): 43–45. http://dx.doi.org/10.1145/2667522.2667536.

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7

Wang, Ying, and Zongzhong Tian. "Efficient Original-Destination Bandwidth: A Novel Model for Arterial Traffic Signal Coordination." Journal Européen des Systèmes Automatisés 53, no. 5 (2020): 609–16. http://dx.doi.org/10.18280/jesa.530503.

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This paper proposes an efficient origin-estimation bandwidth (OD band) model, which provides dedicated progression bands for arterial traffic based on the real-time dynamic matrix of their estimated OD pairs. The innovations of the OD band model are as follows: First, the dynamics of through and turning-in/out traffics are analyzed based on the matrix of their estimated OD pairs, and used to generate the traffic movement sequence at continuous intersections; Second, the end-time of green interval for lag-lag phase sequence at continuous intersections is determined according to the relevant con

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8

Chokshi, Rajvi. "Traffic Controlling System." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (2021): 1507–12. http://dx.doi.org/10.22214/ijraset.2021.38656.

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Abstract: In the present era, the population of people living in cities and the number of vehicles on the road is growing by the day. The necessity to govern lanes, thruways, and streets has become a significant concern as the urban population and, as a result, the number of vehicles has grown. Today's traffic framework places minimal emphasis on real-time traffic conditions, resulting in inefficient traffic management systems. Therefore, to overcome such limitations or drawbacks of the present system, the current research proposes a smart and efficient traffic management system that can analy

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9

Benameur, N., and J. W. Roberts. "Traffic Matrix Inference in IP Networks." Networks and Spatial Economics 4, no. 1 (2004): 103–14. http://dx.doi.org/10.1023/b:nets.0000015658.75205.ed.

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10

Zhou, Huibin, Dafang Zhang, Kun Xie, and Xiaoyang Wang. "Data reconstruction in internet traffic matrix." China Communications 11, no. 7 (2014): 1–12. http://dx.doi.org/10.1109/cc.2014.6895380.

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11

Soule, Augustin, Kavé Salamatian, Antonio Nucci, and Nina Taft. "Traffic matrix tracking using Kalman filters." ACM SIGMETRICS Performance Evaluation Review 33, no. 3 (2005): 24–31. http://dx.doi.org/10.1145/1111572.1111580.

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12

Jiang, Dingde, Xingwei Wang, and Lei Guo. "Mahalanobis distance-based traffic matrix estimation." European Transactions on Telecommunications 21, no. 3 (2010): 195–201. http://dx.doi.org/10.1002/ett.1382.

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13

Tian, Hui, Yingpeng Sang, Hong Shen, and Chunyue Zhou. "Probability-model based network traffic matrix estimation." Computer Science and Information Systems 11, no. 1 (2014): 309–20. http://dx.doi.org/10.2298/csis130212010t.

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Traffic matrix is of great help in many network applications. However, it is very difficult to estimate the traffic matrix for a large-scale network. This is because the estimation problem from limited link measurements is highly underconstrained. We propose a simple probability model for a large-scale practical network. The probability model is then generalized to a general model by including random traffic data. Traffic matrix estimation is then conducted under these two models by two minimization methods. It is shown that the Normalized Root Mean Square Errors of these estimates under our m

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14

He, Lin Bo, Li Liu, and Zhi Wei Sheng. "Research of Network Traffic Matrix Based on Improved Fanout Model." Applied Mechanics and Materials 321-324 (June 2013): 2745–48. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.2745.

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Traffic matrix plays a very important role in network management field, such as network design, network optimization, traffic detection, etc. As a result, it is always a hot topic in network research. Based on traditional fanout model, an improved fanout model is proposed to conduct traffic matrix estimation. The model takes into consideration of estimation deviation brought by non-persistent sudden burst of networks flow in a short time, which improves the accuracy of traffic matrix estimation. The simulation shows with algorithm the estimation value has greatly improved in a one-day period.

