• Title, Summary, Keyword: Congestion Prediction

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Development of the Train Dwell Time Model : Metering Strategy to Control Passenger Flows in the Congested Platform (승강장 혼잡관리를 위한 열차의 정차시간 예측모형)

  • KIM, Hyun;Lee, Seon-Ha;LIM, Guk-Hyun
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.16 no.3
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    • pp.15-27
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    • 2017
  • In general, increasing train dwell time leads to increasing train service frequency, and it in turn contributes to increasing the congestion level of train and platform. Therefore, the studies on train dwell time have received growing attention in the perspective of scheduling train operation. This study develops a prediction model of train dwell time to enable train operators to mitigate platform congestion by metering passenger inflow at platform gate with respect to platform congestion levels in real-time. To estimate the prediction model, three types of independent variables were applied: number of passengers to get into train, number of passengers to get out of trains, and train weights, which are collectable in real-time. The explanatory power of the estimated model was 0.809, and all of the dependent variables were statistically significant at the 99%. As a result, this model can be available for the basis of on-time train service through platform gate metering, which is a strategy to manage passenger inflow at the platform.

The Analysis of The Domestic Transmission System and Transmission Congestion Price (국내 송전계통 및 송전제약 비용 분석)

  • Baeck Woong Ki;Chun Yeong han;Kim Jung hun;Kwak No hong;Son In Jun
    • Proceedings of the KIEE Conference
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    • pp.737-739
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    • 2004
  • The domestic power system established with Cost-Based-Pricing(CBP) from April 2001. The system is a uniform pricing system. System Operator(50) establishes a Price Setting Schedule by the prediction of consumption and the presented bid price(generation cost) of the generation utility. But the Price Setting Schedule doesn't take account of the constraint of the system. This cause a transmission congestion, constrained-on generation and constrained-off generation. This Paper search the way of the increasing efficiency of domestic power system through the redemption of congestion charge.

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Long-term Prediction of Bus Travel Time Using Bus Information System Data (BIS 자료를 이용한 중장기 버스 통행시간 예측)

  • LEE, Jooyoung;Gu, Eunmo;KIM, Hyungjoo;JANG, Kitae
    • Journal of Korean Society of Transportation
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    • v.35 no.4
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    • pp.348-359
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    • 2017
  • Recently, various public transportation activation policies are being implemented in order to mitigate traffic congestion in metropolitan areas. Especially in the metropolitan area, the bus information system has been introduced to provide information on the current location of the bus and the estimated arrival time. However, it is difficult to predict the travel time due to repetitive traffic congestion in buses passing through complex urban areas due to repetitive traffic congestion and bus bunching. The previous bus travel time study has difficulties in providing information on route travel time of bus users and information on long-term travel time due to short-term travel time prediction based on the data-driven method. In this study, the path based long-term bus travel time prediction methodology is studied. For this purpose, the training data is composed of 2015 bus travel information and the 2016 data are composed of verification data. We analyze bus travel information and factors affecting bus travel time were classified into departure time, day of week, and weather factors. These factors were used into clusters with similar patterns using self organizing map. Based on the derived clusters, the reference table for bus travel time by day and departure time for sunny and rainy days were constructed. The accuracy of bus travel time derived from this study was verified using the verification data. It is expected that the prediction algorithm of this paper could overcome the limitation of the existing intuitive and empirical approach, and it is possible to improve bus user satisfaction and to establish flexible public transportation policy by improving prediction accuracy.

