• International Journal of Highway Engineering
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• v.12 no.4
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• pp.1-9
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• 2010

Predictive information on the freeway incident impacts can be a critical criterion in selecting travel options for users and in operating transportation system for operators. Provided properly, users can select time-effective route and operators can effectively run the system efficiently. In this study, a model is proposed to predict freeway incident impacts. The predictive model for incident impacts is based on short-term prediction. The proposed models are examined using MARE. The analysis results suggest that the models are accurate enough to be deployed in a real-world. The development of microscopic models to predict incident effects is expected to help minimize traffic delay and mitigate related social costs.

• Journal of Korean Society of Transportation
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• v.21 no.5
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• pp.83-95
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• 2003

This study aims to develop travel time estimation and prediction models on the freeway using measurements from vehicle detectors. In this study, we established a travel time estimation model using traffic volume which is a principle factor of traffic flow changes by reviewing existing travel time estimation techniques. As a result of goodness of fit test. in the normal traffic condition over 70km/h, RMSEP(Root Mean Square Error Proportion) from travel speed is lower than the proposed model, but the proposed model produce more reliable travel times than the other one in the congestion. Therefore in cases of congestion the model uses the method of calculating the delay time from excess link volumes from the in- and outflow and the vehicle speeds from detectors in the traffic situation at a speed of over 70km/h. We also conducted short term prediction of Kalman Filtering to forecast traffic condition and more accurate travel times using statistical model The results of evaluation showed that the lag time occurred between predicted travel time and estimated travel time but the RMSEP values of predicted travel time to observations are as 1ow as that of estimation.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.10 s.352
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• pp.8-17
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• 2006

Recently, the high-speed Internet users increase rapidly and broadband networks have been widely deployed. However, the current TCP congestion control algorithm was designed for relatively narrowband network environments, and thus its performance is inefficient for traffic transport in broadband networks. To remedy this problem, the TCP having an enhanced congestion control algorithm is required for broadband networks. In this paper, we propose an improved TCP congestion control that can sufficiently utilize the large available bandwidth in broadband networks. The proposed algorithm predicts the available bandwidth by using ACK information and RTT variation, and prevents large packet losses by adjusting congestion window size appropriately. Also, it can rapidly utilize the large available bandwidth by enhancing the legacy TCP algorithm in congestion avoidance phase. In order to evaluate the performance of the proposed algorithm, we use the ns-2 simulator. The simulation results show that the proposed algorithm improves not only the utilization of the available bandwidth but also RTT fairness and the fairness between contending TCP flows better than the HSTCP in high bandwidth delay product network environment.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.22 no.2
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• pp.1-8
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• 2014

This paper introduces a new framework of predicting the arrival time of an aircraft by incorporating the probabilistic information of what type of trajectory pattern will be applied by human air traffic controllers. The proposed method is based on identifying the major patterns of vectored trajectories and finding the statistical relationship of those patterns with various traffic complexity factors. The proposed method is applied to the traffic scenarios in real operations to demonstrate its performances.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.90-93
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• 2022

좁은 ITS(Intelligent Transportation Systems) 대역에서는 채널 혼잡을 피하는 것이 필수적이다. 눈에 띄는 변화가 있을 때만 차량 운동을 보고하는 것은 대역폭 사용을 줄이기 위한 표준화된 접근 방식이다. 그러나 셀룰러 V2X(Vehicle-to-Everything) 통신에서 주기적인 비콘의 빈번한 누락으로 인한 비주기성은 자원 낭비와 자원 스케쥴링의 안정성 문제를 제기한다. 이에 대해 이 논문에서는 자동차의 운동이 물리적 특성에 의해 제약을 받기 때문에 딥러닝 기반 체계로 대부분의 메시지 생성 시간을 정확하게 예측할 수 있다는 것을 보여준다. 제안된 예측 방법은 통상적인 도로주행 시 94.9%의 정확도를 달성한다.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.773-794
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• 2021

Prediction of vehicle traffic volume is very important in planning municipal administration. It may help promote social and economic interests and also prevent traffic congestion costs. Traffic volume as a time-varying trajectory is considered as functional data. In this paper we study three functional regression models that can be used to predict an unseen trajectory of traffic volume based on already observed trajectories. We apply the methods to highway tollgate traffic volume data collected at some tollgates in Seoul, Chuncheon and Gangneung. We compare the prediction errors of the three models to find the best one for each of the three tollgate traffic volumes.

• Journal of Korean Society of Transportation
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• v.29 no.1
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• pp.71-79
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• 2011

Traditionally, a dynamic network model is considered as a tool for solving real-time traffic problems. One of useful and practical ways of using such models is to use it to produce and disseminate forecast travel time information so that the travelers can switch their routes from congested to less-congested or uncongested, which can enhance the performance of the network. This approach seems to be promising when the traffic congestion is severe, especially when sudden incidents happen. A consideration that should be given in implementing this method is that travel time information may affect the future traffic condition itself, creating undesirable side effects such as the over-reaction problem. Furthermore incorrect forecast travel time can make the information unreliable. In this paper, a network-wide travel time prediction model under incidents is developed. The model assumes that all drivers have access to detailed traffic information through personalized in-vehicle devices such as car navigation systems. Drivers are assumed to make their own travel choice based on the travel time information provided. A route-based stochastic variational inequality is formulated, which is used as a basic model for the travel time prediction. A diversion function is introduced to account for the motorists' willingness to divert. An inverse function of the diversion curve is derived to develop a variational inequality formulation for the travel time prediction model. Computational results illustrate the characteristics of the proposed model.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.99-102
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• 2001

It has well known that the congestion of traffic and it's distribution. There are very important problems in the traffic control systems. In this paper, we will purpose an ITFS(Intelligent Traffic Forecasting System) which can determine the car classes and transport them to ITS(Intelligent Traffic control System). The system will be used the Inductive Loop Detector(ILD)and the Fuzzy logic and shown the effectiveness by the computer simulation.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.11a
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• pp.429-432
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• 2003

최근 TCP와 유사하게 멀티미디어 데이터를 전송하기 위한 rate control 프로토콜에 관한 연구가 활발하게 진행되고 있다. 본 논문에서는 멀티미디어 전송 방식에 있어서 TCP와 공정성을 유지하며 TCP와 유사하게 동작하도록 하는 TRCP 프로토콜을 제안한다. TRCP는 TCP vegas 이론을 응용하였으며 RTT와 패킷손실율에 의해 네트워크 혼잡 상태를 미리 예측하고 TCP의 AIMD 방식을 사용하여 TCP와 유사하게 전송율을.조절하는 프로토콜이다. 이 프로토콜에 대한 TCP와 공정성을 검증하기 위하여 시뮬레이션을 실시하고 그 결과를 분석한다.

• Proceedings of the KAIS Fall Conference
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• 2006.05a
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• pp.462-465
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• 2006

차가 증가함에 따라, 교통은 혼잡하게 되고, 교통 체증은 더욱 심화된다. 만약에, 교통 체증이나 도로의 속도를 이전의 통계를 이용하여 예측할 수 있다면 상당히 도움이 될 것이다. 본 논문은 다양한 종류의 도로 중 고속도로의 속도에 영향을 주는 요소를 분석하여 상호 영향을 주는 요소를 고찰한다. 이를 수행하기 위해 고속 도로 교통에 대한 데이터베이스를 구축하며, 도로 교통 데이터베이스에 교통 체증의 시간대의 가설을 적용하고, 다양한 데이터 마이닝의 연산을 사용하여 결과를 도출한다.