• Title, Summary, Keyword: Congestion Prediction

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Subway Congestion Prediction and Recommendation System using Big Data Analysis (빅데이터 분석을 이용한 지하철 혼잡도 예측 및 추천시스템)

  • Kim, Jin-su
    • Journal of Digital Convergence
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    • v.14 no.11
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    • pp.289-295
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    • 2016
  • Subway is a future-oriented means of transportation that can be safely and quickly mass transport many passengers than buses and taxis. Congestion growth due to the increase of the metro users is one of the factors that hinder citizens' rights to comfortably use the subway. Accordingly, congestion prediction in the subway is one of the ways to maximize the use of passenger convenience and comfort. In this paper, we monitor the level of congestion in real time via the existing congestion on the metro using multiple regression analysis and big data processing, as well as their departure station and arrival station information More information about the transfer stations offer a personalized congestion prediction system. The accuracy of the predicted congestion shows about 81% accuracy, which is compared to the real congestion. In this paper, the proposed prediction and recommendation application will be a help to prediction of subway congestion and user convenience.

ABR Traffic Control Using Feedback Information and Algorithm

  • Lee, Kwang-Ok;Son, Young-Su;Kim, Hyeon-ju;Bae, Sang-Hyun
    • Proceedings of the KAIS Fall Conference
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    • pp.236-242
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    • 2003
  • ATM ABR service controls network traffic using feedback information on the network congestion situation in order to guarantee the demanded service qualities and the available cell rates. In this paper we apply the control method using queue length prediction to the formation of feedback information for more efficient ABR traffic control. If backward node receive the longer delayed feedback information on the impending congestion, the switch can be already congested from the uncontrolled arriving traffic and the fluctuation of queue length can be inefficiently high in the continuing time intervals. The feedback control method proposed in this paper predicts the queue length in the switch using the slope of queue length prediction function and queue length changes in time-series. The predicted congestion information is backward to the node. NLMS and neural network are used as the predictive control functions, and they are compared from performance on the queue length prediction. Simulation results show the efficiency of the proposed method compared to the feedback control method without the prediction. Therefore, we conclude that the efficient congestion and stability of the queue length controls are possible using the prediction scheme that can resolve the problems caused from the longer delays of the feedback information.

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A Study on Estimate Model for Peak Time Congestion

  • Kim, Deug-Bong;Yoo, Sang-Lok
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.20 no.3
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    • pp.285-291
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    • 2014
  • This study applied regression analysis to evaluate the impact of hourly average congestion calculated by bumper model in the congested area of each passage of each port on the peak time congestion, to suggest the model formula that can predict the peak time congestion. This study conducted regression analysis of hourly average congestion and peak time congestion based on the AIS survey study of 20 ports in Korea. As a result of analysis, it was found that the hourly average congestion has a significant impact on the peak time congestion and the prediction model formula was derived. This formula($C_p=4.457C_a+29.202$) can be used to calculate the peak time congestion based on the predicted hourly average congestion.

Development of Traffic Congestion Prediction Module Using Vehicle Detection System for Intelligent Transportation System (ITS를 위한 차량검지시스템을 기반으로 한 교통 정체 예측 모듈 개발)

  • Sin, Won-Sik;Oh, Se-Do;Kim, Young-Jin
    • IE interfaces
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    • v.23 no.4
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    • pp.349-356
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    • 2010
  • The role of Intelligent Transportation System (ITS) is to efficiently manipulate the traffic flow and reduce the cost in logistics by using the state of the art technologies which combine telecommunication, sensor, and control technology. Especially, the hardware part of ITS is rapidly adapting to the up-to-date techniques in GPS and telematics to provide essential raw data to the controllers. However, the software part of ITS needs more sophisticated techniques to take care of vast amount of on-line data to be analyzed by the controller for their decision makings. In this paper, the authors develop a traffic congestion prediction model based on several different parameters from the sensory data captured in the Vehicle Detection System (VDS). This model uses the neural network technology in analyzing the traffic flow and predicting the traffic congestion in the designated area. This model also validates the results by analyzing the errors between actual traffic data and prediction program.

Dynamic Polling Algorithm Based on Line Utilization Prediction (선로 이용률 예측 기반의 동적 폴링 기법)

  • Jo, Gang-Hong;An, Seong-Jin;Jeong, Jin-Uk
    • The KIPS Transactions:PartC
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    • v.9C no.4
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    • pp.489-496
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    • 2002
  • This study proposes a new polling algorithm allowing dynamic change in polling period based on line utilization prediction. Polling is the most important function in network monitoring, but excessive polling data causes rather serious congestion conditions of network when network is In congestion. Therefore, existing multiple polling algorithms decided network congestion or load of agent with previously performed polling Round Trip Time or line utilization, chanced polling period, and controlled polling traffic. But, this algorithm is to change the polling period based on the previous polling and does not reflect network conditions in the current time to be polled. A algorithm proposed in this study is to predict whether polling traffic exceeds threshold of line utilization on polling path based on the past data and to change the polling period with the prediction. In this study, utilization of each line configuring network was predicted with AR model and violation of threshold was presented in probability. In addition, suitability was evaluated by applying the proposed dynamic polling algorithm based on line utilization prediction to the actual network, reasonable level of threshold for line utilization and the violation probability of threshold were decided by experiment. Performance of this algorithm was maximized with these processes.