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15

Li, Bao, Quan Yang, Jianjiang Chen, Dongjin Yu, Dongjing Wang, and Feng Wan. "A Dynamic Spatio-Temporal Deep Learning Model for Lane-Level Traffic Prediction." Journal of Advanced Transportation 2023 (March 8, 2023): 1–14. http://dx.doi.org/10.1155/2023/3208535.

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Traffic prediction aims to predict the future traffic state by mining features from history traffic information, and it is a crucial component for the intelligent transportation system. However, most existing traffic prediction methods focus on road segment prediction while ignore the fine-grainedlane-level traffic prediction. From observations, we found that different lanes on the same road segment have similar but not identical patterns of variation. Lane-level traffic prediction can provide more accurate prediction results for humans or autonomous driving systems to make appropriate and eff

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16

Diao, Zulong, Xin Wang, Dafang Zhang, Yingru Liu, Kun Xie, and Shaoyao He. "Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 890–97. http://dx.doi.org/10.1609/aaai.v33i01.3301890.

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Graph convolutional neural networks (GCNN) have become an increasingly active field of research. It models the spatial dependencies of nodes in a graph with a pre-defined Laplacian matrix based on node distances. However, in many application scenarios, spatial dependencies change over time, and the use of fixed Laplacian matrix cannot capture the change. To track the spatial dependencies among traffic data, we propose a dynamic spatio-temporal GCNN for accurate traffic forecasting. The core of our deep learning framework is the finding of the change of Laplacian matrix with a dynamic Laplacian

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17

Ye, Wencai, Lei Chen, Geng Yang, Hua Dai, and Fu Xiao. "Anomaly-Tolerant Traffic Matrix Estimation via Prior Information Guided Matrix Completion." IEEE Access 5 (2017): 3172–82. http://dx.doi.org/10.1109/access.2017.2671860.

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18

Aloraifan, Dalal, Imtiaz Ahmad, and Ebrahim Alrashed. "Deep learning based network traffic matrix prediction." International Journal of Intelligent Networks 2 (2021): 46–56. http://dx.doi.org/10.1016/j.ijin.2021.06.002.

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19

Tian, Yang, Weiwei Chen, and Chin-Tau Lea. "An SDN-Based Traffic Matrix Estimation Framework." IEEE Transactions on Network and Service Management 15, no. 4 (2018): 1435–45. http://dx.doi.org/10.1109/tnsm.2018.2867998.

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20

SHIMIZU, S. "Traffic Matrix Estimation Using Spike Flow Detection." IEICE Transactions on Communications E88-B, no. 4 (2005): 1484–92. http://dx.doi.org/10.1093/ietcom/e88-b.4.1484.

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21

Zhao, Qi, Zihui Ge, Jia Wang, and Jun Xu. "Robust traffic matrix estimation with imperfect information." ACM SIGMETRICS Performance Evaluation Review 34, no. 1 (2006): 133–44. http://dx.doi.org/10.1145/1140103.1140294.

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22

Sui, Tengfei, Xiaofeng Tao, and Jin Xu. "Random Matrix Theory-Based ROI Identification for Wireless Networks." Wireless Communications and Mobile Computing 2022 (June 21, 2022): 1–13. http://dx.doi.org/10.1155/2022/3644592.

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The identification of region of interests (ROIs) in wireless networks holds the potential to resolve the challenging problems of resource allocation and network traffic prediction for large scale traffic data generated by mobile applications. The rationale is that ROIs are capable of gathering single regions that share similar network characteristics, which promotes better network traffic prediction performance. Previous studies show that spatiotemporal information in network traffic data, such as user behaviors and network status, is nontrivial to ROI identification. However, the modeling bet

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23

Jiang, Hui, and Hongxing Deng. "Expressway Traffic Flow Missing Data Repair Method Based on Coupled Matrix-Tensor Factorizations." Mathematical Problems in Engineering 2021 (February 11, 2021): 1–12. http://dx.doi.org/10.1155/2021/2919073.

