• Wind and Structures
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• v.30 no.4
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• pp.325-338
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• 2020

In this paper, the improvement of the anti-snow performance of a high-speed train (HST) is studied using the unsteady Reynolds-Averaged Navier-Stokes simulations (URANS) coupled with the Discrete Phase Model (DPM). The influences of the proposed flow control scheme on the velocity distribution of the airflow and snow particles, snow concentration level and accumulated mass in the bogie cavities are analyzed. The results show that the front anti-snow structures can effectively deflect downward the airflow and snow particles at the entrance of the cavities and alleviate the strong impact on the bogie bottom, thereby decrease the local accumulated snow. The rotational rear plates with the deflecting angle of 45° are found to present well deflecting effect on the particles' trajectories and force more snow to flow out of the cavities, and thus significantly reduce the accretion distribution on the bogie top. Furthermore, running speeds of HST are shown to have a great effect on the snow-resistance capability of the flow control scheme. The proposed flow control scheme achieves more snow reduction for HST at higher train's running speed in the cold regions.

• International Journal of Automotive Technology
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• v.7 no.1
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• pp.25-34
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• 2006

For decades, simulation technique has been well validated in areas such as computer and communication systems. Recently, the technique has been much used in the area of transportation and traffic forecasting. Several methods have been proposed for investigating complex traffic flows. However, the dynamics of vehicles and diversities of driver characteristics have never been considered sufficiently in these methods, although they are considered important factors in traffic flow analysis. In this paper, we propose a traffic simulation tool called Multi-Agent for Traffic Simulation with Vehicle Dynamics Model (MATDYMO). Road transport consultants, traffic engineers and urban traffic control center managers are expected to use MATDYMO to efficiently simulate traffic flow. MATDYMO has four sub systems: the road management system, the vehicle motion control system, the driver management system, and the integration control system. The road management system simulates traffic flow for various traffic environments (e.g., multi-lane roads, nodes, virtual lanes, and signals); the vehicle motion control system constructs the vehicle agent by using various vehicle dynamic models; the driver management system constructs the driver agent capable of having different driving styles; and lastly, the integrated control system regulates the MATDYMO as a whole and observes the agents running in the system. The vehicle motion control system and driver management system are described in the companion paper. An interrupted and uninterrupted flow model were simulated, and the simulation results were verified by comparing them with the results from a commercial software, TRANSYT-7F. The simulation result of the uninterrupted flow model showed that the driver agent displayed human-like behavior ranging from slow and careful driving to fast and aggressive driving. The simulation of the interrupted flow model was implemented as two cases. The first case analyzed traffic flow as the traffic signals changed at different intervals and as the turning traffic volume changed. Second case analyzed the traffic flow as the traffic signals changed at different intervals and as the road length changed. The simulation results of the interrupted flow model showed that the close relationship between traffic state change and traffic signal interval.

• Journal of Applied Reliability
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• v.18 no.2
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• pp.153-160
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• 2018

Purpose: A traffic signal controller needs to control and coordinate to ensure that traffic and pedestrians move as smoothly as possible. Since a traffic signal controller has a significant impact on the safety of vehicles and pedestrians, it is important to monitor the failure and deterioration of the traffic signal controller. The purpose of this paper is to propose an IoT (Internet of Things)-based monitoring system for a traffic signal controller. Methods: Every traffic signal controller has a nominal system trajectory specified when it is deployed. The proposed IoT-based monitoring system collects the system trajectory information through real-time monitoring. By comparing the nominal system trajectory and the monitored system trajectory, we are able to detect the failure and deterioration of the traffic signal controller. Conclusion: The proposed IoT-based monitoring system can contribute to the safety of vehicles and pedestrians by maximizing the availability of a traffic signal controller.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4246-4267
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• 2020

As the network goes deep into all aspects of people's lives, the number and the complexity of network traffic is increasing, and traffic classification becomes more and more important. How to classify them effectively is an important prerequisite for network management and planning, and ensuring network security. With the continuous development of deep learning, more and more traffic classification begins to use it as the main method, which achieves better results than traditional classification methods. In this paper, we provide a comprehensive review of network traffic classification based on deep learning. Firstly, we introduce the research background and progress of network traffic classification. Then, we summarize and compare traffic classification based on deep learning such as stack autoencoder, one-dimensional convolution neural network, two-dimensional convolution neural network, three-dimensional convolution neural network, long short-term memory network and Deep Belief Networks. In addition, we compare traffic classification based on deep learning with other methods such as based on port number, deep packets detection and machine learning. Finally, the future research directions of network traffic classification based on deep learning are prospected.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.95-100
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• 2023

