• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.6
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• pp.116-126
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• 2016

In this study, lane-wise traffic flow characteristics were analysed on uninterrupted flow using a new notion of "lane-wise travel speed reversal (LTSR)" which is defined as a phenomena that travel speed in the median lane is lower than other lanes. Mathematical formulation was also proposed to calculate the strength of LTSR. The experiment road site is Seoul Outer Ring Expressway (Jayuro-IC~Jangsoo-IC), and travel trajectories for each four lane were collected for weekdays (Mon. through Fri.) during morning peak. Comparing lane-wise travel speeds for entire test road section, no LTSR was observed, meaning that the travel speed in the median lane is the fastest, followed by 2nd, 3rd, and 4th lane as in order. Howerver, the result of microscopic analysis using 100-meter discrete road section based data shows that LTSR occurs many times. Especially the strength of LTSR is higher in congestion area and freeway merge and diverge segment. It is expected that these results could be used as a fundamental data when establishing lane-by-lane traffic operation strategy and developing lane-wise traffic information collection and dissemination technology.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.42-60
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• 2024

This study analyzed the characteristics of mixed traffic flows with autonomous vehicles on highway weaving sections and assessed the safety of vehicle-following pairs based on surrogate safety indicators. The intelligent driver model (IDM) was utilized to emulate the driving behavior of autonomous vehicles, and the weaving sections were divided into lengths of 300 and 600 meters for analysis within a micro-traffic simulation (VISSIM). Although significant differences were found in the average speed, density, and headway between the two sections through t-test results, no significant differences were observed when comparing the number of conflicts per indicator and the vehicle-following pair. Four safety indicators were selected for the mixed traffic evaluation based on their ability to represent risk levels similar to those perceived by drivers. The safety analysis, based on the selected four indicators, determined that autonomous vehicles following other autonomous vehicles were the safest pairing. Future research should focus on integrating these indicators into a single comprehensive index for analysis.

• The Journal of the Korea Contents Association
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• v.8 no.10
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• pp.76-87
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• 2008

Prediction of travel time on road network is one of crucial research issue in dynamic route guidance system. A new approach based on Rule-Based classification is proposed for predicting travel time. This approach departs from many existing prediction models in that it explicitly consider traffic patterns during day time as well as week day. We can predict travel time accurately by considering both traffic condition of time range in a day and traffic patterns of vehicles in a week. We compare the proposed method with the existing prediction models like Link-based, Micro-T* and Switching model. It is also revealed that proposed method can reduce MARE (mean absolute relative error) significantly, compared with the existing predictors.

• Journal of Korean Society of Transportation
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• v.27 no.2
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• pp.207-214
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• 2009

This study proposed an advanced warning information system based on real-time traffic conflict analysis. An algorithm to detect and analyze unsafe traffic events associated with car-following and lane-changes using individual vehicle trajectories was developed. A positive guidance procedure was adopted to provide warning information to alert drivers to hazardous traffic conditions derived from the outcomes of the algorithm. In addition, autoregressive integrated moving average (ARIMA) analyses were conducted to investigate the predictability of warning information for the enhancement of information reliability.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.208-221
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• 2020

The objective of this study is to estimate and analyze the traffic density of continuous flow using the trajectory of individual vehicles and the headway of sample probe vehicles-front vehicles obtained from ADAS (Advanced Driver Assitance System) installed in sample probe vehicles. In the past, traffic density of continuous traffic flow was mainly estimated by processing data such as traffic volume, speed, and share collected from Vehicle Detection System, or by counting the number of vehicles directly using video information such as CCTV. This method showed the limitation of spatial limitations in estimating traffic density, and low reliability of estimation in the event of traffic congestion. To overcome the limitations of prior research, In this study, individual vehicle trajectory data and vehicle headway information collected from ADAS are used to detect the space on the road and to estimate the spatiotemporal traffic density using the Generalized Density formula. As a result, an analysis of the accuracy of the traffic density estimates according to the sampling rate of ADAS vehicles showed that the expected sampling rate of 30% was approximately 90% consistent with the actual traffic density. This study contribute to efficient traffic operation management by estimating reliable traffic density in road situations where ADAS and autonomous vehicles are mixed.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.29-44
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• 2009

