• 제목/요약/키워드: traffic congestion

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항계내 항로의 해상교통 혼잡도 평가에 관하여 - 울산 신항만의 혼잡도 평가를 기준으로 - (Evaluation of Traffic Congestion in Channels within Harbour Limit -On Channels in Ulsan New Port Development-)

  • 구자윤
    • 한국항만학회지
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    • 제11권2호
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    • pp.173-189
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    • 1997
  • Whether over taking or parallel sailing of two or more vessels is allowable on marine traffic route or not, the traffic congestion due to traffic volume has to be evaluated separately. In Gaduk-sudo, overtaking or parallel sailing is so allowable that the Bumper Model is introduced to evaluated the traffic congestion. But the channels within the habour limit such as the route of Ulsan New Port are so prohibited overtaking or parallel sailing that the traffic congestion has to be evaluated by using theoretical traffic capacity or by traffic simulation. In this paper, the congestion of Southern New Port and Mipo Port was evaluated the congestion by using theoretical traffic capacity and the other area of Ulsan Port by traffic simulation. The incresed traffic volumes on Ulsan Channels according to Ulsan New Port Development in 2011 were evaluated to have no effect with the traffic congestion.

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위성 랜덤 액세스 채널에서 Bursty 트래픽의 신속한 전송을 위한 빠른 혼잡 제어 기법 (Fast Congestion Control to Transmit Bursty Traffic Rapidly in Satellite Random Access Channel)

  • 노홍준;이윤성;임재성;박형원;이호섭
    • 한국통신학회논문지
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    • 제39C권11호
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    • pp.1031-1041
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    • 2014
  • 본 논문은 복제 패킷을 사용하는 위성 랜덤 액세스 채널에서 bursty 트래픽을 안정적으로 전송하기 위한 트래픽 부하 제어 기법으로 FCC (Fast Congestion Control)을 제안한다. 위성 랜덤 액세스 채널에서 순간적으로 발생하는 bursty 트래픽은 그 양이 많을 경우 충돌 확률로 인하여 backlogged 트래픽이 다수 발생할 수 있다. FCC는 access probability를 통해 트래픽 부하를 제어하며, backlogged 트래픽의 양을 추정한다. 또한 backlogged 트래픽이 최대 처리량에 해당하는 트래픽 부하를 넘어설 경우 빠르게 congestion 상태로 전환한다. Congestion 상태에서는 backlogged 트래픽이 우선적으로 처리되며, 새로 유입되는 트래픽은 congestion 상태가 지속되는 동안 채널에 접속하지 않고 대기하다가 congestion 상태가 종료되는 시점에 채널에 유입된다. Congestion 상태에서 backlogged 트래픽은 신속한 전송을 보장받기 때문에 지연 시간이 단축된다. 따라서 FCC는 긴급성이 요구되는 군 트래픽에 매우 적합한 기술이다. 본 논문은 모의실험을 통해 기존 기법 대비 제안 기법의 우수성을 확인하였다.

Stochastic Traffic Congestion Evaluation of Korean Highway Traffic Information System with Structural Changes

  • Lee, Yongwoong;Jeon, Saebom;Park, Yousung
    • Asia pacific journal of information systems
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    • 제26권3호
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    • pp.427-448
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    • 2016
  • The stochastic phenomena of traffic network condition, such as traffic speed and density, are affected not only by exogenous traffic control but also by endogenous changes in service time during congestion. In this paper, we propose a mixed M/G/1 queuing model by introducing a condition-varying parameter of traffic congestion to reflect structural changes in the traffic network. We also develop congestion indices to evaluate network efficiency in terms of traffic flow and economic cost in traffic operating system using structure-changing queuing model, and perform scenario analysis according to various traffic network improvement policies. Empirical analysis using Korean highway traffic operating system shows that our suggested model better captures structural changes in the traffic queue. The scenario analysis also shows that occasional reversible lane operation during peak times can be more efficient and feasible than regular lane extension in Korea.