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Traffic flow data is the basis of traffic management, planning, control, and other forms of implementation. Once missing, it will directly affect the monitoring and prediction of expressway traffic status. Regarding this, this paper proposes a repair method for the traffic flow missing data of expressway, combined with the idea of coupled matrix-tensor factorizations (CMTF), to couple the auxiliary traffic flow data into the main traffic flow data and to construct the coupling matrix-tensor expression of traffic flow data, and the alternating direction multiplier algorithm is used to realize t

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24

Zhang, Bing, and Bing Jie Zhang. "Evaluation of Traffic Organization Scheme during the Construction of Urban Subway Station Based on TransCAD." Applied Mechanics and Materials 505-506 (January 2014): 433–36. http://dx.doi.org/10.4028/www.scientific.net/amm.505-506.433.

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Urban subway station construction has a direct influence on the surrounding road network, so the traffic organization scheme needs be optimized according to construction process. For accurately simulating the traffic conditions when the station to be constructed, the OD trip matrix from OD matrix estimation was distributed on different road network by using TransCAD. The traffic organization scheme would be more scientific and reasonable by multiple traffic organization scheme compared selected evaluation, which the service level of roads and intersections as the main evaluation indicators. Th

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25

Abdullah Jaber Al Hamadani, Rihab, Mahdi Mosleh, Ali Hashim Abbas Al-Sallami, and Rasool Sadeghi. "Improvement of Network Traffic Prediction in Beyond 5G Network using Sparse Decomposition and BiLSTM Neural Network." Qubahan Academic Journal 5, no. 2 (2025): 156–76. https://doi.org/10.48161/qaj.v5n2a1690.

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Companies providing telecommunication services, especially in Beyond 5G networks, are increasingly interested in traffic forecasting to improve the services provided to their users. However, forecasting network traffic is challenging due to traffic data's dynamic and non-stationary nature. This study proposes an effective deep learning-based traffic prediction technique using BiLSTM (Bidirectional Long Short-Term Memory). The proposed method begins with preprocessing using K-SVD (K-means Singular Value Decomposition) to reduce dimensionality and enhance data representation. Next, sparse featur

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26

Ermagun, Alireza, and David M. Levinson. "Development and application of the network weight matrix to predict traffic flow for congested and uncongested conditions." Environment and Planning B: Urban Analytics and City Science 46, no. 9 (2018): 1684–705. http://dx.doi.org/10.1177/2399808318763368.

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To capture network dependence between traffic links, we introduce two distinct network weight matrices ([Formula: see text]), which replace spatial weight matrices used in traffic forecasting methods. The first stands on the notion of betweenness centrality and link vulnerability in traffic networks. To derive this matrix, we use an unweighted betweenness method and assume all traffic flow is assigned to the shortest path. The other relies on flow rate change in traffic links. For forming this matrix, we use the flow information of traffic links and employ user equilibrium assignment and the m

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27

Martynenko, Alexander Valerievich, and Elena Gennadyevna Filippova. "Analysis of properties of passenger traffic gravity model for linear network." Transport of the Urals, no. 4 (2020): 23–28. http://dx.doi.org/10.20291/1815-9400-2020-4-23-28.

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For the description of spatial distribution of passenger traffic in transport network a correspondence matrix is usually used. The elements of the matrix are the volumes of passenger traffic between each pair of network vertices. The elements of the matrix can be calculated with the use of mathematical apparatus based on the transport gravity model. The correspondence matrix gained by the above mentioned method depends on network structure, model parameters and initial data on the number of incoming and departing passengers for each network vertex. Moreover, the dependence has significantly no

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28

Pachuau, Joseph L., Arnab Roy, Gopal Krishna, and Anish Kumar Saha. "Estimation of Traffic Matrix from Links Load using Genetic Algorithm." Scalable Computing: Practice and Experience 22, no. 1 (2021): 29–38. http://dx.doi.org/10.12694/scpe.v22i1.1834.

