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Journal articles on the topic 'Traffic matrix'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 June 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 (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

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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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 (September 4, 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 (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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8

Chokshi, Rajvi. "Traffic Controlling System." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (October 31, 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 analyze real-time traffic and take appropriate action. This is achieved by the application of an image processing technique, that would capture the real-time pictures of the paths to compare with the reference image of the path. The evaluation matrix is created to decide the amount of time each light must be on. In addition, an evaluation matrix is created. The purpose of the evaluation matrix is to determine how long each light must be turned on. The MATLAB 7.8 was used to perform the study.

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9

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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10

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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11

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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12

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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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 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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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 efficient decisions. In traffic prediction, the mining of spatial features is an important step and graph-based methods are effective methods. While most existing graph-based methods construct a static adjacent matrix, these methods are difficult to respond to spatio-temporal changes in time. In this paper, we propose a deep learning model for lane-level traffic prediction. Specifically, we take advantage of the graph convolutional network (GCN) with a data-driven adjacent matrix for spatial feature modeling and treat different lanes of the same road segment as different nodes. The data-driven adjacent matrix consists of the fundamental distance-based adjacent matrix and the dynamic lane correlation matrix. The temporal features are extracted with the gated recurrent unit (GRU). Then, we adaptively fuse spatial and temporal features with the gating mechanism to get the final spatio-temporal features for lane-level traffic prediction. Extensive experiments on a real-world dataset validate the effectiveness of our model.

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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 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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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 (December 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 (April 1, 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 (June 26, 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 between these clues regarding spatiotemporal information is not yet fully explored. To this end, we propose a random matrix theory-based ROI identification (RRI) approach. By observing the intensification or diminution of network characteristic differences, i.e., divergence, between adjacent single regions, the ROIs can be identified. Firstly, we leverage the spatiotemporal information of area network traffic data with a spike model which can be described as a zero mean random matrix with a deterministic perturbation matrix. Then, we put forward an average divergence capacity model for ROI identification by estimating the divergent degree of adjacent regions. Case studies on three real-world network traffic datasets demonstrate the effectiveness of our proposed RRI method. The ROI identification greatly improves the network traffic prediction performance, yielding a decrease of root mean square error and mean absolute error by 36.87 % and 52.26 % , respectively.

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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 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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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. 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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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 (April 29, 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 feature extraction is performed using Discrete Wavelet Transform (DWT), and a sparse matrix is constructed. A Genetic Algorithm (GA) is used to optimize the sparse matrix, which effectively selects the most significant features for prediction. The optimized sparse matrix is fed into the BiLSTM model for accurate traffic forecasting. Experimental results show that the proposed method significantly reduces Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) while achieving higher accuracy (ACC) compared to traditional neural networks. The results demonstrate that the proposed sparse matrix, integrated with BiLSTM, provides superior prediction accuracy and better generalization, making it a robust solution for network traffic forecasting in Beyond 5G networks.

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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 (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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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 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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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 (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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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 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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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 (November 29, 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, including traffic prediction and anomaly analysis in real battlefield network environments.

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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 (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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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 (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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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 (October 17, 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 (June 21, 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 residual neural networks—a very deep set of neural networks—to the problem for the first time. The suggested architecture reliably predicts the whole network’s O–D flows utilizing link flows, hence inverting the standard traffic assignment problem. It deduces a relevant correlation between traffic flow statistics and network topology from traffic flow characteristics. To train the proposed deep learning architecture, random synthetic flow data was generated from the historical demand data of the network. A large-scale network was used to test and confirm the model’s performance. Then, the Sioux Falls network was used to compare the results with the literature. The robustness of applying the proposed framework to this particular combined traffic flow problem was determined by maintaining superior prediction accuracy over the literature with a moderate number of traffic sensors.

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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 proposed to measure the correlation between subareas. Then, the matrix of regional traffic speed data is constructed. Thirdly, the matrix is input into the GRU prediction model to get the predicted traffic speed. The proposed algorithm’s prediction performance is verified based on the GPS data collected from the adjacent region between Beijing Highways and Expressway.

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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 that the proposed method outperform in network traffic classification and improve the classification accuracy.

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Saeed, Hayatullah, and Mohammad Azim Nazari. "Applications of Matrix Multiplication." Journal for Research in Applied Sciences and Biotechnology 3, no. 3 (June 2, 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 well describe this beautiful issue. By matrix multiplication, we can encode and decode messages. To encode a message, we choose an invertible matrix and multiply the uncoded row matrices (on the right) by to obtain coded row matrices, this idea will be clarify by some useful examples. Also we used to study certain relationships between objects by matrix multiplication. We will clarify all of these applications by useful examples. In this paper, we present six different applications of matrix multiplication.

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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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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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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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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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., 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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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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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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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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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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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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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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Owolabi, A. O. "Trip patterns in Akure, Nigeria: A land-use analytical approach." Journal of Transportation Management 21, no. 3 (October 1, 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 methodology able to address situations on the scale of actual cases. This study demonstrates a simple way of identifying traffic count posts tailored to deal w ith real-size cases. The proposed methodology is based on a maximum matrix coverage criterion. Using a limited number of incremental trials, a set of links whose traffic flows give maximum coverage of the demand and maximum fitness to the corresponding traffic count rates are identified as traffic count posts. The results show that more traffic count posts do not necessarily yield a better result. This article reports on a project conducted for the public works ministry of the UAE city of Sharjah.

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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 (April 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 methodology able to address situations on the scale of actual cases. This study demonstrates a simple way of identifying traffic count posts tailored to deal w ith real-size cases. The proposed methodology is based on a maximum matrix coverage criterion. Using a limited number of incremental trials, a set of links whose traffic flows give maximum coverage of the demand and maximum fitness to the corresponding traffic count rates are identified as traffic count posts. The results show that more traffic count posts do not necessarily yield a better result. This article reports on a project conducted for the public works ministry of the UAE city of Sharjah.

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