과거 교통정체 패턴을 이용한 현재의 교통정체 변화 판별 알고리즘 (An Algorithm for Identifying the Change of the Current Traffic Congestion Using Historical Traffic Congestion Patterns)

  • 이경민;홍봉희;정도성;이지완
    • 정보과학회 컴퓨팅의 실제 논문지
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    • 제21권1호
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    • pp.19-28
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    • 2015
  • 본 논문에서는 과거 교통정체 패턴을 이용하여 현재의 교통정체가 풀리는 정체인지 아니면 악화되는 정체인지를 판별하는 알고리즘을 제안한다. 과거 교통정체 패턴은 다중 포인터를 이용하여 정체구간들을 연결한 인접 리스트에 교통정체의 시간적 길이와 공간적 길이로 저장된다. 교통정체가 시작된 구간에 해당하는 헤드노드를 탐색하고 현재패턴과 가장 유사한 과거 교통정체 패턴을 이용하여 장래의 교통정체 변화정보를 제공한다. 실험을 통해 검증한 결과, 도로 구간 하나에 대한 정체 변화를 판별하였을 때 실제 값과 비교해서 평균적으로 15분 오차를 보였으며, 연속된 다수의 도로 구간들을 결합하여 비교적 긴 구간의 정체 변화를 판별하였을 경우 평균적으로 10분 이내의 오차를 보이며 실제 값과 유사한 것을 보였다.

VDS 자료 기반 고속도로 교통혼잡비용 산정 방법론 연구 (Estimation of the Expressway Traffic Congestion Cost Using Vehicle Detection System Data)

  • 김상구;윤일수;박재범;박인기;천승훈;김경현;안현경
    • 한국도로학회논문집
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    • 제18권1호
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    • pp.99-107
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    • 2016
  • PURPOSES : This study was initiated to estimate expressway traffic congestion costs by using Vehicle Detection System (VDS) data. METHODS : The overall methodology for estimating expressway traffic congestion costs is based on the methodology used in a study conducted by a study team from the Korea Transport Institute (KOTI). However, this study uses VDS data, including conzone speeds and volumes, instead of the volume delay function for estimating travel times. RESULTS : The expressway traffic congestion costs estimated in this study are generally lower than those observed in KOTI's method. The expressway lines that ranked highest for traffic congestion costs are the Seoul Ring Expressway, Gyeongbu Expressway, and the Youngdong Expressway. Those lines account for 64.54% of the entire expressway traffic congestion costs. In addition, this study estimates the daily traffic congestion costs. The traffic congestion cost on Saturdays is the highest. CONCLUSIONS : This study can be thought of as a new trial to estimate expressway traffic congestion costs by using actual traffic data collected from an entire expressway system in order to overcome the limitations of associated studies. In the future, the methodology for estimating traffic congestion cost is expected to be improved by utilizing associated big-data gathered from other ITS facilities and car navigation systems.

Multivariate Congestion Prediction using Stacked LSTM Autoencoder based Bidirectional LSTM Model

  • Vijayalakshmi, B;Thanga, Ramya S;Ramar, K
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제17권1호
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    • pp.216-238
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    • 2023
  • In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

A real-time multiple vehicle tracking method for traffic congestion identification

  • Zhang, Xiaoyu;Hu, Shiqiang;Zhang, Huanlong;Hu, Xing
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제10권6호
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    • pp.2483-2503
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    • 2016
  • Traffic congestion is a severe problem in many modern cities around the world. Real-time and accurate traffic congestion identification can provide the advanced traffic management systems with a reliable basis to take measurements. The most used data sources for traffic congestion are loop detector, GPS data, and video surveillance. Video based traffic monitoring systems have gained much attention due to their enormous advantages, such as low cost, flexibility to redesign the system and providing a rich information source for human understanding. In general, most existing video based systems for monitoring road traffic rely on stationary cameras and multiple vehicle tracking method. However, most commonly used multiple vehicle tracking methods are lack of effective track initiation schemes. Based on the motion of the vehicle usually obeys constant velocity model, a novel vehicle recognition method is proposed. The state of recognized vehicle is sent to the GM-PHD filter as birth target. In this way, we relieve the insensitive of GM-PHD filter for new entering vehicle. Combining with the advanced vehicle detection and data association techniques, this multiple vehicle tracking method is used to identify traffic congestion. It can be implemented in real-time with high accuracy and robustness. The advantages of our proposed method are validated on four real traffic data.