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Traffic Matrix (TM) is a representation of all traffic flows in a network. It is helpful for traffic engineering and network management. It contains the traffic measurement for all parts of a network and thus for larger network it is difficult to measure precisely. Link load are easily obtainable but they fail to provide a complete TM representation. Also link load and TM relationship forms an under-determined system with infinite set of solutions. One of the well known traffic models Gravity model provides a rough estimation of the TM. We have proposed a Genetic algorithm (GA) based optimizat

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29

Luo, Xianglong, Xue Meng, Wenjuan Gan, and Yonghong Chen. "Traffic Data Imputation Algorithm Based on Improved Low-Rank Matrix Decomposition." Journal of Sensors 2019 (July 1, 2019): 1–11. http://dx.doi.org/10.1155/2019/7092713.

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Traffic data plays a very important role in Intelligent Transportation Systems (ITS). ITS requires complete traffic data in transportation control, management, guidance, and evaluation. However, the traffic data collected from many different types of sensors often includes missing data due to sensor damage or data transmission error, which affects the effectiveness and reliability of ITS. In order to ensure the quality and integrity of traffic flow data, it is very important to propose a satisfying data imputation method. However, most of the existing imputation methods cannot fully consider t

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30

Shi, Hao, Guofeng Wang, Rouxi Wang, Jinshan Yang, and Kaishuan Shang. "Manifold Structure Analysis of Tactical Network Traffic Matrix Based on Maximum Variance Unfolding Algorithm." Journal of Electronic Research and Application 7, no. 6 (2023): 42–49. http://dx.doi.org/10.26689/jera.v7i6.5668.

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As modern weapons and equipment undergo increasing levels of informatization, intelligence, and networking, the topology and traffic characteristics of battlefield data networks built with tactical data links are becoming progressively complex. In this paper, we employ a traffic matrix to model the tactical data link network. We propose a method that utilizes the Maximum Variance Unfolding (MVU) algorithm to conduct nonlinear dimensionality reduction analysis on high-dimensional open network traffic matrix datasets. This approach introduces novel ideas and methods for future applications, incl

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31

Besenczi, Renátó, Norbert Bátfai, Péter Jeszenszky, Roland Major, Fanny Monori, and Márton Ispány. "Large-scale simulation of traffic flow using Markov model." PLOS ONE 16, no. 2 (2021): e0246062. http://dx.doi.org/10.1371/journal.pone.0246062.

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Modeling and simulating movement of vehicles in established transportation infrastructures, especially in large urban road networks is an important task. It helps in understanding and handling traffic problems, optimizing traffic regulations and adapting the traffic management in real time for unexpected disaster events. A mathematically rigorous stochastic model that can be used for traffic analysis was proposed earlier by other researchers which is based on an interplay between graph and Markov chain theories. This model provides a transition probability matrix which describes the traffic’s

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32

Liang, Yunyi, Zhiyong Cui, Yu Tian, Huimiao Chen, and Yinhai Wang. "A Deep Generative Adversarial Architecture for Network-Wide Spatial-Temporal Traffic-State Estimation." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 45 (2018): 87–105. http://dx.doi.org/10.1177/0361198118798737.

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This study proposes a deep generative adversarial architecture (GAA) for network-wide spatial-temporal traffic-state estimation. The GAA is able to combine traffic-flow theory with neural networks and thus improve the accuracy of traffic-state estimation. It consists of two Long Short-Term Memory Neural Networks (LSTM NNs) which capture correlation in time and space among traffic flow and traffic density. One of the LSTM NNs, called a discriminative network, aims to maximize the probability of assigning correct labels to both true traffic-state matrices (i.e., traffic flow and traffic density

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33

Shang, Fengjun. "An estimating method for IP traffic matrix based on generalized inverse matrix." International Journal of Intelligent Computing and Cybernetics 1, no. 4 (2008): 521–36. http://dx.doi.org/10.1108/17563780810919104.

