• Journal of the Institute of Convergence Signal Processing
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• v.13 no.2
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• pp.106-112
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• 2012

Smart Highway is the intelligent highway that improves a traffic safety, reduces incidence of traffic accidents, and supports intelligent and convenient driving environment so that drivers can drive at high speeds in safety[1]. In order to implement the highway, it is required to gather a dangerous data such as obstacle, wild animal, disabled car, etc. To provide the situation information of the highway, it has been gathered traffic information using various sensors. However, this technique has problems such as the problems of various information gathering, lack of accuracy depending on weather conditions and limitation of maintenance. Therefore, in order to provide safe driving information to driver by gathering dangerous condition, radar system is needed. In this paper, we used a developing 34.5GHz RWR(Road Watch Radar) radar for gathering dangerous information and we verified performance of obstacle detecting and resolution through field test.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.133-141
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• 2021

The development and demand for the system that provides users with traffic information and efficient road use have continued. also, this system provides the basic technology of the Intelligent Transport System (ITS). The most used traffic information collection tools are Vehicle detectors (VDS) and short-range wireless communication (DSRC) on express way. In order to generate reliable traffic information, it is necessary to efficiently manage and utilize the collected data as well as high-quality traffic data collection and processing technology. In this study, traffic information collection·processing·provision systems were investigated, and analyze the current status and problems of traffic information collected through VDS. Based on this, we would like to present an improved collection algorithm that utilizes the Kalman filter for vehicle information measurement of VDS data. By using the algorithm of this study, it is possible to minimize the time delay of the estimated value as well as the noise removal that inevitably occurs during measurement.

• Journal of Korean Society of Transportation
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• v.18 no.1
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• pp.47-59
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• 2000

The purpose of this study is to develop travel-time estimation model using neural networks and prediction model using neural networks and kalman-filtering technique. The data used in this study are travel speed collected from inductive loop vehicle detection systems(VDS) and travel time collected from the toll collection system (TCS) between Seoul and Osan toll Plaza on the Seoul-Pusan Expressway. Two models, one for travel-time estimation and the other for travel-time Prediction were developed. Application cases of each model were divided into two cases, so-called, a single-region and a multiple-region. because of the different characteristics of travel behavior shown on each region. For the evaluation of the travel time estimation and Prediction models, two Parameters. i.e. mode and mean were compared using five-minute interval data sets. The test results show that mode was superior to mean in representing the relationship between speed and travel time. It is, however shown that mean value gives better results in case of insufficient data. It should be noted that the estimation and the Prediction of travel times based on the VDS data have been improved by using neural networks, because the waiting time at exit toll gates can be included for the estimation of travel time based on the VDS data by considering differences between VDS and TCS travel time Patterns in the models. In conclusion, the results show that the developed models decrease estimation and prediction errors. As a result of comparing the developed model with the existing model using the observed data, the equality coefficients of the developed model was average 88% and the existing model was average 68%. Thus, the developed model was improved minimum 17% and maximum 23% rather then existing model .

• Journal of Korean Society of Transportation
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• v.33 no.3
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• pp.256-267
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• 2015

The intelligent transportation systems allow us to have valuable opportunities for collecting wide-area coverage traffic data. The significant efforts have been made in many countries to provide the reliable traffic conditions information such as travel time. This study analyzes the impacts of the fog weather conditions on the traffic stream. Also, a strategy for predicting the long-term traffic speeds is developed under foggy weather conditions. The results show that the average of speed reductions are 2.92kph and 5.36kph under the slight and heavy fog respectively. The best prediction performance is achieved when the previous 45 pattern cases data is used, and the 14.11% of mean absolute percentage error(MAPE) is obtained. The outcomes of this study support the development of more reliable traffic information for providing advanced traffic information service.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.4 no.3 s.8
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• pp.33-43
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• 2005

Traffic condition monitoring system serves as the foundation for all intelligent transportation system operation. Loop detectors and Video Image Processing are the most widely common technology approach to condition monitoring in korea Highways. Lane Usage is defined as the proportion of total link volume served by each lane. In this research, the lane Usage(LU) of two lane link for one day. Interval is 56% : 44%. The LU of three lane link is 39% : 37% : 24%. The LU of four lane link is 25% : 29% : 26% : 21%. These analysis reveal that each lane distributions of link are not same. This research investigates the general concept of lane usage by using collected loop detector data and the investigated that lane distribution is different by traffic lane and lane usage is consistent by time of day.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.821-829
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• 2006

Traffic load comprise primary input to pavement design causing pavement damage. therefore it should be proceeded suitable traffic load distribution modeling for pavement design and analysis. Traffic load have been represented by equivalent single axle loads (ESALs) which convert mixed traffic stream into one value for design purposes. But there are some limit to apply ESALs to other roads because it is empirical value developed as part of the original AASHO(American Association of State Highway Officials) road test. There have been many efforts to solve these problems. Several leading country have implemented M-E(Mechanistic-Empirical) design procedures based on mechanical concept. As a result, they established traffic load quantification method using load distribution model known as Axle Load Spectra. This paper details Axle Load Spectra and presents axle load distribution model based on normal mixture distribution function using truck load data collected by WIM system installed in national highway. Axle load spectra and axle load distribution model presented in this paper could be useful for basic data when making traffic load quantification plan for pavement design, overweight vehicle permit plan and pavement maintenance cost plan.

