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Journal articles on the topic 'Traffic matrix'

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Published: 4 June 2021

Last updated: 2 February 2022

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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 (October 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 (January 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 (September 22, 2015): 579–92. http://dx.doi.org/10.1145/2829988.2787471.

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4

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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5

Tune, Paul, and Matthew Roughan. "Maximum entropy traffic matrix synthesis." ACM SIGMETRICS Performance Evaluation Review 42, no. 2 (September 4, 2014): 43–45. http://dx.doi.org/10.1145/2667522.2667536.

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6

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 the traffic matrix and delay matrix this paper quantifies overlay network congestion, which reflect the current network security situation. The experimental results show the restoration effect of traffic matrix and delay matrix is well and the congestion degree reflects the actual network state.

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7

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

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8

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

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9

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

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10

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

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11

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 model assumption are very small. For a large-scale network, the traffic matrix estimation methods also perform well. The comparison of two minimization methods shown in the simulation results complies with the analysis.

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12

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 (November 15, 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 constraints, the relationship between the start/end-time of green interval and the minimum/maximum green intervals; Third, the bandwidths of the two directions of the artery ware produced, after being weighted by their traffic demands. The intuitiveness, convenience, and feasibility of the OD band model were fully demonstrated through a case study. Overall, the OD band model helps to produce bi-directional progression bands for traffic with many turning movements on the artery, and enables the through and turning-in/out traffics to proceed through continuous intersections, when the signals at those intersections are green.

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13

SHIMIZU, S. "Traffic Matrix Estimation Using Spike Flow Detection." IEICE Transactions on Communications E88-B, no. 4 (April 1, 2005): 1484–92. http://dx.doi.org/10.1093/ietcom/e88-b.4.1484.

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14

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 (December 2018): 1435–45. http://dx.doi.org/10.1109/tnsm.2018.2867998.

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15

Zhao, Qi, Zihui Ge, Jia Wang, and Jun Xu. "Robust traffic matrix estimation with imperfect information." ACM SIGMETRICS Performance Evaluation Review 34, no. 1 (June 26, 2006): 133–44. http://dx.doi.org/10.1145/1140103.1140294.

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16

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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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

Shang, Fengjun. "An estimating method for IP traffic matrix based on generalized inverse matrix." International Journal of Intelligent Computing and Cybernetics 1, no. 4 (October 17, 2008): 521–36. http://dx.doi.org/10.1108/17563780810919104.

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19

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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20

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 the repair of missing traffic flow data. Combined with the measured data of expressway traffic flow, the experimental results show that, under different missing data types and missing rates, the proposed method outperforms the methods lacking auxiliary traffic flow data and achieves a good repair effect, especially for high miss data rates.

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21

ZHANG, Ke. "Traffic matrix estimation based on generalized linear inversion." Journal of Computer Applications 28, no. 3 (March 20, 2008): 582–85. http://dx.doi.org/10.3724/sp.j.1087.2008.00582.

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22

., Subha Nair. "TRIP MATRIX ESTIMATION FROM TRAFFIC COUNTS USING CUBE." International Journal of Research in Engineering and Technology 02, no. 13 (November 25, 2013): 142–48. http://dx.doi.org/10.15623/ijret.2013.0213025.

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23

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 (March 2016): 31–40. http://dx.doi.org/10.5862/jcstcs.236.4.

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24

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

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25

Qian, Ye-kui, and Ming Chen. "On the Manifold Structure of Internet Traffic Matrix." Journal of Electronics & Information Technology 32, no. 12 (January 24, 2011): 2981–86. http://dx.doi.org/10.3724/sp.j.1146.2010.00130.

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26

OHSITA, Y., S. ATA, and M. MURATA. "Identification of Attack Nodes from Traffic Matrix Estimation." IEICE Transactions on Communications E90-B, no. 10 (October 1, 2007): 2854–64. http://dx.doi.org/10.1093/ietcom/e90-b.10.2854.

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27

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 (May 1, 2017): 3934–38. http://dx.doi.org/10.1166/asl.2017.8283.

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28

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

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29

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 (January 2012): 501–10. http://dx.doi.org/10.1016/j.camwa.2011.08.020.

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30

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 (January 2011): 75–86. http://dx.doi.org/10.1016/j.aeue.2010.02.008.

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31

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 matrix estimator. To enable timely learning with a low complexity, we creatively incorporate tensor decomposition into the deep learning framework, where real-time traffic data are decomposed into a global component that is stable and depends on long-term temporal-spatial traffic relationship and a local component that captures the traffic fluctuations. We propose a novel design to estimate the dynamic Laplacian matrix of the graph with above two components based on our theoretical derivation, and introduce our design basis. The forecasting performance is evaluated with two realtime traffic datasets. Experiment results demonstrate that our network can achieve up to 25% accuracy improvement.