An Automatic Pattern Recognition Algorithm for Identifying the Spatio-temporal Congestion Evolution Patterns in Freeway Historic Data (고속도로 이력데이터에 포함된 정체 시공간 전개 패턴 자동인식 알고리즘 개발)

  • Park, Eun Mi;Oh, Hyun Sun
    • Journal of Korean Society of Transportation
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    • v.32 no.5
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    • pp.522-530
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    • 2014
  • Spatio-temporal congestion evolution pattern can be reproduced using the VDS(Vehicle Detection System) historic speed dataset in the TMC(Traffic Management Center)s. Such dataset provides a pool of spatio-temporally experienced traffic conditions. Traffic flow pattern is known as spatio-temporally recurred, and even non-recurrent congestion caused by incidents has patterns according to the incident conditions. These imply that the information should be useful for traffic prediction and traffic management. Traffic flow predictions are generally performed using black-box approaches such as neural network, genetic algorithm, and etc. Black-box approaches are not designed to provide an explanation of their modeling and reasoning process and not to estimate the benefits and the risks of the implementation of such a solution. TMCs are reluctant to employ the black-box approaches even though there are numerous valuable articles. This research proposes a more readily understandable and intuitively appealing data-driven approach and developes an algorithm for identifying congestion patterns for recurrent and non-recurrent congestion management and information provision.

Road Speed Prediction Scheme Considering Traffic Incidents (교통 돌발 상황을 고려한 도로 속도 예측 기법)

  • Park, Songhee;Choi, Dojin;Bok, Kyoungsoo;Yoo, Jaesoo
    • The Journal of the Korea Contents Association
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    • v.20 no.4
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    • pp.25-37
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    • 2020
  • As social costs of traffic congestion increase, various studies are underway to predict road speed. In order to improve the accuracy of road speed prediction, unexpected traffic situations need to be considered. In this paper, we propose a road speed prediction scheme considering traffic incidents affecting road speed. We use not only the speed data of the target road but also the speed data of the connected roads to reflect the impact of the connected roads. We also analyze the amount of speed change to predict the traffic congestion caused by traffic incidents. We use the speed data of connected roads and target road with input data to predict road speed in the first place. To reduce the prediction error caused by breaking the regular road flow due to traffic incidents, we predict the final road speed by applying event weights. It is shown through various performance evaluations that the proposed method outperforms the existing methods.

A Study on an Adaptive UPC Algorithm Based on Traffic Multiplexing Information in ATM Networks (ATM 망에서 트래픽 다중화 정보에 의한 적응적 UPC 알고리즘에 관한 연구)

  • Kim, Yeong-Cheol;Byeon, Jae-Yeong;Seo, Hyeon-Seung
    • The Transactions of the Korea Information Processing Society
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    • v.6 no.10
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    • pp.2779-2789
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    • 1999
  • In this paper, we propose a new neural Buffered Leaky Bucket algorithm for preventing the degradation of network performance caused by congestion and dealing with the traffic congestion in ATM networks. We networks. We justify the validity of the suggested method through performance comparison in aspects of cell loss rate and mean transfer delay under a variety of traffic conditions requiring the different QoS(Quality of Service). also, the cell scheduling algorithms such as DWRR and DWEDF used for multiplexing the incoming traffics are induced to get the delay time of the traffics fairly. The network congestion information from cell scheduler is used to control the predicted traffic loss rate of Neural Leaky Bucket, and token generation rate is changed by the predicted values. The prediction of traffic loss rate by neural networks can effectively reduce the cell loss rate and the cell transfer delay of next incoming cells and be applied to other traffic control systems. Computer simulation results performed for traffic prediction show that QoSs of the various kinds of traffics are increased.

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A Study on Predictive Traffic Control Algorithms for ABR Services (ABR 서비스를 위한 트래픽 예측 제어 알고리즘 연구)

  • 오창윤;장봉석
    • Journal of Internet Computing and Services
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    • v.1 no.2
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    • pp.29-37
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    • 2000
  • Asynchronous transfer mode is flexible to support multimedia communication services using asynchronous time-sharing and statistical multimedia techniques to the existing data communication area, ATM ABR service controls network traffic using feedback information on the network congestion situation in order to guarantee the demanded service qualities and the available cell rates, In this paper we apply the control method using queue length prediction to the formation of feedback information for more efficient ABR traffic control. If backward node receive the longer delayed feedback information on the impending congestion, the switch can be already congested from the uncontrolled arriving traffic and the fluctuation of queue length can be inefficiently high in the continuing time intervals, The feedback control method proposed in this paper predicts the queue length in the switch using the slope of queue length prediction function and queue length changes in time-series, The predicted congestion information is backward to the node, NLMS and neural network are used as the predictive control functions, and they are compared from performance on the queue length prediction. Simulation results show the efficiency of the proposed method compared to the feedback control method without the prediction, Therefore, we conclude that the efficient congestion and stability of the queue length controls are possible using the prediction scheme that can resolve the problems caused from the longer delays of the feedback information.

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An implementation of the dynamic rate leaky bucket algorithm combined with a neural network based prediction (신경회로망 예측기법을 결합한 Dynamic Rate Leaky Bucket 알고리즘의 구현)

  • 이두헌;신요안;김영한
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.22 no.2
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    • pp.259-267
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    • 1997
  • The advent of B-ISDN using ATM(asynchronous transfer mode) made possible a variety of new multimedia services, however it also created a problem of congestion control due to bursty nature of various traffic sources. To tackle this problem, UPC/NPC(user parameter control/network parameter control) have been actively studied and DRLB(dynamic rate leaky bucket) algorithm, in which the token generation rate is changed according to states of data source andbuffer occupancy, is a good example of the UPC/NPC. However, the DRLB algorithm has drawbacks of low efficiency and difficult real-time implementation for bursty traffic sources because the determination of token generation rate in the algorithm is based on the present state of network. In this paper, we propose a more plastic and effective congestion control algorithm by combining the DRLB algorithm and neural network based prediction to remedy the drawbacks of the DRLB algorithm, and verify the efficacy of the proposed method by computer simulations.

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