• Journal of Korean Society of Transportation
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• v.18 no.4
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• pp.117-126
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• 2000

Conventional methods for collecting origin-destination trips have been mainly relied on the surveys of home or roadside interview. However, the methods tend to be costly, labor intensive and time disruptive to the trip makers, thus the methods are not considered suitable for Planning applications such as routing guidance, arterial management and information Provision, as the parts of deployments in Intelligent Transport Systems Motivated by the problems, more economic ways to estimate origin-destination trip tables have been studied since the late 1970s. Some of them, which have been estimating O-D table from link traffic counts are generally Entropy maximizing, Maximum likelihood, Generalized least squares(GLS), and Bayesian inference estimation etc. In the Paper, with user equilibrium constraint we formulate GLS problem for estimating O-D trips and develop a solution a1gorithm by using Genetic Algorithm, which has been known as a g1oba1 searching technique. For the purpose of evaluating the method, we apply it to Seoul inner ringroad and compare it with gradient method proposed by Spiess(1990). From the resu1ts we fond that the method developed in the Paper is superior to other.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.2
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• pp.92-104
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• 2017

Previous management for speed in road traffic system was aimed only to the improvement of mobility and safety. However, consideration for the aspect of environment and energy consumption efficiency was valued less than the former ones. Nevertheless, economical damage scope caused by climate change has been increasing and it is estimated that environmental value will be increased because of the change of external circumstances. In addition, policy for reducing carbon emission in transportation system was assessed as insufficient in improving the condition of traffic road since it only focused on the transition of private vehicle into public transportation and development of eco-friendly car. Now it is the time to prepare for the adaptation strategy and precaution for the increased number of private vehicle in Korea. For this, paradigm shift in traffic operation which includes the policy not only about the mobility but also about caring environment would be needed. It is needed to be able to monitor the actual amount of greenhouse gas in real time to reduce the amount of emitted greenhouse gas in the aspect of traffic management. In this research, a methodology which can build on-line greenhouse gas emission monitoring system by using real time traffic data and predicting the circumstance in next 5 minutes was suggested.

• Proceedings of the KAIS Fall Conference
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• 2010.05a
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• pp.400-403
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• 2010

도로상의 안개로 인한 시정감소는 교통사고를 유발하는 주된 원인이므로 전방의 운전자에게 도로의 시정거리를 미리 알려주어 안전운행을 유도하기 위한 안개경고시스템은 도로의 안전관리를 위해 매우 중요한 요소이다. 우리는 CCTV 카메라 영상에서 도로상에 통행중인 차량의 이동영역이 시정에 따라 달라진다는 점에 착안하여 이동영역을 추출하고 이를 이용하여 가시거리를 계산하는 시정 측정 장치를 개발하고 있으며, 주간, 야간 등 날씨의 변화에 덜 민감하면서도 효과적이고 정확한 이동영역의 추출은 시정측정을 위해 매우 중요하다. 본 논문에서는 이동영역의 추출을 위해 영상대비를 이용하여 자동으로 임계치를 결정하는 방법을 제안하며, 결정된 임계치를 적용시킴으로써 프레임간의 차영상으로 부터 잡음이 효과적으로 제거될 수 있음을 보인다. 또한, 차영상을 일정시간 누적시키는 방법을 통해 효과적으로 차량의 이동영역이 추출 되는 것을 보이기 위해 실제 고속도로에서 촬영된 CCTV 영상을 이용하여 실험한 결과를 제시한다.

• International Journal of Highway Engineering
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• v.10 no.3
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• pp.221-230
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• 2008

In Korea, Expressway and National Highway System has been continually managed by their own pavement management system. The pavement condition evaluation system has not been developed for the municipal roads except for Seoul city. Therefore, this study focuses on analyzing the characteristics of distress in major city's pavement and developing the pavement condition index for the municipal PMS. Panel rating and pavement condition survey for the selected pavement sections were conducted for developing pavement condition index. Municipal level pavement condition index(MPCI) was developed by statistical analysis. Also, a sensitivity analysis for each independent variable of the MPCI and comparison with other pavement condition indicies, such as SPI and HPCI, were performed.