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32

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 (February 9, 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 optimization method to further the solutions of the Gravity model. The Gravity model is applied as an initial solution and then GA model is applied taking the link load-TM relationship as a objective function. Results shows improvement over Gravity model.

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33

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 the impact of sensor data with data missing and the spatiotemporal correlation characteristics of traffic flow on imputation results. In this paper, a traffic data imputation method is proposed based on improved low-rank matrix decomposition (ILRMD), which fully considers the influence of missing data and effectively utilizes the spatiotemporal correlation characteristics among traffic data. The proposed method uses not only the traffic data around the sensor including missing data, but also the sensor data with data missing. The information of missing data is reflected into the coefficient matrix, and the spatiotemporal correlation characteristics are applied in order to obtain more accurate imputation results. The real traffic data collected from the Caltrans Performance Measurement System (PeMS) are used to evaluate the imputation performance of the proposed method. Experiment results show that the average imputation accuracy with proposed method can be improved 87.07% compared with the SVR, ARIMA, KNN, DBN-SVR, WNN, and traditional MC methods, and it is an effective method for data imputation.

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34

YOSHII, Toshio, Masao KUWAHARA, Hirokazu AKAHANE, and Ryota HORIGUCHI. "Estimation of a Time Dependent OD Matrix from Traffic Counts Using Dynamic Traffic Simulation." INFRASTRUCTURE PLANNING REVIEW 15 (1998): 461–68. http://dx.doi.org/10.2208/journalip.15.461.

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35

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 (February 9, 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 dynamic with its unique stationary distribution of the vehicles on the road network. In this paper, a new parametrization is presented for this model by introducing the concept of two-dimensional stationary distribution which can handle the traffic’s dynamic together with the vehicles’ distribution. In addition, the weighted least squares estimation method is applied for estimating this new parameter matrix using trajectory data. In a case study, we apply our method on the Taxi Trajectory Prediction dataset and road network data from the OpenStreetMap project, both available publicly. To test our approach, we have implemented the proposed model in software. We have run simulations in medium and large scales and both the model and estimation procedure, based on artificial and real datasets, have been proved satisfactory and superior to the frequency based maximum likelihood method. In a real application, we have unfolded a stationary distribution on the map graph of Porto, based on the dataset. The approach described here combines techniques which, when used together to analyze traffic on large road networks, has not previously been reported.

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36

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. The results show that traffic organization scheme obtained by this method can greatly reduce the traffic influence on the surrounding road network during construction.

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37

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 (March 19, 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 method of successive averages algorithm to solve the network. The components of the network weight matrices are a function not simply of adjacency, but of network topology, network structure, and demand configuration. We test and compare the network weight matrices in different traffic conditions using the Nguyen–Dupuis network. The results lead to a conclusion that the network weight matrices operate better than traditional spatial weight matrices. Comparing the unweighted and flow-weighted network weight matrices, we also reveal that the assigned flow network weight matrices perform two times better than a betweenness network weight matrix, particularly in congested traffic conditions.

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38

Meng, Fan Bo, Hong Hao Zhao, Qing Qi Zhao, Wei Zhe Ma, Zhi Gang Qi, and Jin You Su. "A Traffic Matrix Recovery Algorithm via Low-Dimension Nature in Smart Grid." Applied Mechanics and Materials 392 (September 2013): 593–97. http://dx.doi.org/10.4028/www.scientific.net/amm.392.593.

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In this literature, we explore the solution of network traffic recovery in smart grid. Taking account of dimensionality of network traffic in smart grid, we propose a novel reconstruction model via network tomography. In our algorithm, we use the low-dimension nature of traffic matrix and the greedy adaptive dictionary algorithm to convert the network tomography into the problem of sparse reconstruction at first. Then we solve network traffic by an iterative greedy algorithm. Simulation results indicate that proposed algorithm exhibits noticeably improvement in estimation error comparing with previous work.

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39

Li, Zhuo Yue. "Applied Research of the Relational Matrix Analysis in the Road Traffic Safety Measures Decisions." Advanced Materials Research 919-921 (April 2014): 1091–95. http://dx.doi.org/10.4028/www.scientific.net/amr.919-921.1091.

