• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.89-100
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• 2008

In an urban network controlled by traffic signals, there is an interaction between the signal timing and the routes chosen by individual road users. This study develops a bi level programming model for traffic signal optimization in networks with path based traffic assignment. In the bi level programming model, genetic algorithm approach has been proposed to solve upper level problem for a signalized road network. Path based traffic assignment using column generation technique which is proposed by M.H. Xu, is applied at the lower-level. Genetic Algorithm provieds a feasible set of signal timings within specified lower and upper bounds signal timing variables and feeds into lower level problem. The performance of this model is investigated in numerical experiment in a sample network. In result, optimal signal settings and user equilibrium flows are made.

• Spatial Information Research
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• v.13 no.3 s.34
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• pp.309-321
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• 2005

To analyze travel demand fur transportation policy and transportation planning, it is important to construct realistic and reliable traffic data. And it needs a user friendly system to demonstrate transportation problems in the transportation planning and transportation management aspect. Generally, to construct network for analysis and collection about social and economical data is a core of transportation planning model. However, it takes a lot of time and effect. To overcome this problem GIS is more effective and efficient in data processing, such as selecting, editing and visualizing, etc. However, it is an early stage to use CIS in the transportation problems. This paper shows a new GIS-T system. The system can give traffic information and plan transportation planning using GIS which has ability as spatial representation and spatial analysis. To build this system, we design interfaces that are able to communicate transportation package for analysis with GIS and manage network efficiently, such as editing and examination. And we also develop a module for traffic information processing to handle spatial data and add it on the system. The proposed system shows more realistic transportation network modeling because the system presents more effective conditions to analyze network. And it can be a tool that can analyze various transportation problems.

• Journal of Korean Society of Transportation
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• v.16 no.4
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• pp.127-138
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• 1998

본 논문에서는 기존의 교통정보 시스템의 단점을 보완하는 중앙집중형 교통정보 시스템의 구축에 관하여 설명한다. 이 시스템은 중앙의 교통정보센터에서 전자지도 및 교통정보를 관리한다. 따라서, 사용자는 전자지도 및 교통정보 갱신의 필요가 없어지며, 교통정보 수신장치를 통해서 경로 정보를 원하는 시기에 제공받는다. 교통정보 수신장치는, 휴대용 노트북과 개인휴대 통신기기인 Cellular Phone이나 PCS Phone이 결합된 형태로 구현함으로서 대중적으로 널리 보급될 수 있는 교통정보시스템을 구축한다. 본 논문에서는 정확한 교통정보의 생성을 위하여 실시간 교통정보를 저장하는 새로운 교통데이터베이스의 구조를 제시하고 직진,우회전,좌회전,유턴 차량의 흐름을 모두 표현할 수 있는 새로운 교통 네트워크 모델을 제시한다. 많은 수의 노드와 아크로 이루어진 교통 네트워크 내에서, 경로계획 시간이 길어지는 문제 해결 방법으로 전체를 여러 개의 지역으로 나누는 방법을 제시하고, 각 지역 내의 경로계획에는 교통데이터베이스를 참조하는 Dijkstra 알고리즘을 사용한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.01a
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• pp.125-126
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• 2014

센서 스트림 데이터는 센서 네트워크를 통해 수집되는 데이터로 실시간 처리를 요구하며, 연속적으로 끊임없이 발생하는 스트림 데이터이다. 이러한 스트림 데이터는 양이 방대하여 이를 저장하기가 매우 어려우며, 동시에 데이터를 검색하는 데에는 많은 시간이 소요된다. 본 논문에서는 센서 네트워크에서의 효율적인 스트림 데이터 처리 시스템을 제안한다. 이 시스템은 캐시테이블을 사용함으로써 데이터베이스에 최소화된 접근으로 데이터 스트림 관리 시스템의 성능을 개선하였다. 그리고 센서 네트워크에서 읽어 들여온 불완전 데이터를 효율적으로 정제하고 상위 단계로 전송한다.

