• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.6
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• pp.13-22
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• 2009

Recently, DSRC technology is used in the various fields such as parking system, BIS, ETC, etc. This paper suggests a traffic information system using this DSRC technology. The traffic information processing based on point detection using existing vehicle detection equipment is the system in which a collection and a service are operated separately while the traffic information system based on the link detection using DSRC is able to collect and provide the traffic information through the communication between RSE and OBU. The speed of a traffic congestion is high on the process converted from a point passing speed to a link average speed because the vehicle detection equipment makes the link traffic information into the point information. When the condition of traffic is deteriorated, traffic speed of the vehicle detection equipment becomes higher than DSRC. Especially, in this system, deflection by data of the traffic speed of the traffic information system is much decreased, and the unexpected condition detection and traffic condition are provided promptly.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.2
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• pp.1-11
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• 2014

In this paper, travel speed patterns were deducted based on historical DSRC travel speed data using Decision Tree technique to improve availability of the massive amount of historical data. These patterns were designed to reflect spatio-temporal vicissitudes in reality by generating pattern units classified by months, time of day, and highway sections. The study area was from Seoul TG to Ansung IC sections on Gyung-bu highway where high peak time of day frequently occurs in South Korea. Decision Tree technique was applied to categorize travel speed according to day of week. As a result, five different pattern groups were generated: (Mon)(Tue Wed Thu)(Fri)(Sat)(Sun). Statistical verification was conducted to prove the validity of patterns on nine different highway sections, and the accuracy of fitting was found to be 93%. To reduce travel pattern errors against individual travel speed data, inclusion of four additional variables were also tested. Among those variables, 'traffic condition on previous month' variable improved the pattern grouping accuracy by reducing 50% of speed variance in the decision tree model developed.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.04b
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• pp.1435-1438
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• 2002

미래의 산업으로 부각되고 있는 지능형교통시스템(ITS: Intelligent Transport Systems)은 실시간 데이터의 원활한 전송을 위하여 차량과 노변장치 간의 고속 무선통신 방식인 단거리전용통신(DSRC) 방식을 적용하고 있다. 그러나, DSRC 방식은 실시간 환경에 적합한 축약된 3계층(물리, 데이터링크, 응용계층) 구조로 이뤄져 있으며, 통신영역간에 로밍이나 핸드오프 기능이 결여되어 있어 일반적인 데이터서비스를 제공하기에는 여러 가지 문제점을 안고 있다. 이에, 본 연구에서는 이러한 문제점을 분석하고, DSRC 상에서 효율적인 데이터서비스를 제공할 수 있는 이동에이전트 아키텍처를 제안하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.5 no.3 s.11
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• pp.1-12
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• 2006

Nowdays ITS is being installed in each local autonomous entity, and BIS installation and operation is prior to other ITS sub systems for the public service. The methods of positioning md wireless communication in BIS are DSRC+DSRC, GPS+wireless communication, Beacon+Beacon, which are chosen and operated as the local features. Before this study, the before and after survey of BIS' quality have only been done without performance evaluation of BIS. So the method of BIS' evaluation have been established including performance test in this paper. And the evaluation of some BIS' reliability and efficiency have been done using the reliability of arriving data and the wireless communication response rates after choosing typical BIS' sub system.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.6
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• pp.25-33
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• 2014

This study investigates the reliability of DSRC-based traffic information system on the typical urban arterial with the minimum sample size method. VISSIM has been employed to calculate the required sample size. After comparing the number of hi-pass vehicles recorded from DSRC and the required sample size, this study found that the interrupted traffic state tends to generate more outliers than the uninterrupted one, the lack of the number of vehicles completely passing links with multiple driveways makes it difficult to estimate the reliable traffic information, the traffic information during peak hour is relatively more reliable than that during off-peak hour, and the reliability of DSRC-based traffic information system depends on the significance level in calculating the sample size. The driveway density and traffic signal operation due to the individual link length significantly affects the required sample size, resulting in determining the reliability of the DSRC-based traffic information system.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2465-2471
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• 2013

