• Title/Summary/Keyword: Smart traffic

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An Application of Driver's Critical Gap on a Changing Lane Assistance System for an Unprotected Left-turn (비보호 좌회전 보조를 목적으로 하는 차선 변경 보조 시스템에서의 임계간격 적용)

  • Jeong, Hwang Hun;Shin, Hee Young;Seo, Myoung Kook
    • Journal of Drive and Control
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    • v.19 no.3
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    • pp.47-52
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    • 2022
  • The C-ITS (Cooperative-intelligent Transport System) is a driver assistance system that prevents car accidents and enhances traffic conditions, via sharing traffic information between vehicles and roadway infrastructures. A CLAS (changing lane assistance system) for unprotected left-turn, is a C-ITS that assists a driver with safely changing lanes. This system addresses a driver's critical gap, that enables the system to express a driver's uncertainty. A driver's critical gap is a time that can be used in a threshold, to change a lane or not. Unfortunately, a driver's critical gap is difficult to use in a CLAS directly. This paper addresses a driver's critical gap, and how it can be applied in a CLAS for an unprotected left-turn.

Survey on Smart Contract Programming Languages (스마트 컨트랙트 프로그래밍 언어 동향 조사)

  • Kim, Ik-Soon
    • Electronics and Telecommunications Trends
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    • v.35 no.5
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    • pp.134-138
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    • 2020
  • Blockchain is an enabling technology for managing data with high trust and transparency among connected computers. Blockchain emerged with the advent of the Bitcoin cryptocurrency, and then, evolved as general-purpose platforms such as Ethereum, EOS, R3 Corda, and IBM Hyperledger Fabric. The application of blockchain covers a broad range of areas such as fintech, decentralized identity, distribution, real estate trading, games, and drone air traffic management. Smart contracts are indispensable for constructing blockchain services. This survey classifies smart contract languages by their features and shows their differences from existing general-purpose programming languages.

A study for the reduction of network traffic through an efficient processing of the trend analysis information (경향분석 정보의 효율적인 처리를 통한 네트워크 트래픽 감소 방안에 대한 연구)

  • Youn, Chun-Kyun
    • Journal of Digital Convergence
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    • v.10 no.1
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    • pp.323-333
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    • 2012
  • Network traffic demand is increasing explosively because of various smart equipment and services on smart era. It causes of traffic overload for wireless and wired network. Network management system is very important to control the explosion of data traffic. It uses SNMP to communicate with various network resources for management functions and creates lots of management traffic. Those are can be serious traffic congestion on a network. I propose an improving function of SNMP to minimize unnecessary traffics between manager and agent for collecting the Trend Analysis Information which is mainly used to monitor and accumulate for a specific time period in this paper. The results of test show it has compatibility with the existing SNMP and greatly decreases the amount of network traffic and response time.

Pattern Analysis of Traffic Accident data and Prediction of Victim Injury Severity Using Hybrid Model (교통사고 데이터의 패턴 분석과 Hybrid Model을 이용한 피해자 상해 심각도 예측)

  • Ju, Yeong Ji;Hong, Taek Eun;Shin, Ju Hyun
    • Smart Media Journal
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    • v.5 no.4
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    • pp.75-82
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    • 2016
  • Although Korea's economic and domestic automobile market through the change of road environment are growth, the traffic accident rate has also increased, and the casualties is at a serious level. For this reason, the government is establishing and promoting policies to open traffic accident data and solve problems. In this paper, describe the method of predicting traffic accidents by eliminating the class imbalance using the traffic accident data and constructing the Hybrid Model. Using the original traffic accident data and the sampled data as learning data which use FP-Growth algorithm it learn patterns associated with traffic accident injury severity. Accordingly, In this paper purpose a method for predicting the severity of a victim of a traffic accident by analyzing the association patterns of two learning data, we can extract the same related patterns, when a decision tree and multinomial logistic regression analysis are performed, a hybrid model is constructed by assigning weights to related attributes.

A Study on the Development of Simulator for Performance Evaluation of Traffic Control using UPC Algorithm in ATM Network (ATM 망에서 UPC를 이용한 트래픽 제어방법의 성능평가를 위한 시뮬레이터의 개발에 관한 연구)

  • 김문선
    • Journal of the Korea Society for Simulation
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    • v.8 no.2
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    • pp.45-56
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    • 1999
  • It is necessary that we should control the traffic to not only efficiently use the rich bandwidth of ATM network but also satisfy the users various requirements for service quality. However, it is very difficult to decide which control mechanism would be applied in real network because there are various types of ATM traffic and traffic control mechanisms. In this paper, a smart simulator is developed ot analyze the performance of a UPC(Usage Parameter Control) mechanism which is a typical traffic control mechanism. The simulator consists of a user interface that supports a menu-driven input form and a simulation program that is executed with the users input parameters. Especially, the simulator establishes more powerful and flexible simulation environment since it supports a more complex simulation applying various source traffic to several different UPC mechanisms at the same time and allows an arbitrary user-defined traffic in addition to some well-known traffic.