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34

Alshehri, Abdullah, Mahmoud Owais, Jayadev Gyani, Mishal H. Aljarbou, and Saleh Alsulamy. "Residual Neural Networks for Origin–Destination Trip Matrix Estimation from Traffic Sensor Information." Sustainability 15, no. 13 (2023): 9881. http://dx.doi.org/10.3390/su15139881.

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Traffic management and control applications require comprehensive knowledge of traffic flow data. Typically, such information is gathered using traffic sensors, which have two basic challenges: First, it is impractical or impossible to install sensors on every arc in a network. Second, sensors do not provide direct information on origin-to-destination (O–D) demand flows. Consequently, it is essential to identify the optimal locations for deploying traffic sensors and then enhance the knowledge gained from this link flow sample to forecast the network’s traffic flow. This article presents resid

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35

Gao, Yuan, Jiandong Zhao, Ziyan Qin, Yingzi Feng, Zhenzhen Yang, and Bin Jia. "Traffic Speed Forecast in Adjacent Region between Highway and Urban Expressway: Based on MFD and GRU Model." Journal of Advanced Transportation 2020 (December 2, 2020): 1–18. http://dx.doi.org/10.1155/2020/8897325.

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Traffic congestion in the adjacent region between the highway and urban expressway is becoming more and more serious. This paper proposes a traffic speed forecast method based on the Macroscopic Fundamental Diagram (MFD) and Gated Recurrent Unit (GRU) model to provide the necessary traffic guidance information for travelers in this region. Firstly, considering that the road traffic speed is affected by the macroscopic traffic state, the adjacent region between the highway and expressway is divided into subareas based on the MFD. Secondly, the spatial-temporal correlation coefficient is propose

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36

Wang, Hong Zhi, and Li Hui Yan. "A New Network Traffic Classification Method Based on Optimized Hadamard Matrix and ECOC-SVM." Advanced Materials Research 989-994 (July 2014): 1895–900. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1895.

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The traditional network traffic classification methods have many shortcomings, the classification accuracy is not high, the encrypted traffic cannot be analyzed, and the computational burden is usually large. To overcome above problems, this paper presents a new network traffic classification method based on optimized Hadamard matrix and ECOC. Through restructuring the Hadamard matrix and erasing the interference rows and columns, the ECOC table is optimized while eliminating SVM sample imbalance, and the error correcting ability for classification is reserved. The experiments results show tha

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37

Saeed, Hayatullah, and Mohammad Azim Nazari. "Applications of Matrix Multiplication." Journal for Research in Applied Sciences and Biotechnology 3, no. 3 (2024): 1–8. http://dx.doi.org/10.55544/jrasb.3.3.1.

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In this paper we present some interesting applications of the matrix’s multiplication, that include the Leslie matrix and population change, which we calculate this kind of changes from year to other year by matrix multiplication. Another important part of the paper is Analysis of Traffic Flow, we represent the flow of traffic through a network of one-way streets. Another much important part is the production costs, this is fantastic usage of matrix multiplication, in which, A company manufactures three products. Its production expenses are divided into three categories, here in this paper we

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38

Qian, Ye-kui, and Ming Chen. "On the Manifold Structure of Internet Traffic Matrix." Journal of Electronics & Information Technology 32, no. 12 (2011): 2981–86. http://dx.doi.org/10.3724/sp.j.1146.2010.00130.

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39

Xiong, Zikai, Yimin Wei, Renjie Xu, and Yanwei Xu. "Low-rank traffic matrix completion with marginal information." Journal of Computational and Applied Mathematics 410 (August 2022): 114219. http://dx.doi.org/10.1016/j.cam.2022.114219.

