This paper aims to investigate the impact of occasional traffic crashes on the urban traffic network flow. Toward this purpose, an extended model of coupled Nagel–Schreckenberg (NaSch) and Biham–Middleton–Levine (BML) models is pres...
With the increasing penetration rate of electric vehicles, the spatiotemporal coupling relationship between the power distribution network and traffic network is stronger than ever before. Under the dynamic wireless charging mode, traffic jam chargin...
Network traffic prediction is an important tool for the management and control of IoT, and timely and accurate traffic prediction models play a crucial role in improving the IoT service quality. The degree of burstiness in intelligent network traffic...
Network slicing is a promising technique used in the smart delivery of traffic and can satisfy the requirements of specific applications or systems based on the features of the 5G network. To this end, an appropriate slice needs to be selected for ea...
Recently, a multi-agent based network automation architecture has been proposed. The architecture is named multi-agent based network automation of the network management system (MANA-NMS). The architectural framework introduced atomized network funct...
Traffic congestion is becoming a critical problem in urban traffic planning. Intelligent transportation systems can help expand the capacity of urban roads to alleviate traffic congestion. As a key concept in intelligent transportation systems, urban...
The industrial network infrastructures are transforming to a horizontal architecture to enable data availability for advanced applications and enhance flexibility for integrating new technologies. The uninterrupted operation of the legacy systems nee...
Autonomous driving technology will bring revolutionary changes to the development of future cities and transportation. In order to study the impact of autonomous driving on urban transportation networks, this paper first summarizes the development st...
To scientifically evaluate the reliability of air traffic networks, a definition of air traffic network reliability is proposed in this paper. Calculation models of the connectivity reliability, travel-time reliability, and capacity reliability of th...
Traffic replay can effectively improve the fidelity of network emulation and can support emerging network verification and network security evaluation. A distributed traffic replay framework for network emulation, named DTRF, is designed for large-sc...
With the recent development of traffic networks, traffic detector layout has become very complicated, due to the complexity of traffic network structures and states. Thus, this paper presents an optimal method for traffic detector layout based on net...
This manuscript investigates the network traffic prediction problem, with the aim of predicting network traffic on a network function virtualization (NFV)-enabled and digital twin (DT)-assisted physical network for network service providers and netwo...
To evaluate the characteristics of traffic noise under different network structures, an evaluation method is established to clarify the mechanism of traffic noise impacted by variational network structures. First, a stochastic user equilibrium (SUE)...
Artificial intelligence-assisted security is an important field of research in relation to information security. One of the most important tasks is to distinguish between normal and abnormal network traffic (such as malicious or sudden traffic). Traf...
Wireless sensor networks (WSNs) are gaining traction in the realm of network communication, renowned for their adaptability, configuration, and flexibility. The forthcoming network traffic within WSNs can be forecasted through temporal sequence model...
15 February 2023
Prediction of traffic violations plays a key role in transportation safety. Combining with deep learning to predict traffic violations has become a new development trend. However, existing methods are based on regular spatial grids which leads to a f...
More than 75% of Internet traffic is now encrypted, and this percentage is constantly increasing. The majority of communications are secured using common encryption protocols such as SSL/TLS and IPsec to ensure security and protect the privacy of Int...
Encryption is a fundamental security measure to safeguard data during transmission to ensure confidentiality while at the same time posing a great challenge for traditional packet and traffic inspection. In response to the proliferation of diverse ne...
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 proposed in...
The accompanying increased traffic due to the rapid development of tourism at present calls for the better sustainability and equilibrium of tourism and traffic resources in the future. In recent decades, considerable attention has been paid to highw...
Network traffic flow prediction model is fundamental to the network performance evaluation and the design of network control scheme which is crucial for the success of high-speed networks. Aiming at shortcoming of the conventional network traffic tim...
Network traffic analysis is crucial for understanding network behavior and identifying underlying applications, protocols, and service groups. The increasing complexity of network environments, driven by the evolution of the Internet, poses significa...
A traffic light recognition system is a very important building block in an advanced driving assistance system and an autonomous vehicle system. In this paper, we propose a two-staged deep-learning-based traffic light recognition method that consists...
Maritime traffic can reflect the diverse and complex relations between countries and regions, such as economic trade and geopolitics. Based on the AIS (Automatic Identification System) trajectory data of ships, this study constructs the Maritime Silk...
The transport of information packets in complex networks is a prototype system for the study of traffic jamming, a nonlinear dynamic phenomenon that arises with increased traffic load and limited network capacity. The underlying mathematical framewor...