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Traffic Flow Prediction with Spatio-Temporal Information Fusion using Graph Neural Networks

  • Huijuan Ding;Giseop Noh
    • International journal of advanced smart convergence
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    • v.12 no.4
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    • pp.88-97
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    • 2023
  • Traffic flow prediction is of great significance in urban planning and traffic management. As the complexity of urban traffic increases, existing prediction methods still face challenges, especially for the fusion of spatiotemporal information and the capture of long-term dependencies. This study aims to use the fusion model of graph neural network to solve the spatio-temporal information fusion problem in traffic flow prediction. We propose a new deep learning model Spatio-Temporal Information Fusion using Graph Neural Networks (STFGNN). We use GCN module, TCN module and LSTM module alternately to carry out spatiotemporal information fusion. GCN and multi-core TCN capture the temporal and spatial dependencies of traffic flow respectively, and LSTM connects multiple fusion modules to carry out spatiotemporal information fusion. In the experimental evaluation of real traffic flow data, STFGNN showed better performance than other models.

A Proposal for SmartTV Development Plan by Applying Big Data Analysis Methodology (빅데이터 분석 방법을 적용한 스마트 TV의 발전 방안에 관한 제언)

  • Park, Nam-Gue;Kim, Sun-Bae
    • Journal of Digital Convergence
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    • v.12 no.1
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    • pp.347-358
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    • 2014
  • A smart TV is able to show terrestrial broadcasting and also can be used as a computer -VOD, games, image communications, application utilities and so on. In order to carry out Smart TV business, it has to contains contents, platforms, network terminal unit. If ill-equipped with any of these aboves, it must cooperate with other licensee. Therefore, Smart TV business is necessary to cooperate with each business agent. In this paper, we will look into domestic/foreign country Smart TV market, policy, vitalization strategy, and suggest the application of big data analysis methodology for Smart TV vitalization method - 1) hardware infrastructure building based on cloud computing 2) Network upgradability acceptable traffic increase 3) Technical development cooperation between each licensee 4) Variable Smart TV contents supply 5) Cooperation with party interested individuals in using UX/UI for N-Screen, network traffic estimation may increase, customized supply smart contents for consumer in real time.

High Performance QoS Traffic Transmission Scheme for Real-Time Multimedia Services in Wireless Networks

  • Kang, Moonsik
    • IEIE Transactions on Smart Processing and Computing
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    • v.1 no.3
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    • pp.182-191
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    • 2012
  • This paper proposes a high performance QoS (Quality of Service) traffic transmission scheme to provide real-time multimedia services in wireless networks. This scheme is based on both a traffic estimation of the mean rate and a header compression method by dividing this network model into two parts, core RTP/UDP/IP network and wireless access parts, using the IEEE 802.11 WLAN. The improvement achieved by the scheme means that it can be designed to include a means of provisioning the high performance QoS strategy according to the requirements of each particular traffic flow by adapting the header compression for real-time multimedia data. A performance evaluation was carried out to show the effectiveness of the proposed traffic transmission scheme.

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Big Data Based Dynamic Flow Aggregation over 5G Network Slicing

  • Sun, Guolin;Mareri, Bruce;Liu, Guisong;Fang, Xiufen;Jiang, Wei
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.11 no.10
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    • pp.4717-4737
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    • 2017
  • Today, smart grids, smart homes, smart water networks, and intelligent transportation, are infrastructure systems that connect our world more than we ever thought possible and are associated with a single concept, the Internet of Things (IoT). The number of devices connected to the IoT and hence the number of traffic flow increases continuously, as well as the emergence of new applications. Although cutting-edge hardware technology can be employed to achieve a fast implementation to handle this huge data streams, there will always be a limit on size of traffic supported by a given architecture. However, recent cloud-based big data technologies fortunately offer an ideal environment to handle this issue. Moreover, the ever-increasing high volume of traffic created on demand presents great challenges for flow management. As a solution, flow aggregation decreases the number of flows needed to be processed by the network. The previous works in the literature prove that most of aggregation strategies designed for smart grids aim at optimizing system operation performance. They consider a common identifier to aggregate traffic on each device, having its independent static aggregation policy. In this paper, we propose a dynamic approach to aggregate flows based on traffic characteristics and device preferences. Our algorithm runs on a big data platform to provide an end-to-end network visibility of flows, which performs high-speed and high-volume computations to identify the clusters of similar flows and aggregate massive number of mice flows into a few meta-flows. Compared with existing solutions, our approach dynamically aggregates large number of such small flows into fewer flows, based on traffic characteristics and access node preferences. Using this approach, we alleviate the problem of processing a large amount of micro flows, and also significantly improve the accuracy of meeting the access node QoS demands. We conducted experiments, using a dataset of up to 100,000 flows, and studied the performance of our algorithm analytically. The experimental results are presented to show the promising effectiveness and scalability of our proposed approach.

Open Architecture of Transportation Information Dissemination using OPEN API (OPEN API를 이용한 개방형 교통정보 제공기법)

  • Lee, Ji-Won;Nam, Doo-Hee
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.12 no.1
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    • pp.109-114
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    • 2012
  • Intelligent Transportation Systems (ITS) is aimed to implement IT(Information Technology) to develop the next-generation transportation system in order to improve traffic conditions. In order to provide traffic information, the methods that provide available traffic informations to the public are needed. In this paper, analysis of the transportation applications in smart-phone and currently available methods of traffic information's sharing and providing were discussed. Finally, OPEN API was discussed and shows its effectiveness for transportation information area especially in smart phone.