Traffic generation model
View article: Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction
Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction Open
This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describi…
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Deep Learning for Encrypted Traffic Classification: An Overview Open
Traffic classification has been studied for two decades and applied to a wide\nrange of applications from QoS provisioning and billing in ISPs to\nsecurity-related applications in firewalls and intrusion detection systems.\nPort-based, dat…
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Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey Open
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Trajectory data-based traffic flow studies: A revisit Open
In this paper, we review trajectory data-based traffic flow studies that have been conducted over the last 15 years. Our purpose is to provide a roadmap for readers who have an interest in the latest developments of traffic flow theory tha…
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Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN Open
Network modeling is a critical component for building self-driving Software-Defined Networks, particularly to find optimal routing schemes that meet the goals set by administrators. However, existing modeling techniques do not meet the req…
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Predicting real-time traffic conflicts using deep learning Open
Recently, technologies for predicting traffic conflicts in real-time have been gaining momentum due to their proactive nature of application and the growing implementation of ADAS technology in intelligent vehicles. In ADAS, machine learni…
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IoT Devices Recognition Through Network Traffic Analysis Open
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Traffic engineering in software-defined networking: Measurement and management Open
As the next generation network architecture, software-defined networking (SDN) has exciting application prospects. Its core idea is to separate the forwarding layer and control layer of network system, where network operators can program p…
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A Long Short-Term Memory Recurrent Neural Network Framework for Network Traffic Matrix Prediction Open
Network Traffic Matrix (TM) prediction is defined as the problem of estimating future network traffic from the previous and achieved network traffic data. It is widely used in network planning, resource management and network security. Lon…
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Fine-granularity inference and estimations to network traffic for SDN Open
An end-to-end network traffic matrix is significantly helpful for network management and for Software Defined Networks (SDN). However, the end-to-end network traffic matrix's inferences and estimations are a challenging problem. Moreover, …
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Graph Neural Network for Traffic Forecasting: The Research Progress Open
Traffic forecasting has been regarded as the basis for many intelligent transportation system (ITS) applications, including but not limited to trip planning, road traffic control, and vehicle routing. Various forecasting methods have been …
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Road Network Resilience: How to Identify Critical Links Subject to Day-to-Day Disruptions Open
Disruptive events occur on road networks on a daily basis and affect traffic flow. Resilience analysis aims to assess the consequences of such disruptions by quantifying the ability of a network to absorb and react to adverse events. In th…
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Virtual Network Topology Adaptability Based on Data Analytics for Traffic Prediction Open
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,crea…
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Adaptive encrypted traffic fingerprinting with bi-directional dependence Open
Recently, network traffic analysis has been increasingly used in various applications including security, targeted advertisements, and network management. However, data encryption performed on network traffic poses a challenge to these ana…
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DeepTSP: Deep traffic state prediction model based on large-scale empirical data Open
Real-time traffic state (e.g., speed) prediction is an essential component for traffic control and management in an urban road network. How to build an effective large-scale traffic state prediction system is a challenging but highly valua…
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Spatial-Temporal Cellular Traffic Prediction for 5G and Beyond: A Graph Neural Networks-Based Approach Open
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record
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A Novel Fuzzy-Based Convolutional Neural Network Method to Traffic Flow Prediction With Uncertain Traffic Accident Information Open
As a key part of the method of improving traffic capacity, traffic flow prediction is becoming a research hot-spot of traffic science and intelligent technology, in which the accuracy of traffic flow prediction is particularly concerned. I…
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Two-Stream Multi-Channel Convolutional Neural Network for Multi-Lane Traffic Speed Prediction Considering Traffic Volume Impact Open
Traffic speed prediction is a critically important component of intelligent transportation systems. Recently, with the rapid development of deep learning and transportation data science, a growing body of new traffic speed prediction model…
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Data Traffic Classification in Software Defined Networks (SDN) using supervised-learning Open
Traffic classification with accuracy is of prime importance in network activities such as security monitoring, traffic engineering, fault detection, accounting of network usage, billing and for providing differentiation in Quality of Servi…
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Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks Open
Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely …
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Traffic Flow Forecast Through Time Series Analysis Based on Deep Learning Open
Traffic congestion is a thorny issue to many large and medium-sized cities, posing a serious threat to sustainable urban development. Recently, intelligent traffic system (ITS) has emerged as an effective tool to mitigate urban congestion.…
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Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models Open
Traffic state estimation (TSE) reconstructs the traffic variables (e.g., density or average velocity) on road segments using partially observed data, which is important for traffic managements. Traditional TSE approaches mainly bifurcate i…
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Predicting traffic propagation flow in urban road network with multi-graph convolutional network Open
Traffic volume propagating from upstream road link to downstream road link is the key parameter for designing intersection signal timing scheme. Recent works successfully used graph convolutional network (GCN) and specific time-series mode…
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Network Traffic Prediction Model Considering Road Traffic Parameters Using Artificial Intelligence Methods in VANET Open
Vehicular Ad hoc Networks (VANETs) are established on vehicles that are intelligent and can have Vehicle-to-Vehicle (V2V) and Vehicle-to-Road Side Units (V2R) communications. In this paper, we propose a model for predicting network traffic…
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Encrypted Network Traffic Classification Using Deep and Parallel Network-in-Network Models Open
Network traffic classification aims to recognize different application or traffic types by analyzing received data packets. This paper presents a neural network model with deep and parallel network-in-network (NIN) structures for classifyi…
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A Meta-Learning Scheme for Adaptive Short-Term Network Traffic Prediction Open
Network traffic prediction is a fundamental prerequisite for dynamic resource provisioning in wireline and wireless networks, but is known to be challenging due to non-stationarity and due to its burstiness and self-similar nature. The pre…
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A Multiple Regression Approach for Traffic Flow Estimation Open
Traffic flow information is of great importance for transport planning and related research. The conventional methods of automated data collection, such as annual average daily traffic (AADT) data, are often restricted by limited installat…
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Analysis of Urban Road Traffic Network Based on Complex Network Open
Urban road traffic is a typical network. The analysis and study of the topology structure is the basis of the traffic state evaluation and the traffic organization optimization. This paper redefines the urban road traffic weighted network …
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Video based Traffic Forecasting using Convolution Neural Network Model and Transfer Learning Techniques Open
The ideas, algorithms and models developed for application in one particular domain can be applied for solving similar issues in a different domain using the modern concept termed as transfer learning. The connection between spatiotemporal…
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Road2Vec: Measuring Traffic Interactions in Urban Road System from Massive Travel Routes Open
Good characterization of traffic interactions among urban roads can facilitate traffic-related applications, such as traffic control and short-term forecasting. Most studies measure the traffic interaction between two roads by their topolo…