• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.568-571
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• 2007

본 연구에서는 차량검지시스템을 이용하여 메타데이터의 검지시스템의 하드웨어와 소프트웨어가 도로에 적합한 시스템이 되도록 실험을 통하여 이를 검증하고, 표준화를 위해 제안하였다. 첫째, 교통정보를 메타데이터하고자 할 때 그 구축범위가 너무 광범위한 점을 보완하여 시내 교통정보의 범위를 줄이고자 적용범위로 VDS(차량검지시스템 : Vehicle Detection System)의 메타데이터 구성요소에 대한 표준을 마련하였다. 둘째, 본 표준에서는 차량의 속도, 단위 시간당 차량의 통과수 및 점유시간 등을 조사할 수 있는 VDS의 메타데이터 구성요소를 추출하고 데이터 요소에 대한 정의 및 기술형식을 정의하였다. 셋째, VDS의 메타데이터 표준지정은 교통정보의 데이터 요소 및 형식을 동일하게 사용하도록 함으로써 상이한 개발업체에 의해 개발된 시스템의 교통정보를 일관성 있게 표현할 수 있다. 넷째, 본 표준은 VDS 메타데이터의 데이터 요소명을 표준화하기 위해서 Data Dictionary를 구축하여 실제 데이터 요소에서 data dictionary에서 정의된 약어들의 조합으로 표시하여 데이터 모델링시 유용하게 쓰일 수 있도록 하였다.

• 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.25 no.6
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• pp.79-88
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• 2007

Design Hourly Volume (DHV) is the hourly volume used for designing a section of road. DHV is also used to estimate the expected number of vehicles to pass or traverse the relevant section of road in a future target year. The Design Hour Factor (DHF) is defined as the ratio of DHV to Average Annual Daily Traffic (AADT). In addition to high precision of predicted traffic volume, in order to design a roadway to be the proper scale, applying appropriate DHFs considering traffic flow characteristics and type of area which surrounds the relevant roadway is important. This study categorizes sections of expressway (Suh Hae An Expressway) according to their area type and estimates DHFs utilizing traffic data obtained from a vehicle detection system (VDS). This study shows that DHFs calculated using VDS data are different from those using traffic data acquired from a coverage survey. While AADTs from both data show similar values, peak hour volumes from both data show significant differences especially for recreational areas. DHFs from the coverage survey are quite different from the values provided by the Korean design guide or previous research results and DHFs for urban areas are higher than recreational areas. However, DHFs from VDS shows similar values to previous research results. The result of this study suggests that using VDS for estimating DHFs is more reliable than using a coverage survey.

• Journal of Korean Society of Transportation
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• v.33 no.1
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• pp.14-28
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• 2015

Numerous research has been conducted using internet search data since the mid-2000s. For example, Google Inc. developed a service predicting influenza patterns using the internet search data. The main objective of this study is to prove the hypothesis that highway traffic indices are similar to the internet search patterns. In order to achieve this objective, a model to predict the number of vehicles entering the expressway and space-mean speed was developed and the goodness-of-fit of the model was assessed. The results revealed several findings. First, it was shown that the Google search traffic was a good predictor for the TCS entering traffic volume model at sites with frequent commute trips, and it had a negative correlation with the TCS entering traffic volume. Second, the Naver search traffic was utilized for the TCS entering traffic volume model at sites with numerous recreational trips, and it was positively correlated with the TCS entering traffic volume. Third, it was uncovered that the VDS speed had a negative relationship with the search traffic on the time series diagram. Lastly, it was concluded that the transfer function noise time series model showed the better goodness-of-fit compared to the other time series model. It is expected that "Big Data" from the internet search data can be extensively applied in the transportation field if the sources of search traffic, time difference and aggregation units are explored in the follow-up studies.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.12
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• pp.1857-1863
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• 2012