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40

Raevskaya, A. P., and A. Yu Krylatov. "OD-Matrix Estimation for Urban Traffic Area Control." St. Petersburg State Polytechnical University Journal. Computer Science. Telecommunications and Control Systems. 236, no. 1 (2016): 31–40. http://dx.doi.org/10.5862/jcstcs.236.4.

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41

ZHANG, Ke. "Traffic matrix estimation based on generalized linear inversion." Journal of Computer Applications 28, no. 3 (2008): 582–85. http://dx.doi.org/10.3724/sp.j.1087.2008.00582.

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., Subha Nair. "TRIP MATRIX ESTIMATION FROM TRAFFIC COUNTS USING CUBE." International Journal of Research in Engineering and Technology 02, no. 13 (2013): 142–48. http://dx.doi.org/10.15623/ijret.2013.0213025.

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43

Tan, Liansheng, and Xiangjun Wang. "A Novel Method to Estimate IP Traffic Matrix." IEEE Communications Letters 11, no. 11 (2007): 907–9. http://dx.doi.org/10.1109/lcomm.2007.071066.

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OHSITA, Y., S. ATA, and M. MURATA. "Identification of Attack Nodes from Traffic Matrix Estimation." IEICE Transactions on Communications E90-B, no. 10 (2007): 2854–64. http://dx.doi.org/10.1093/ietcom/e90-b.10.2854.

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45

Irawati, Indrarini Dyah, Andriyan Bayu Suksmono, and Ian Joseph Matheus Edward. "Internet Traffic Matrix Estimation Based on Compressive Sampling." Advanced Science Letters 23, no. 5 (2017): 3934–38. http://dx.doi.org/10.1166/asl.2017.8283.

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46

Short, Benjamin, and Francis A. Barr. "Membrane Traffic: A Glitch in the Golgi Matrix." Current Biology 13, no. 8 (2003): R311—R313. http://dx.doi.org/10.1016/s0960-9822(03)00234-3.

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47

Lee, Sang Min, Dong Seong Kim, Je Hak Lee, and Jong Sou Park. "Detection of DDoS attacks using optimized traffic matrix." Computers & Mathematics with Applications 63, no. 2 (2012): 501–10. http://dx.doi.org/10.1016/j.camwa.2011.08.020.

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48

Jiang, Dingde, Xingwei Wang, Lei Guo, Haizhuan Ni, and Zhenhua Chen. "Accurate estimation of large-scale IP traffic matrix." AEU - International Journal of Electronics and Communications 65, no. 1 (2011): 75–86. http://dx.doi.org/10.1016/j.aeue.2010.02.008.

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49

Owolabi, A. O. "Trip patterns in Akure, Nigeria: A land-use analytical approach." Journal of Transportation Management 21, no. 3 (2010): 63–71. http://dx.doi.org/10.22237/jotm/1285891560.

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For transportation planners, the use of Origin-Destination (OD) matrix adjustment, is receiving considerable attention. However, there are concerns about the validity of results, primarily related to the number and location of traffic count posts. This leads to the question “What would be the best set of traffic count posts to use in OD matrix adjustment modules?” It has been proved that solving this problem is cumbersome. There have been several attempts (either exact or heuristic approaches) to address this problem. But due to the inherent complexities, there is no efficient and easy-to-use

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50

Bagolee, Saeed Asadi, Mohsen Asadi, and Lorna Richardson. "Identifying traffic count posts for origin-destination matrix adjustments: An approach to actual size networks." Journal of Transportation Management 22, no. 1 (2011): 79–88. http://dx.doi.org/10.22237/jotm/1301616360.

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For transportation planners, the use of Origin-Destination (OD) matrix adjustment, is receiving considerable attention. However, there are concerns about the validity of results, primarily related to the number and location of traffic count posts. This leads to the question “What would be the best set of traffic count posts to use in OD matrix adjustment modules?” It has been proved that solving this problem is cumbersome. There have been several attempts (either exact or heuristic approaches) to address this problem. But due to the inherent complexities, there is no efficient and easy-to-use

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