This research addresses multiple challenges faced by ship networks, including limited bandwidth, unstable network connections, high latency, and command priority. To solve these problems, we used reinforcement learning-based methods to simulate traff...
An urban public traffic network is a typical high-order complex network. There are multiple types of transportation in an urban public traffic network, and each type has different impacts on urban transportation. Robustness analyses of urban public t...
Network anomaly detection faces unique challenges from dynamic traffic, including large data volume, few attributes, and human factors that influence it, making it difficult to identify typical behavioral characteristics. To address this, we propose...
The current airspace route system consists mainly of pre-defined routes with a low number of intersections to facilitate air traffic controllers to oversee the traffic. Our aim is a method to create an artificial and reliable route network based on p...
With the continuous development of information technology, a spontaneous interdependent network has formed within the air traffic control network. Due to the internal interdependence, any small, failed node may trigger a cascade failure of the entire...
IoT Android application is the most common implementation system in the mobile ecosystem. As assaults have increased over time, malware attacks will likely happen on 5G mobile IoT Android applications. The huge threat posed by malware to communicatio...
Telecommuting and telelearning have gradually become mainstream lifestyles in the post-epidemic era. The extensive interconnection of massive terminals gives attackers more opportunities, which brings more significant challenges to network traffic se...
With continuous economic development, most urban road networks are facing unprecedented traffic congestion. Centralized traffic control is difficult to achieve, and distributed traffic control based on partitioning a road network into subnetworks is...
The enormous growth of services and data transmitted over the internet, the bloodstream of modern civilization, has caused a remarkable increase in cyber attack threats. This fact has forced the development of methods of preventing attacks. Among the...
Anomaly detection systems can accurately identify malicious network traffic, providing network security. With the development of internet technology, network attacks are becoming more and more sourced and complicated, making it difficult for traditio...
Classifying mobile applications from encrypted network traffic is a common and basic requirement in network security and network management. Existing works classify mobile applications from flows, based on which application fingerprints and classifie...
Due to the inherent complexity of urban road networks and the irregular periodic fluctuations of traffic flow, traffic forecasting remains a challenging spatiotemporal modeling task.Existing studies predominantly focus on capturing spatial dependenci...
Road traffic accidents are a significant public health issue, accounting for almost 1.3 million deaths worldwide annually, with millions more experiencing non-fatal injuries. A variety of subjective and objective factors contribute to the occurrence...
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 describing the time...
Intelligent ships are the future direction of maritime transportation. Route design and route planning of intelligent ships require high-precision, real-time maritime traffic network information, which changes dynamically as the traffic environment c...
Traffic flow prediction provides support for travel management, vehicle scheduling, and intelligent transportation system construction. In this work, a graph space–time network (GSTNCNI), incorporating complex network feature information, is pr...
For telecom operators, it is of great significance to employ artificial intelligence (AI) and big data technology in a software-defined network (SDN) in order to achieve intelligent network control, traffic management and optimization. This paper pro...
Logging network traffic offers valuable insights into data flow, enabling the proactive analysis and troubleshooting of issues as they arise. Moreover, it provides a means to access and examine the exchanged information among network users that would...
The traffic data of the intelligent network with the Space-Integrated Ground Information Network have the characteristics of robust traffic burst, large traffic data, and high volatility compared with the ground traffic. We propose a Gated Temporal C...
The paper introduces an artificial neural network ensemble for decentralized control of traffic signals based on data from sensor network. According to the decentralized approach, traffic signals at each intersection are controlled independently usin...
Accurate traffic prediction is crucial to the construction of intelligent transportation systems. This task remains challenging because of the complicated and dynamic spatiotemporal dependency in traffic networks. While various graph-based spatiotemp...
Traffic sign detection is a research hotspot in advanced assisted driving systems, given the complex background, light transformation, and scale changes of traffic sign targets, as well as the problems of slow result acquisition and low accuracy of e...
It seems to be a truism to say that we should pay more and more attention to network traffic safety. Such a goal may be achieved with many different approaches. In this paper, we put our attention on the increase in network traffic safety based on th...
The wide application of software-defined network (SDN) architecture, combined with its centralized control characteristics, have exacerbated the potential risk of network attacks, and the traditional anomaly traffic detection methods are facing the c...
We present a traffic light detection and recognition approach for traffic lights that utilizes convolutional neural networks. We also introduce a technique for identifying arrow signal lights in multiple urban traffic environments. For detection, we...
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