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The road traffic safety measures decisions plays an increasingly prominent role not only in the road traffic management but also in the development of social economy. Combining the Changhong Road, Xiangyang, Hubei Province road traffic situation, the paper makes the road traffic safety measures decisions by using relational matrix analysis(RMA) and through the expected effect of the application of the RMA, this paper pointes out the practice significance of the application of RMA to the road traffic safety measures decisions.

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40

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 non-linear character and can’t be presented in explicit form. This complicates the research of common properties of correspondence matrix and forecasting its change at modification of transport network, shift in transport behaviour of passengers (it affects the gravity model parameters) and at random fluctuations of number of incoming and departing passengers for network vertices. At the investigation of the mentioned dependence scientists use various approaches on the basis of both analytical apparatus and approximate methods. The paper presents classic simulation modeling for the analysis of the correspondence matrix and the volume of passenger traffic for the linear transport network calculated on its basis. The use of the proposed approach allowed determining that at random distribution of volumes of incoming and departing passengers the passenger traffic is also a random value distributed according to the normal law. Moreover, the authors gained the dependence between the passenger traffic and the parameter of gravity model connected with the average trip length. Besides, the authors studied the dependence of passenger traffic from the network scale.

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41

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 (October 8, 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 within a given spatial-temporal area) and the traffic-state matrices generated from the other neural network. The other LSTM NN, called a generative network, aims to generate traffic-state matrices which maximize the probability that the discriminative network assigns true labels to them. The two LSTM NNs are trained simultaneously such that the trained generative network can generate traffic matrices similar to those in the training data set. Given a traffic-state matrix with missing values, we use back-propagation on three defined loss functions to map the corrupted matrix to a latent space. The mapping vector is then passed through the pre-trained generative network to estimate the missing values of the corrupted matrix. The proposed GAA is compared with the existing Bayesian network approach on loop detector data collected from Seattle, Washington and that collected from San Diego, California. Experimental results indicate that the GAA can achieve higher accuracy in traffic-state estimation than the Bayesian network approach.

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42

KumarBaruah, Arun, and Niky Baruah. "Clique Matrix of a Graph in Traffic Control Problems." International Journal of Computer Applications 53, no. 6 (September 25, 2012): 41–45. http://dx.doi.org/10.5120/8427-2194.

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43

WEI, Duo, and Guanghong LYU. "IP traffic matrix estimation based on ant colony optimization." Journal of Computer Applications 33, no. 1 (September 22, 2013): 92–95. http://dx.doi.org/10.3724/sp.j.1087.2013.00092.

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44

Zhao, Jianlong, Hua Qu, Jihong Zhao, and Dingchao Jiang. "Towards traffic matrix prediction with LSTM recurrent neural networks." Electronics Letters 54, no. 9 (May 2018): 566–68. http://dx.doi.org/10.1049/el.2018.0336.

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45

Shi, Ronghua, Mengjie Yang, Ying Zhao, Fangfang Zhou, Wei Huang, and Sheng Zhang. "A Matrix-Based Visualization System for Network Traffic Forensics." IEEE Systems Journal 10, no. 4 (December 2016): 1350–60. http://dx.doi.org/10.1109/jsyst.2014.2358997.

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46

Yang, Hai, and Jing Zhou. "Optimal traffic counting locations for origin–destination matrix estimation." Transportation Research Part B: Methodological 32, no. 2 (February 1998): 109–26. http://dx.doi.org/10.1016/s0191-2615(97)00016-7.

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47

Jiang, Dingde, Laisen Nie, Zhihan Lv, and Houbing Song. "Spatio-Temporal Kronecker Compressive Sensing for Traffic Matrix Recovery." IEEE Access 4 (2016): 3046–53. http://dx.doi.org/10.1109/access.2016.2573264.

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48

Jiang, Dingde, and Guangmin Hu. "GARCH model-based large-scale IP traffic matrix estimation." IEEE Communications Letters 13, no. 1 (January 2009): 52–54. http://dx.doi.org/10.1109/lcomm.2008.081271.

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49

Castillo, Enrique, Inmaculada Gallego, Santos Sanchez-Cambronero, and Ana Rivas. "Matrix Tools for General Observability Analysis in Traffic Networks." IEEE Transactions on Intelligent Transportation Systems 11, no. 4 (December 2010): 799–813. http://dx.doi.org/10.1109/tits.2010.2050768.

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50

Queiroz, Wander J., Miriam A. M. Capretz, and Mario A. R. Dantas. "A MapReduce Approach for Traffic Matrix Estimation in SDN." IEEE Access 8 (2020): 149065–76. http://dx.doi.org/10.1109/access.2020.3016249.

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