The VDS(Vehicle Detection System) collects and transfers information about traffic situations in real time, therefore it makes the traffic management effective. Recently, the VDSs have provided good stability and accuracy in regard to system reliability and functions but they also have showed problems such as raising costs and consuming times when a new system is installed and/or the environmental requirements for the system are set up. The reason of the problems is that up to now the collection of the data and information about the traffic situations has been achieved by the 1:1 information exchange between the traffic control surveillance center and the each traffic field, between equipments and centers, and among data processing equipments and also centers. The communication systems used in the VDS are generally composed of 1 : 1 connection of the lines because the communication protocols are different in the most of the cases mentioned above. Consequently, this makes the number of communication lines become larger and causes the cost for the whole traffic information systems to increase. In this paper, a development of a controller to unify the communication protocols for the VDS is peformed to solve the problems which were mentioned above. Specially, the controller developed in this paper was applied to a radar vehicle detector and tested to show its usefulness. In addition to that, the developed controller was also designed to include functions to transfer the information about weather conditions on the roads.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.3
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• pp.17-28
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• 2024

In this study, the capacity of the tunnel and the general section was calculated and compared using the VDS detector data, and the decrease rate in capacity of the tunnel section was analyzed by tunnel type. To compare the capacity of the tunnel and the general section, the Product Limit Method (PLM) was applied to the VDS detector data. As a result of comparing the capacity of the tunnel and general section, the capacity of the tunnel section decreased by about 6.5% compared to the general section. To classify the tunnel type, the tunnel extension and the number of lanes were used as variables, and there was a difference in the decrease rate of capacity by tunnel group classified by each criterion.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.6 no.2
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• pp.1-14
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• 2007

The purpose of this research is to investigate service item to develop archived VDS(Vehicle Detecting System) data user services. Through the review of related studies and literature and investigation of the current application status of the vehicle detector data, the service item from the historical detector data were identified. The relative importance of the identified service item was measured based on the application purpose, usage and frequency of application.

• International Journal of Highway Engineering
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• v.18 no.1
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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.

• International Journal of Highway Engineering
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• v.10 no.1
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• pp.155-164
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• 2008

It is critical to develop a core ITS technology such as real-time travel time estimation in order that the efficient use of the ITS implementation can be achieved as the ITS infrastructure and relevant facilities are broadly installed in recent years. The provision of travel time information in real-time allows travellers to make informed decisions and hence not only the traveller's travel utilities but also the road utilization can be maximized. In this paper, a hybrid model is proposed to combine VDS and AVI which have different characteristics in terms of space and time dimensions. The proposed model can incorporate the immediacy of VDS data and the reality of AVI data into one single framework simultaneously. In addition, the solution algorithm is made to have no significant computational burden so that the model can be deployable in real world. A set of real field data is used to analyze the reliability and applicability of the proposed model. The analysis results suggest that the proposed model is very efficient computationally and improves the accuracy of the information provided, which demonstrates the real-time applicability of the proposed model. In particular, the data fusion methodology developed in this paper is expected to be used more widely when a new type of traffic data becomes available.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.1005-1008
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• 2011

본 논문에서는 교통 이력자료의 시공간 데이터를 활용하여 교통 분석 및 예측에 필요한 신뢰성 높은 데이터를 제공하기 위한 TCS/HI-PASS 전처리 알고리즘을 제안한다. 시공간 데이터의 전처리 알고리즘은 각종 교통정보에 이용되고 있으며, 그 중 대표적으로 활용되고 있는 것이 차량 검지기(VDS)를 통해 수집된 교통량, 속도, 점유율 정보이다. 이러한 정보에 가공처리 알고리즘을 적용하여 공간평균속도 기반의 통행시간을 산정하고 있으며, 고속도로 통행료 수납시스템(TCS)으로 부터는 출발영업소와 도착영업소의 진 출입시간을 기반으로 평균통행시간을 산정하고 있다. 본 연구에서는 차량 검지기(VDS) 데이터와 기존 TCS 데이터의 전처리 알고리즘을 분석하여 TCS와 HI-PASS 데이터 기반의 개선된 전처리 알고리즘을 설계, 구현하였다.