• 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.

• Journal of Korean Society of Transportation
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• v.18 no.3
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• pp.55-76
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• 2000

We propose a general solution methodology for identifying the optimal aggregation interval sizes as a function of the traffic dynamics and frequency of observations for four cases : i) link travel time estimation, ii) corridor/route travel time estimation, iii) link travel time forecasting. and iv) corridor/route travel time forecasting. We first develop statistical models which define Mean Square Error (MSE) for four different cases and interpret the models from a traffic flow perspective. The emphasis is on i) the tradeoff between the Precision and bias, 2) the difference between estimation and forecasting, and 3) the implication of the correlation between links on the corridor/route travel time estimation and forecasting, We then demonstrate the Proposed models to the real-world travel time data from Houston, Texas which were collected as Part of the Automatic Vehicle Identification (AVI) system of the Houston Transtar system. The best aggregation interval sizes for the link travel time estimation and forecasting were different and the function of the traffic dynamics. For the best aggregation interval sizes for the corridor/route travel time estimation and forecasting, the covariance between links had an important effect.

• Journal of the Korean Data and Information Science Society
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• v.8 no.2
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• pp.271-276
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• 1997

A dynamic discount approach is proposed for the estimation of the Poisson parameter and the forecasting of the Poisson random variable, where the parameter of the Poisson distribution varies over time intervals. The recursive estimation procedure of the Poisson parameter is provided. Also the forecasted distribution of the Poisson random variable in the next time interval based on the information gathered until the current time interval is provided.

• Journal of the Korean Statistical Society
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• v.31 no.2
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• pp.143-152
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• 2002

In an effort to make a right choice among ratio estimation strategies and PPS sampling strategies, we conduct an empirical investigation of the relative performances of three ratio estimation strategies and four PPS estimation strategies using a set of 12 natural populations. The quality of a strategy is measured in the traditional way, namely with the consideration of efficiency, achieved coverage rate of the nominal 99% confidence interval and approach to normality (asymmetry).

• Journal of the Korean Data and Information Science Society
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• v.7 no.2
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• pp.227-234
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• 1996

The discount survival model is proposed for the application of the Cox model on the analysis of survival data with time-varying effects of covariates. Algorithms for the recursive estimation of the parameter vector and the retrospective estimation of the survival function are suggested. Also the algorithm of forecasting of the survival function of individuals of specific covariates in the next time interval based on the information gathered until the end of a certain time interval is suggested.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.8
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• pp.708-715
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• 2014

In this paper, we suggest a method of realtime confidence interval estimation to detect abnormal states of sensor data. For realtime confidence interval estimation, the mean square errors of the exponential smoothing method and moving average method, two of the time series analysis method, where compared, and the moving average method with less errors was applied. When the sensor data passes the bounds of the confidence interval estimation, the administrator is notified through alarming. As the suggested method is for realtime anomaly detection in a ship, an Android terminal was adopted for better communication between the wireless sensor network and users. For safe navigation, an administrator can make decisions promptly and accurately upon emergency situation in a ship by referring to the anomaly detection information through realtime confidence interval estimation.

• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.927-934
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• 2010

In this paper, we introduce a method of parameter estimation of progressive Type I interval censored sample and progressive type II censored sample. We propose a new parameter estimation method, that is converting the data which obtained by progressive type I interval censored, those data be used to estimate of the parameter in progressive type II censored sample. We used exponential distribution with unknown scale parameter, the maximum likelihood estimator of the parameter calculates from the two methods. A simulation is conducted to compare two kinds of methods, it is found that the proposed method obtains a better estimate than progressive Type I interval censoring method in terms of mean square error.

• 전자공학회논문지 IE
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• v.47 no.3
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• pp.35-40
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• 2010

In this paper, we propose an algorithm to improve the precision of initial carrier-frequency offset estimation for multiband-OFDM (MB-OFDM) UWB system which is considering the quantization-noise effect. In the general OFDM system, the two adjacent and repeated preamble symbols are used for the initial carrier-frequency synchronization while the performance of the frequency-offset estimation is bounded by quantization effect generated from analog-to-digital conversion at the receiver. This paper proposes a method in which one-symbol interval between two adjacent preamble symbols for the initial frequency synchronization is extended to multiple-symbol interval between non-adjacent symbols in an extent that phase ambiguity does not occur. In this paper, we also present '6' as optimal multiple symbol interval for the MB-OFDM system with 30 preamble symbols on 3-band hopping and with 4-bit A/D conversion at the receiver. Under the channel environments for the MB-OFDM UWB system, the simulation results show that the proposed estimation algorithm can achieve the initial estimation in offset precision less than 5 ppm.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.2 s.5
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• pp.129-140
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• 2004

In this paper, we conduct the research about optimal aggregation interval of travel time data on interrupted traffic flow and verify the reliability of AVI collected data by using car plate matching method in RTMS for systematic collection and analysis of link travel time data on interrupted traffic flow rural arterial. We perform Kolmosorov-Smirnov test on AVT collected sample data and on entire population data, and conclude that the sample data does not represent pure random sampling and hence includes sample collection error. We suggest that additional review is necessary to investigate the effectiveness of AVI collected sample data as link representative data. We also develop statistical model by applying two estimation techniques namely point estimation and interval estimation for calculating optimal aggregation interval. We have implemented our model and determine that point estimate is preferable over interval estimate for exactly selecting and deciding optimal aggregation interval. Our final conclusion is that 5-minute aggregation interval is optimal to estimate travel time in RTMS, as is currently being used our investigation is based on AVI data collected from Yang-ji to Yong-in $42^{nd}$ National road.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1289-1300
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• 2009

The double sampling scheme is effective in reducing the sampling cost. However, the doubly sampled data is contaminated by two types of error, namely false-positive and false-negative errors. These would make the statistical analysis more difficult, and it would require more sophisticate analysis tools. For instance, the Wald method for the interval estimation of a proportion would not work well. In fact, it is well known that the Wald confidence interval behaves very poorly in many sampling schemes. In this note, the property of the Wald interval is investigated in terms of the coverage probability and the expected width. An alternative confidence interval based on the Agresti-Coull's approach is recommended.