• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제13권6호
• /
• pp.2964-2985
• /
• 2019

Global data center IP traffic is expected to reach 20.6 zettabytes (ZB) by the end of 2021. Intra-data center networks (Intra-DCN) will account for 71.5% of the data center traffic flow and will be the largest portion of the traffic. The understanding of traffic distribution in IntraDCN is still sketchy. It causes significant amount of bandwidth to go unutilized, and creates avoidable choke points. Conventional transport protocols such as Optical Packet Switching (OPS) and Optical Burst Switching (OBS) allow a one-sided view of the traffic flow in the network. This therefore causes disjointed and uncoordinated decision-making at each node. For effective resource planning, there is the need to consider joining the distributed with centralized management which anticipates the system's needs and regulates the entire network. Methods derived from Kalman filters have proved effective in planning road networks. Considering the network available bandwidth as data transport highways, we propose an intelligent enhanced SDN concept applied to OBS architecture. A management plane (MP) is added to conventional control (CP) and data planes (DP). The MP assembles the traffic spatio-temporal parameters from ingress nodes, uses Kalman filtering prediction-based algorithm to estimate traffic demand. Prior to packets arrival at edges nodes, it regularly forwards updates of resources allocation to CPs. Simulations were done on a hybrid scheme (1+1) and on the centralized OBS. The results demonstrated that the proposition decreases the packet loss ratio. It also improves network latency and throughput-up to 84 and 51%, respectively, versus the traditional scheme.

• 한국ITS학회 논문지
• /
• 제7권6호
• /
• pp.12-20
• /
• 2008

최근에 가속화된 무선 통신 기술의 발달은 교통 분야에도 새로운 가능성을 제시하고 있다. 무선 통신 기술의 하나인 애드혹 네트워크(ad hoc network)는 서비스를 제공하는 시설(Infrastructure) 없이도 노드간 통신을 통하여 데이터를 교환 할 수 있는 기술이다. 특히, 차량에 의한 애드혹 네트워크를 가리키는 Vehicular ad hoc networks (VANETs)는 주행중 차량간 통신을 가능하게 하여, 차량들 스스로 자기가 경험한 교통정보를 공유하는 분산형 교통 정보 시스템을 구성할 수 있다. 본 연구에서는 VANET을 기반으로 하는 교통 정보 시스템에서 차량간 통신에 의하여 전달되는 교통정보의 전달범위를 측정하였다. 정보의 전달 범위를 측정하기 위하여 미시적 모형의 교통 시뮬레이터인 Paramics와 네트워크 시뮬레이터인 QualNet을 통합한 컴퓨터 모의실험 환경을 구축하여, 실제 도로망과 교통수요를 바탕으로 실험을 실시하였다. 결과에 의하면, 제한속도가 97km/hr(60mile/hr)인 고속도로에서 시장점유율이 10%인 경우, 5km 전방의 교통 정보를 얻는데 비혼잡시(10veh/ln.km) 약 3분, 혼잡시(40veh/ln.km) 43초 소요되었다. 즉, 비혼잡시 대부분의 교통 정보는 반대 방향으로 진행중인 차량에 의하여 전달되고, 혼잡시에는 같은 방향으로 진행하는 차량에 의한 전달 기회가 많아진다는 사실을 보여준다. 특히, 비혼잡시 낮은 시장점유율(3%)에서도 교통 정보는 효율적으로 전달되고 있었다. 본 연구에서 소개된 VANETs은 발전 가능성이 높은 기술로, 정보를 신속하게 전달할 수 있는 장점과 함께 차량과 인프라간의 통신을 위한 시설의 설치시 밀도를 결정하는데 중요한 자료로 활용될 것으로 기대된다.

• 디지털산업정보학회논문지
• /
• 제6권2호
• /
• pp.19-27
• /
• 2010

Historical archived traffic data management system enables a long term time-series analysis and provides data necessary to acquire the constantly changing traffic conditions and to evaluate and analyze various traffic related strategies and policies. Such features are provided by maintaining highly reliable traffic data through scientific and systematic management. Now, the management systems for massive traffic data have a several problems such as, the storing and management methods of a large volume of archive data. In this paper, we describe how to storing and management for the massive traffic data and, we propose methodology for logical and physical architecture, collecting and storing, database design and implementation, process design of massive traffic data.

• IEIE Transactions on Smart Processing and Computing
• /
• 제1권1호
• /
• pp.1-7
• /
• 2012

This paper presents a system used to automatically recognize the road traffic control gestures of police officers. In this approach,the control gestures of traffic police officers are captured in the form of depth images.A human skeleton is then constructed using a kinematic model. The feature vector describing a traffic control gesture is built from the relative angles found amongst the joints of the constructed human skeleton. We utilize Support Vector Machines (SVMs) to perform the gesture recognition. Experiments show that our proposed method is robust and efficient and is suitable for real-time application. We also present a testbed system based on the SVMs trained data for real-time traffic gesture recognition.