Due to Traffic accidents people faces health and economical casualties around the world. As the population increases vehicles on road increase which leads to congestion in cities. Congestion can lead to increasing accident risks due to the expansion in transportation systems. Modern cities are adopting various technologies to minimize traffic accidents by predicting mathematically. Traffic accidents cause economical casualties and potential death. Therefore, to ensure people's safety, the concept of the smart city makes sense. In a smart city, traffic accident factors like road condition, light condition, weather condition etcetera are important to consider to predict traffic accident severity. Several machine learning models can significantly be employed to determine and predict traffic accident severity. This research paper illustrated the performance of a hybridized neural network and compared it with other machine learning models in order to measure the accuracy of predicting traffic accident severity. Dataset of city Leeds, UK is being used to train and test the model. Then the results are being compared with each other. Particle Swarm optimization with artificial neural network (PSO-ANN) gave promising results compared to other machine learning models like Random Forest, Naïve Bayes, Nearest Centroid, K Nearest Neighbor Classification. PSO- ANN model can be adopted in the transportation system to counter traffic accident issues. The nearest centroid model gave the lowest accuracy score whereas PSO-ANN gave the highest accuracy score. All the test results and findings obtained in our study can provide valuable information on reducing traffic accidents.

• Journal of Communications and Networks
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• v.7 no.1
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• pp.54-64
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• 2005

This paper presents an advanced architecture for a traffic generator capable of producing ATM traffic streams according to fully general semi-Markovian stochastic models. The architecture employs a basic traffic generator platform and enhances it by adding facilities for 'driving' the cell generation process through high-level specifications. Several kinds of optimization are employed for enhancing the software's speed to match the hardware's potential and for ensuring that traffic streams corresponding to models with a wide range of parameters can be generated efficiently and reliably. The proposed traffic generation procedure is highly modular. Thus, although this paper deals with ATM traffic, the main elements of the architecture can be used equally well for generating traffic loads on other networking technologies, IP-based networks being a notable example.

• ETRI Journal
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• v.36 no.1
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• pp.69-79
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• 2014

The scheduling scheme in packet switching networks is one of the most critical features that can affect the performance of the network. Hence, many scheduling algorithms have been suggested and some indices, such as fairness and latency, have been proposed for the comparison of their performances. While the nature of Internet traffic is bursty, traditional scheduling algorithms try to smooth the traffic and serve the users based on this smoothed traffic. As a result, the fairness index mainly considers this smoothed traffic and the service rate as the main parameter to differentiate among different sessions or flows. This work uses burstiness as a differentiating factor to evaluate scheduling algorithms proposed in the literature. To achieve this goal, a new index that evaluates the performance of a scheduler with bursty traffic is introduced. Additionally, this paper introduces a new scheduler that not only uses arrival rates but also considers burstiness parameters in its scheduling algorithms.

• Journal of the military operations research society of Korea
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• v.30 no.1
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• pp.107-134
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• 2004

Today's Internet is providing a single service which is so-called "best-effort service". Now, multimedia and real-time applications are not only demanding large bandwidth but also requiring high QoS. For this, MPLS and DiffServ technology can be adopted to support more scalability and QoS for data traffic engineering. The DISN(Defense Information Systems Network) supports CBR service for voice traffic and VBR service for data traffic which is best-effort service. We propose how to adopt MPLS and DiffServ technology to support traffic engineering and guarantee QoS in the DISN. A traffic analysis according to prioritized traffic classes is done using OPNET simulation tool for assuring QoS. The result shows that low priority packets are delayed a little bit, but high priority packets are transferred more efficiently than without traffic engineering.gineering.

• Journal of Information Processing Systems
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• v.10 no.1
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• pp.119-131
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• 2014

In recent literature on traffic scheduling, the combination of the two-dimensional discrete-time Markov chain (DTMC) and the Markov modulated Poisson process (MMPP) is used to analyze the capacity of VoIP traffic in the cognitive radio system. The performance of the cognitive radio system solely depends on the accuracy of spectrum sensing techniques, the minimization of false alarms, and the scheduling of traffic channels. In this paper, we only emphasize the scheduling of traffic channels (i.e., traffic handling techniques for the primary user [PU] and the secondary user [SU]). We consider the following three different traffic models: the cross-layer analytical model, M/G/1(m) traffic, and the IEEE 802.16e/m scheduling approach to evaluate the performance of the VoIP services of the cognitive radio system from the context of blocking probability and throughput.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.273-279
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• 2022

Owing to the need to establish a cooperative-intelligent transport system (C-ITS) environment in the transportation sector locally and abroad, various research and development efforts such as high-tech road infrastructure, connection technology between road components, and traffic information systems are currently underway. However, the current central control center-oriented information collection and provision service structure and the insufficient road infrastructure limit the realization of the C-ITS, which requires a diversity of traffic information, real-time data, advanced traffic safety management, and transportation convenience services. In this study, a network construction method based on the existing received signal strength indicator (RSSI) selected as a comparison target, and the experimental target and the proposed intelligent edge network compared and analyzed. The result of the analysis showed that the data transmission rate in the intelligent edge network was 97.48%, the data transmission time was 215 ms, and the recovery time of network failure was 49,983 ms.