• Journal of Advanced Navigation Technology
• /
• v.5 no.1
• /
• pp.62-73
• /
• 2001

In this paper, performance of DSRC systems is analyzed with considering the real roads and height of vehicles. The channels are modeled as 2-Ray and 4-Ray with a direct wave and reflected waves by a road and buildings in a physical layer because DSRC keeps LOS propagation characteristics, and the pass loss for each model is calculated respectively. Rician factor is obtained through the calculated path loss on two models for DSRC, and the performance of the systems is analyzed in AWGN and Rician fading channels, Impulsive noise and Rician fading channels respectively. As a result, in Rician fading channels with impulsive noise(A=0.2, ${\Gamma}^{\prime}=0.22$), BER is below $10^{-6}$ when the distance is farther than 80[m] and 40[m] in 2-Ray model and 4-Ray model respectively. For performance improvement, BCH coding scheme and MRC diversity scheme are adopted.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.34 no.5
• /
• pp.1535-1545
• /
• 2014

This study aims at improving the Reliability of DSRC Traffic information considering Traffic and Road Characteristics. First of all, this study analyzed the characteristics of DSRC data on urban expressway and problems of outlier data occurrence. After then, this study produced reliable traffic information by using an optimal method of the Outlier-Filtering. After Outlier-Filtering, this study performed accuracy evaluation and appropriateness check for the number of samples per confidence level. As a result, it showed that the MAPE was between 2.2% and 9.7% and RSME was between 2.2 and 7.5 which are very similar figures to the actual average traffic speed. Also, The samples of both Am peak and Pm peak periods were analyzed to be appropriate at the confidence level of 95%, and 90% within the allowable error range of 5kph.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.16 no.6
• /
• pp.29-41
• /
• 2017

In this study, we used individual trip data to estimate a macroscopic fundamental diagram (MFD) that relates flow (or production) to density (or state) in Daegu metropolitan city. The individual trip data were generated by processing data that were collected from DSRC-based (dedicated short range communication) traffic data collection system. Using the processed individual trip data, we first examined whether the assumptions for MFD are valid, and then the relation between outflow and accumulation was estimated in our study site. As a result, we found that i) the assumptions are valid to construct MFD; and ii) the reproducible and well-defined MFDs exist in the network level.

• Transportation Technology and Policy
• /
• v.10 no.3
• /
• pp.21-25
• /
• 2013

• 한국ITS학회:학술대회논문집
• /
• 2002.11a
• /
• pp.59-62
• /
• 2002

• 전자공학회논문지 IE
• /
• v.46 no.4
• /
• pp.1-7
• /
• 2009

The final goal of ITS and ETC will be to improve the traffic efficiency and mobile safety without new road construction. DSRC system is emerging nowadays as a solution of them. China DSRC standard which was released in May 2007 has low bit rate, short message and simple MAC control. The DSRC system users want a long lifetime over 1 year with just one battery. In this paper, we propose the SoC of very low power consumption architecture. Several digital logic concept and analog power control logics were used for very low power consumption. The SoC operation mode and clock speed, operation voltage range, wakeup signal detector, analog comparator and Internal Voltage Regulator & External Power Switch were designed. We confirmed that the SoC power consumption is under 8.5mA@20Mhz, 0.9mA@1Mhz in active mode, and under 5uA in power down mode, by computer simulation. The design of SoC was finished on Aug. 2008, and fabricated on Nov. 2008 with $0.18{\mu}m$ CMOS process.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.34 no.6
• /
• pp.1851-1861
• /
• 2014