Different Impacts of Independent Recurrent and Non-Recurrent Congestion on Freeway Segments (고속도로상의 독립적인 반복 및 비반복정체의 영향비교)

  • Gang, Gyeong-Pyo;Jang, Myeong-Sun
    • Journal of Korean Society of Transportation
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    • v.25 no.6
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    • pp.99-109
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    • 2007
  • There have been few studies on the impacts of independent recurrent and non-recurrent congestion on freeway networks. The main reason is due partly to the lack of traffic data collected during those periods of recurrent and non-recurrent congestion and partly to the difficulty of using the simulation tools effectively. This study has suggested a methodology to analyze the independent impacts of the recurrent and non-recurrent congestion on target freeway segments. The proposed methodology is based on an elaborately calibrated simulation analysis, using real traffic data obtained during the recurrent and non-recurrent congestion periods. This paper has also summarized the evaluation results from the field tests of two ITS technologies, which were developed to provide drivers with real-time traffic information under traffic congestion. As a result, their accuracy may not be guaranteed during the transition periods such as the non-recurrent congestion. In summary, this study has been focused on the importance of non-recurrent congestion compared to recurrent congestion, and the proposed methodology is expected to provide a basic foundation for prioritizing limited government investments for improving freeway network performance degraded by recurrent or non-recurrent congestion.

An Adaptable Integrated Prediction System for Traffic Service of Telematics

  • Cho, Mi-Gyung;Yu, Young-Jung
    • Journal of information and communication convergence engineering
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    • v.5 no.2
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    • pp.171-176
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    • 2007
  • To give a guarantee a consistently high level of quality and reliability of Telematics traffic service, traffic flow forecasting is very important issue. In this paper, we proposed an adaptable integrated prediction model to predict the traffic flow in the future. Our model combines two methods, short-term prediction model and long-term prediction model with different combining coefficients to reflect current traffic condition. Short-term model uses the Kalman filtering technique to predict the future traffic conditions. And long-term model processes accumulated speed patterns which means the analysis results for all past speeds of each road by classifying the same day and the same time interval. Combining two models makes it possible to predict future traffic flow with higher accuracy over a longer time range. Many experiments showed our algorithm gives a better precise prediction than only an accumulated speed pattern that is used commonly. The result can be applied to the car navigation to support a dynamic shortest path. In addition, it can give users the travel information to avoid the traffic congestion areas.

A Deep Learning-Based Model for Predicting Traffic Congestion in Semiconductor Fabrication (딥러닝을 활용한 반도체 제조 물류 시스템 통행량 예측모델 설계)

  • Kim, Jong Myeong;Kim, Ock Hyeon;Hong, Sung Bin;Lim, Dae-Eun
    • Journal of Industrial Technology
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    • v.39 no.1
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    • pp.27-31
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    • 2019
  • Semiconductor logistics systems are facing difficulties in increasing production as production processes become more complicated due to the upgrading of fine processes. Therefore, the purpose of the research is to design predictive models that can predict traffic during the pre-planning stage, identify the risk zones that occur during the production process, and prevent them in advance. As a solution, we build FABs using automode simulation to collect data. Then, the traffic prediction model of the areas of interest is constructed using deep learning techniques (keras - multistory conceptron structure). The design of the predictive model gave an estimate of the traffic in the area of interest with an accuracy of about 87%. The expected effect can be used as an indicator for making decisions by proactively identifying congestion risk areas during the Fab Design or Factory Expansion Planning stage, as the maximum traffic per section is predicted.

The Prediction Modelling of Traffic Flow with Time-Variable Non-Linear Characteristic in ATM Network (시변비선형 특성을 지닌 ATM 통화유량 예측 모델링)

  • 김윤석
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.25 no.9A
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    • pp.1299-1305
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    • 2000
  • In B-ISDN, to realize ATM, the optimum control method of multi-media traffic must be proposed. Because there is not the traffic model of multi-media to make clear, the realization of optimum ATM congestion control is very difficult. In this paper, the traffic model is assumed to be slowly time-variable non-linear function and for real-time prediction of it, new model which is composed with parallel triple neural networks is proposed. And the simulation to predict assumed ATM traffic is executed. From the result, it's capability is shown that the proposed neural network model can be used in ATM congestion control.