A variety of research efforts, using advanced wireless communication technologies, have been made to develop more reliable traffic information system. This study presents a novel decentralized traffic information system based on vehicle infrastructure integration (VII). A major objective of this study was also to devise a methodology for determining appropriate spacing of roadside equipment (RSE) to fully exploit the benefits of the proposed VII-based traffic information system. Evaluation of travel time estimation accuracy was conducted with various RSE spacings and the market penetration rates of equipped vehicle. A microscopic traffic simulator, VISSIM, was used to obtain individual vehicle travel information for the evaluation. In addition, the ANOVA tests were conducted to draw statistically significant results of simulation analyses in determining the RSE spacing. It is expected that the proposed methodology will be a valuable precursor to implementing capability-enhanced next generation traffic information systems under the forthcoming ubiquitous transportation environment.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.4
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• pp.68-80
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• 2016

This study proposes the traffic prediction and optimal traffic control system based on cell transmission model and genetic algorithm in cloud environment. The proposed prediction and control system consists of four parts. 1) Data preprocessing module detects and imputes the corrupted data and missing data points. 2) Data-driven traffic prediction module predicts the future traffic state using Multi-level K-Nearest Neighbor (MK-NN) Algorithm with stored historical data in SQL database. 3) Online traffic simulation module simulates the future traffic state in various situations including accident, road work, and extreme weather condition with predicted traffic data by MK-NN. 4) Optimal road control module produces the control strategy for large road network with cell transmission model and genetic algorithm. The results show that proposed system can effectively reduce the Vehicle Hours Traveled upto 60%.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.413-416
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• 2005

본 본문에서는 여러 가지 카오스 방정식을 자율 이동 로봇에 내장할 수 있는 카오스 이동 로봇의 하드웨어를 구현하였다. 이 카오스 로봇은 로봇 주행이 다양한 곡면의 카오스 궤적을 가지고 주행 또는 탐색할 수 있도록 여러 종류의 카오스 회로 즉 Chua's 회로, Lorenz 회로, 하이퍼카오스 회로 등을 카오스 로봇에 내장하도록 설계되어 있도록 설계되어 있다.

• 한국IT서비스학회:학술대회논문집
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• 2008.11a
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• pp.490-493
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• 2008

최근 텔레매틱스 기술은 운전자와 차량, 차량과 외부의 정보들을 이용하여 다른 산업과의 융합이 급속히 진행되고 있으며, 다양한 서비스에 대한 요구 역시 점차 증가하는 추세이다. 최근에는 차량의 위치 및 궤적 데이터 정보 서비스뿐만 아니라 차량을 통한 차별화된 고객별 영업, 마케팅 및 서비스 제공에 대한 VRM이 대두되고 있다. VRM은 차량의 주행 정보, 과거 이동정보, 운전자의 정보 등과 같은 차량에 관련된 다양한 정보에서 특정 규칙과 추출된 패턴 정보를 기반으로 영업, 서비스 생산, 마케팅 전략을 수립 및 적용할 수 있도록 해주는 것으로 CRM과 유사하다고 할 수 있다. 따라서 본 논문에서는 기존의 CRM의 성공 요인에 대한 선행 연구를 검토해 VRM의 전략적 관점에서 프로세스에 준거하여 고객자산가치관리와 VRM의 핵심기능인 고객자산가치 관리역량을 통해 차량의 주행정보, 차량 정검 정보, 운전자의 정보 등과 같은 차량에 관련된 모든 정보를 VRM 프레임워크가 갖추어야 할 요소를 도출하고 텔레매틱스 기반 VRM 프레임워크 모델을 제안해보고자 한다.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.7
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• pp.155-162
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• 2003

We propose a new technique far real-tine controller design of a autonomous cruise mobile robot with three drive wheels. The proposed control scheme uses a Caussian function as a unit function in the fuzzy neural network. and a back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-foray. The control performance of the proposed controller is illustrated by performing the computer simulation for trajectory tracking of the speed and azimuth of a autonomous cruise mobile robot driven by three independent wheels.