• 한국정보과학회:학술대회논문집
• /
• 한국정보과학회 2011년도 한국컴퓨터종합학술대회논문집 Vol.38 No.1(A)
• /
• pp.491-493
• /
• 2011

Traffic sign detection is the domain of automatic driver assistant systems. There are literatures for traffic sign detection using color information, however, color-based method contains ill-posed condition and to extract the region of interest is difficult. In our work, we propose a method for traffic sign detection using k-means clustering method, back-propagation neural network, and projection histogram features that yields the robustness for ill-posed condition. Using the color information of traffic signs enables k-means algorithm to cluster the region of interest for the detection efficiently. In each step of clustering, a cluster is verified by the neural network so that the cluster exactly represents the location of a traffic sign. Proposed method is practical, and yields robustness for the unexpected region of interest or for multiple detections.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2004년도 ICEIC The International Conference on Electronics Informations and Communications
• /
• pp.171-175
• /
• 2004

Network management for detail analysis can cause speed decline of application in case of lack band width by traffic increase of the explosive Internet. Because a manager requests MIB value for the desired objects to an agent by management policy, and then the agent responds to the manager. Such processes are repeated, so it can cause increase of network traffic. Specially, repetitious occurrence of sending-receiving information is very inefficient for a same object when a trend analysis of traffic is performed. In this paper, an efficient SNMP is proposed to add new PDUs into the existing SNMP in order to accept time function. Utilizing this PDU, it minimizes unnecessary sending-receiving message and collects information for trend management of network efficiently. This proposed SNMP is tested for compatibility with the existing SNMP and decreases amount of network traffic largely

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제10권1호
• /
• pp.136-151
• /
• 2016

Flexible large-scale WLANs are now widely deployed in crowded and highly mobile places such as campus, airport, shopping mall and company etc. But network management is hard for large-scale WLANs due to highly uneven interference and throughput among links. So the traffic is difficult to predict accurately. In the paper, through analysis of traffic in two real large-scale WLANs, Granger Causality is found in both scenarios. In combination with information entropy, it shows that the traffic prediction of target AP considering Granger Causality can be more predictable than that utilizing target AP alone, or that of considering irrelevant APs. So We develops new method -Granger Causality and Vector Auto-Regression (GCVAR), which takes APs series sharing Granger Causality based on Vector Auto-regression (VAR) into account, to predict the traffic flow in two real scenarios, thus redundant and noise introduced by multivariate time series could be removed. Experiments show that GCVAR is much more effective compared to that of traditional univariate time series (e.g. ARIMA, WARIMA). In particular, GCVAR consumes two orders of magnitude less than that caused by ARIMA/WARIMA.

• 한국콘텐츠학회:학술대회논문집
• /
• 한국콘텐츠학회 2004년도 추계 종합학술대회 논문집
• /
• pp.501-504
• /
• 2004

10개교차로 연동을 위해서는 정확한 신호주기를 개선하기 위해서는 직진 및 회전차선의 공유로 인한 정확한 직진 차량의 파악을 해야한다.10개교차로 교차로 연동을 할려면, 에측되는 교통량의 DATA와 차선 및 길이 보정게수산출이 없이는 압막힘 현상이 발생한다. 명절, 백화점 SALE, 출퇴근시간, 각종행사시에는 교통량이 갑자기 증가시에는 최적 신호주기를 생성하여야한다. 이러한 문제점을 개선하기위해서는 예상 행사인원이 500명이 초과할 경우에 최소한 2-3 시간전에 경찰청으로 미리 교통량을 측정하여 보고해야한다. 출 퇴근시간별로, 교통사고 다발 지역에 차선별로 위치한 건물에 진입하는 예상 교통량을 측정하는 신경망 기법이 필요하다.

• 대한전자공학회논문지TC
• /
• 제44권1호
• /
• pp.85-92
• /
• 2007

교통사고는 해마다 감소하고 있는 추세이다. 그러나 대형사고나 뺑소니 사고는 계속 증가하고 이다. 뿐만 아니라, 교통사고는 주변지역의 교통 정체를 유발하게 되어 사회적 비용이 들게 된다. 이 때문에 우리는 본 논문에서 교통사고를 예방할 수 있는 방안을 제시하고, 교통사고가 발생한 경우 교통사고를 신속히 처리할 수 있는 방안을 제시하였다. 운전자는 DMB를 사용함으로서 교통상황을 청각과 시각을 통해서 보다 정확히 알 수 있다. 끝으로, 우리는 TPEG으로 보다 효과적인 교통정보를 제공하는 방안을 제안하였다.

• 자동차안전학회지
• /
• 제14권2호
• /
• pp.45-50
• /
• 2022

The vehicle motion in urban environment is determined by surrounding traffic flow, which cause understanding the flow to be a factor that dominantly affects the motion planning of the vehicle. The traffic flow in this urban environment is accessed using various urban infrastructure information. This paper represents a color recognition algorithm for traffic lights to perceive traffic condition which is a main information among various urban infrastructure information. Deep learning based vision open source realizes positions of traffic lights around the host vehicle. The data are processed to input data based on whether it exists on the route of ego vehicle. The colors of traffic lights are estimated through pixel values from the camera image. The proposed algorithm is validated in intersection situations with traffic lights on the test track. The results show that the proposed algorithm guarantees precise recognition on traffic lights associated with the ego vehicle path in urban intersection scenarios.