This study aims to calculate reliable sectional travel speeds with the consideration of the characteristics of the probe data collected in the interrupted traffic flow. First, in order to analysis the characteristics of the probe data, we looked into the distribution of the sectional travel times of each probe vehicle and compared the difference in the sectional travel speeds of each probe vehicle collected by DSRC. As a result, it is shown that outliers should be removed for the distribution of the sectional travel times. However, The comparison results show that the sectional travel speeds from the DSRC probe vehicles are not significantly different. Finally, based on the distribution characteristics of the sectional travel speeds of each probe vehicle and the representative values counted during a collection period, we drew the optimal outlier removal procedure and evaluated the estimation errors. The evaluation results showed that the DSRC sectional travel speeds were found to be similar to the observed values from actually running vehicles. On the contrary, in the case of the sectional travel speeds of intra-city bus, it was analyzed that they were less accurate than the DSRC sectional travel speeds. In the future, it will be necessary to improve BIS process and make use of the travel information on intra-city buses collected in real time to find various ways of applying it as traffic information.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.34 no.6
• /
• pp.1873-1879
• /
• 2014

There are various methodologies to forecast the travel time using real-time data but the K-nearest neighborhood (KNN) method in general is regarded as the most one in forecasting when there are enough historical data. The objective of this study is to evaluate applicability of KNN method. In this study, real-time and historical data of toll collection system (TCS) traffic flow and the dedicated short range communication (DSRC) link travel time, and the historical path travel time data are used as input data for KNN approach. The proposed method investigates the path travel time which is the nearest to TCS traffic flow and DSRC link travel time from real-time and historical data, then it calculates the predicted path travel time using weight average method. The results show that accuracy increased when weighted value of DSRC link travel time increases. Moreover the trend of forecasted and real travel times are similar. In addition, the error in forecasted travel time could be further reduced when more historical data could be available in the future database.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2013.07a
• /
• pp.15-18
• /
• 2013

본 논문에서는 교통상황을 실시간으로 반영하여 최적의 이동경로를 제공하는 적응형 내비게이션 시스템을 제안한다. 본 시스템은 세 가지 요소로 구성되어있다. 첫 번째로 도로교통망 중 한 구획의 도로상황정보를 수집하고 공유하는 Traffic Control Center (TCC). 두 번째로 개별적인 도로나 교차로에 설치되어 차량으로부터 도로상황정보를 수집하는 Road Side Unit(RSU). 마지막으로 차량들 간의 망 형성을 위해서 사용되는 Dedicated Short Range Communications(DSRC)를 기반으로 차량에 설치된 단말기가 있다. DSRC를 기반으로 RSU와의 통신하는 단말기는 실시간 도로교통정보를 기반으로 운전자에게 최적의 경로를 제공한다. 이동속도와 같은 교통정보는 단말기에서 측정되고, RSU로 전송된다. RSU는 이 정보를 처리하여 해당 도로의 도로상황지수를 생성하고, 주기적으로 TCC에 전송한다. TCC는 RSU로부터 도로상황지수를 통합하여 TCC의 관할 구획의 모든 차량에 대해 도로교통정보를 만든다. 마지막으로 단말기는 효율적인 경로안내를 위해 최적의 경로를 도출하여 운전자에게 제공한다. 따라서 단말기, RSU, TCC간의 상호작용을 통해 AINS는 동적인 교통상황(정체, 교통사고 등)을 기반으로 새로운 형태의 적응형 지능 내비게이션을 제공할 수 있다.

• International Journal of Highway Engineering
• /
• v.16 no.4
• /
• pp.127-134
• /
• 2014

PURPOSES : To operate more efficient traffic management system, it is utmost important to detect the change in congestion level on a freeway segment rapidly and reliably. This study aims to develop classification method of congestion change type. METHODS: This research proposes two classification methods to capture the change of the congestion level on freeway segments using the dedicated short range communication (DSRC) data and the vehicle detection system (VDS) data. For developing the classification methods, the decision tree models were employed in which the independent variable is the change in congestion level and the covariates are the DSRC and VDS data collected from the freeway segments in Korea. RESULTS : The comparison results show that the decision tree model with DSRC data are better than the decision tree model with VDS data. Specifically, the decision tree model using DSRC data with better fits show approximately 95% accuracies. CONCLUSIONS : It is expected that the congestion change type classified using the decision tree models could play an important role in future freeway traffic management strategy.