Real-Time Road Traffic Management Using Floating Car Data

  • Runyoro, Angela-Aida K.;Ko, Jesuk
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제13권4호
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    • pp.269-276
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    • 2013
  • Information and communication technology (ICT) is a promising solution for mitigating road traffic congestion. ICT allows road users and vehicles to be managed based on real-time road status information. In Tanzania, traffic congestion causes losses of TZS 655 billion per year. The main objective of this study was to develop an optimal approach for integrating real-time road information (RRI) to mitigate traffic congestion. Our research survey focused on three cities that are highly affected by traffic congestion, i.e., Arusha, Mwanza, and Dar es Salaam. The results showed that ICT is not yet utilized fully to solve road traffic congestion. Thus, we established a possible approach for Tanzania based on an analysis of road traffic data provided by organizations responsible for road traffic management and road users. Furthermore, we evaluated the available road information management techniques to test their suitability for use in Tanzania. Using the floating car data technique, fuzzy logic was implemented for real-time traffic level detection and decision making. Based on this solution, we propose a RRI system architecture, which considers the effective utilization of readily available communication technology in Tanzania.

교통혼잡비용 추정방법의 개선방안 연구 (Improving the Estimation Method of Traffic Congestion Costs)

  • 조진환;황기연
    • 대한교통학회지
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    • 제28권1호
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    • pp.63-74
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    • 2010
  • 최근 들어 학계와 연구원 등에서 기존의 교통혼잡비용 산정 방식과 내용에 대한 수정 요구가 증대하고 있다. 이에 본 연구에서는 교통혼잡비용에 대해 살펴보고, 교통혼잡비용의 개선방안을 도출하였다. 개선방안으로 교통혼잡비용에 사회적 외부비용을 추정에 포함하는 방안, 온실효과비용, 환경오염 비용 등을 교통혼잡비용의 추정에 합산하는 방안, 교통혼잡비용의 산정방법에 비 반복정체의 문제, 혼잡판단 기준속도의 문제, 혼잡시간대의 추정 문제, 통행속도 문제 등에 대한 대안을 도출하였다. 본 연구에서 제시한 교통혼잡비용 추정 개선 방안이 여러 가지 현실적용에 있어서의 어려움이 있는 한계를 가지고 있지만 지속가능한 발전이라는 시대의 흐름에 맞는 교통혼잡비용의 변화의 기초 자료로 제공하였다는 사실에 의의가 있다.

고속도로 소통상태지수 개발에 관한 연구 (Development of a Traffic Condition Index (TCI) on Expressways)

  • 복기찬;이승준;최윤혁;강정규;이승환
    • 대한교통학회지
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    • 제27권5호
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    • pp.85-95
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    • 2009
  • 지속적인 도로건설에도 불구하고 이용차량의 급증으로 인해 고속도로의 혼잡은 계속 커져 가고 있다. 이를 해결하기 위한 물리적 용량 확대 즉, 도로의 신설, 확장에는 막대한 비용뿐만 아니라 용지확보의 어려움 등으로 장기의 건설기간이 소요되어 혼잡 해소 시까지 상당기간 동안 혼잡의 지속이 불가피하다. 따라서 도로 관리주체는 소통상태가 악화되기 이전에 소통상태에 대한 지속적인 모니터링을 통해 혼잡원인 분석 및 해결대안을 제시하여 적극적으로 혼잡에 대처하는 것이 요구된다. 소통상태의 모니터링을 위해서는 소통상태를 계량적으로 표현할 수 있는 지표가 필요하다. 따라서 본 연구에서는 등급(또는 설계속도)이 다른 도로에 대해 동일한 척도로 평가가 가능하고 소통원활, 혼잡 등의 상태를 구분할 수 있는 혼잡지표(종합소통지수, TCI)를 개발하였다. 또한 개발된 종합소통지수(TCI)는 고속도로 교통관리시스템(FTMS)과 같이 기 설치된 시스템으로부터 획득 가능한 교통데이터를 이용하여 혼잡도를 산출할 수 있기 때문에 기존의 FTMS를 기능적으로 보완할 수 있고 도로 관리주체가 혼잡관리에 쉽게 적용할 수 있는 장점을 지닌다. 한편, TCI의 적용성을 평가하기 위해 경부 및 서해안 고속도로에 적용하여 평균통행속도, TTI 및 TCI 결과 값을 비교하였는데, 활용도, 소통상태 표현 능력 등의 측면에서 TCI의 적용성이 우수한 것으로 나타났다.