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Evaluation of a Traffic Lane Closure and Pavement Repair for a Certain Period (Focusing on the Gimcheon~Sunsan Project) (1차로 전면차단 후 도로포장 보수방법의 효과분석 (김천~선산 사례중심))

  • Ryu, Sung Woo;Park, Kwon-Je;Han, SeungHwan;Choi, InGu;Cho, Yoon-Ho
    • International Journal of Highway Engineering
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    • v.18 no.3
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    • pp.11-19
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    • 2016
  • PURPOSES : This study supports the evidence that it is possible to rehabilitate a damaged pavement with a lane closure specifically based on the Gimcheon~Sunsan project. METHODS : The prediction results from the simulation programs were compared with field monitoring, which focused on traffic management planning, congestion (length, time, and passing speed), bypass, and user cost, among others. RESULTS : The research results showed that lane closure application and pavement repair of the aged pavement in Korea were possible, even though the prediction results were minimally different from the field monitoring. The road agency contributes to service life extension of the rehabilitated pavement using this method. CONCLUSIONS : A marginal effect caused by the lane closure was observed on travelling users or vehicles, and the user cost of pavement repair decreased. Therefore, introducing the repair method or rehabilitation in Korea is possible. Information dissemination through various media was properly done to execute the project well. Moreover, the construction area traffic utilized nearby alternative roads. Therefore, improving the repaired pavemen's service life while ensuring that the pavement management agency can provide a road with comfortable user riding quality was possible.

A Performance Improvement Method with Considering of Congestion Prediction and Packet Loss on UDT Environment (UDT 환경에서 혼잡상황 예측 및 패킷손실을 고려한 성능향상 기법)

  • Park, Jong-Seon;Lee, Seung-Ah;Kim, Seung-Hae;Cho, Gi-Hwan
    • The Journal of the Korea Contents Association
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    • v.11 no.2
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    • pp.69-78
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    • 2011
  • Recently, the bandwidth available to an end user has been dramatically increasing with the advancing of network technologies. This high-speed network naturally requires faster and/or stable data transmission techniques. The UDT(UDP based Data Transfer protocol) is a UDP based transport protocol, and shows more efficient throughput than TCP in the long RTT environment, with benefit of rate control for a SYN time. With a NAK event, however, it is difficult to expect an optimum performance due to the increase of fixed sendInterval and the flow control based on the previous RTT. This paper proposes a rate control method on following a NAK, by adjusting the sendInterval according to some degree of RTT period which calculated from a set of experimental results. In addition, it suggests an improved flow control method based on the TCP vegas, in order to predict the network congestion afterward. An experimental results show that the revised flow control method improves UDT's throughput about 20Mbps. With combining the rate control and flow control proposed, the UDT throughput can be improved up to 26Mbps in average.

Network Routing by Traffic Prediction on Time Series Models (시계열 모형의 트래픽 예측에 기반한 네트워크 라우팅)

  • Jung, Sang-Joon;Chung, Youn-Ky;Kim, Chong-Gun
    • Journal of KIISE:Information Networking
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    • v.32 no.4
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    • pp.433-442
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    • 2005
  • An increase In traffic has a large Influence on the performance of a total network. Therefore, traffic management has become an important issue of network management. In this paper, we propose a new routing algorithm that attempts to analyze network conditions using time series prediction models and to propose predictive optimal routing decisions. Traffic congestion is assumed when the predicting result is bigger than the permitted bandwidth. By collecting traffic in real network, the predictable model is obtained when it minimizes statistical errors. In order to predict network traffic based on time series models, we assume that models satisfy a stationary assumption. The stationary assumption can be evaluated by using ACF(Auto Correlation Function) and PACF(Partial Auto Correlation Function). We can obtain the result of these two functions when it satisfies the stationary assumption. We modify routing oaths by predicting traffic in order to avoid traffic congestion through experiments. As a result, Predicting traffic and balancing load by modifying paths allows us to avoid path congestion and increase network performance.