This paper develops a travel time prediction algorithm that can be used for real-time application. The algorithm searches for the most similar pattern in historical travel time database as soon as a series of real-time data become available. Artificial neural network approach is then taken to forecast travel time in the near future. To examine the performance of this algorithm, travel time data from Gyungbu Highway were obtained and the algorithm is applied. The evaluation shows that the algorithm could predict travel time within 4% error range if comparable patterns are available in the historical travel time database. This paper documents the detailed algorithm and validation procedure, thereby furnishing a key to generating future travel time information.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.6 no.3
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• pp.174-182
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• 2007

There are not many studies on the maintenance and replacement for the ITS equipments. Most of ITS center has no comprehensive regulation on the equipment replacement. This study was focusing on estimation of equipment replacement period and the number of spare parts in stock using the actual failure data of Road Side Equipment (RSE) by Dedicated Short Range Communication (DSRC). The failure data showed a type of bath-tub curves. The data, however, did not fit to any probability distribution curve, which means that the preventive replacement cannot be strongly applied for the RSE. In the aspect of practical strategy, this study suggest that repairing cost and failure frequency be used for decision of replacement of RSE after the 1 or 2 year warrant period. The future study needs to include more RSE failure data as well as other equipments of the ITS.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.1
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• pp.79-86
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• 2013

In this paper which it is relating to the DSRC system based road side equipment, we propose the protocol stack architecture of road side equipment and the process structure of the main RM, L7 and LLC layers which is road side equipment device as well. And also we design the signal flow and data-transfer process as well between road side equipment and on board equipment to describe the installation process between road side equipment and von board equipment based on DSRC system. Thus, it is possible to provide various application services between intelligent transportation systems of road side equipment and local server, as well as it enables the local server managing the memorys of on board equipment which entry in service area thru the road side equipment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.249-257
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• 2017

Filtering the data for travel time records obtained from DSRC probes is essential for a better estimation of the link travel time. This study addresses the major deficiency in the performance of TRANSGUIDE in removing anomalous data. This algorithm is unable to handle unstable traffic flow conditions for certain time intervals, where fluctuations are observed. In this regard, this study proposes an algorithm that is capable of overcoming the weaknesses of TRANSGUIDE. If TRANSGUIDE fails to validate sufficient number of observations inside one time interval, another process specifies a new validity range based on the median absolute deviation (MAD), a common statistical approach. The proposed algorithm suggests the parameters, ${\alpha}$ and ${\beta}$, to consider the maximum allowed outlier within a one-time interval to respond to certain traffic flow conditions. The parameter estimation relies on historical data because it needs to be updated frequently. To test the proposed algorithm, the DSRC probe travel time data were collected from a multilane highway road section. Calibration of the model was performed by statistical data analysis through using cumulative relative frequency. The qualitative evaluation shows satisfactory performance. The proposed model overcomes the deficiency associated with the rapid change in travel time.

• Journal of Korean Society of Transportation
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• v.35 no.1
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• pp.63-78
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• 2017

The purpose of this study is to determine the optimal aggregation interval to increase the reliability when estimating representative value of individual vehicle travel time collected by DSRC equipment in interrupted traffic flow section in National Highway. For this, we use the bimodal asymmetric distribution data, which is the distribution of the most representative individual vehicle travel time collected in the interrupted traffic flow section, and estimate the MSE(Mean Square Error) according to the variation of the aggregation interval of individual vehicle travel time, and determine the optimal aggregation interval. The estimation equation for the MSE estimation utilizes the maximum estimation error equation of t-distribution that can be used in asymmetric distribution. For the analysis of optimal aggregation interval size, the aggregation interval size of individual vehicle travel time was only 3 minutes or more apart from the aggregation interval size of 1-2 minutes in which the collection of data was normally lost due to the signal stop in the interrupted traffic flow section. The aggregation interval that causes the missing part in the data collection causes another error in the missing data correction process and is excluded. As a result, the optimal aggregation interval for the minimum MSE was 3~5 minutes. Considering both the efficiency of the system operation and the improvement of the reliability of calculation of the travel time, it is effective to operate the basic aggregation interval as 5 minutes as usual and to reduce the aggregation interval to 3 minutes in case of congestion.