• Proceedings of the CALSEC Conference
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• 2001.08a
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• pp.457-465
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• 2001

■ 필요성 1. 교통 혼잡으로 인한 물류비 상승으로 국가 경쟁력 약화 2. 기존 최단경로 알고리즘(Dijkstra, Floyd-Warshall 등)의 한계 3. 대규모 네트워크(NP-hard문제)→정보 사용자가 제시하는 시간 내에 정보를 제공 필요 4. 교통 제약(회전금지, U턴, U턴 금지, P-턴) → 실제 교통 상황과 순환 노드 고려가 필요 ■목적 1. 교통제약을 포함한 대규모 교통네트워크에서 실시간 교통정보를 바탕으로 제한된 시간 내에 차별화 된 다수 최적경로 산출 함 2. 유전자알고리즘(GA)을 적용한 동적경로안내시스템 개발(중략)

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1630-1636
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• 2022

The results of current method of traffic sign detection gets hindered by environmental conditions and the traffic sign's condition as well. Therefore, in this paper, we propose a method of improving detection performance of damaged traffic signs by utilizing Hopfield Network and Fuzzy Max-Min Neural Network. In this proposed method, the characteristics of damaged traffic signs are analyzed and those characteristics are configured as the training pattern to be used by Fuzzy Max-Min Neural Network to initially classify the characteristics of the traffic signs. The images with initial characteristics that has been classified are restored by using Hopfield Network. The images restored with Hopfield Network are classified by the Fuzzy Max-Min Neural Network onces again to finally classify and detect the damaged traffic signs. 8 traffic signs with varying degrees of damage are used to evaluate the performance of the proposed method which resulted with an average of 38.76% improvement on classification performance than the Fuzzy Max-Min Neural Network.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.11
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• pp.1927-1933
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• 2006

This paper proposes the implementation plan of information network for an E-Navigation. Firstly, It configures to the network system based on E-Navigation information network using information and communication technology. And then, it builds on the total port network and tuning up the maritime traffic support system. It will expect to the efficiency through information network between ship to ship related pen operator. Building of information network for the E-Navigation, it is to build on the wireless internet infrastructure for the port logistics and traffic safety.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.4
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• pp.765-773
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• 2009

Currently Telematics traffic information services have been various because we can collect real-time traffic information through Intelligent Transport System. In this paper, we proposed and implemented a short-term traffic information prediction model for giving to guarantee the traffic information with high quality in the near future. A Short-term prediction model is for forecasting traffic flows of each segment in the near future. Our prediction model gives an average speed on the each segment from 5 minutes later to 60 minutes later. We designed a Bayesian network for each segment with some casual nodes which makes an impact to the road situation in the future and found out its joint probability density function on the supposition of GMM(Gaussian Mixture Model) using EM(Expectation Maximization) algorithm with training real-time traffic data. To validate the precision of our prediction model we had conducted various experiments with real-time traffic data and computed RMSE(Root Mean Square Error) between a real speed and its prediction speed. As the result, our model gave 4.5, 4.8, 5.2 as an average value of RMSE about 10, 30, 60 minutes later, respectively.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.6
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• pp.34-42
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• 2014

This study is a fundamental research to determine the emergency evacuation roads considering road network function and connectivity. First of all, the functional aspects of the road networks are analyzed in the target area, Sejong city, by using degree centrality(DC) and closeness centrality(CC) from GIS based database. Secondly, how network connectivity makes a change in user's travel pattern and travel time and how it affects the whole network are analyzed using TransCAD simulation program. Finally, after performing cluster analysis of index, first and second emergency evacuation roads are determined by judging the characteristics of clusters. The results of this research will be helpful for making a decision to diminish secondary damages when confronting unexpected disasters.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.5
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• pp.38-48
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• 2017

Link attributes, such as speed, occupancy, and flow, are essential factors for transportation planning and operation. It is, therefore, one of the most important decision-making problems in intelligent transport system (ITS) to determine the optimal location of a sensor for collecting the information on link attributes. This paper aims to develop a model to determine the optimal location of a sensor to minimize the variability of traffic information on whole networks. To achieve this, a network sensor location model (NSLM) is developed to reflect discrete characteristics of data collection. The variability indices of traffic information are calculated based on the summation of diagonal elements of the variance-covariance matrix. To assess the applicability of the developed model, speed data collected from the dedicated short range communication (DSRC) systems were used in Daegu metropolitan area. The developed model in this study contributes to the enhancement of investment efficiency and the improvement of information accuracy in intelligent transport system (ITS).