## 1.Introduction

The maritime traffic investigation could be utilized as data for the maritime traffic congestion and flow evaluation, which is to evaluate whether a fairway could accommodate the maritime traffic volume, as well as setting of speed limits and improvement of fairways.

The previous studies on maritime traffic investigation include a study made on a 3-day maritime traffic investigation (Im et al., 2007; Kim et al., 2011; Lee et al., 2012) as well as studies on a 7-day maritime traffic investigation(Kim et al., 2006) and a 10-day maritime traffic investigation(Park et al., 2006). However, all of them have failed to suggest an error in the estimated maritime traffic upon the number of days investigating the maritime traffic. Also, they also did not suggest variation indexes including a month and a week and a hour of the maritime traffic investigation. In Japan, studies on the maritime traffic investigation have received a lot of interest(Kinzo and Kiyoshi, 1974), but there is no study on the maritime traffic investigation of Korea.

Therefore, this study aims to suggest variation indexes of the monthly and weekly maritime traffic, and validate the computation of the number of days of observation.

## 2.Method of Study

### 2.1.Area of Study

This study was made with AIS(Automatic Identification System) data, which was collected for 365 days from January 1^{st} to December 31^{st}, 2013 at Mokpo Port and the analysis object region was Mokpogu where vessels navigating Mokpo Port pass through, as shown in the Fig. 1.

### 2.2.Procedure of Study

Fig. 2 shows the study procedure, which is to compare the maritime traffic by maritime traffic investigation period such as a month, a week and a hour and then, apply it with the variation index and suggest the maximum standard error by observation days.

## 3.Maritime Traffic Analysis

### 3.1.Daily maritime traffic

Fig. 3 shows the daily maritime traffic. Table 1, revealing the statistics data for 1 year, shows that 20,833 vessels navigated in one year and 76.8 vessels navigated in one day on average.

### 3.2.Monthly maritime traffic

Table 2, showing the investigation of vessels navigating Mokpogu by month, reveals that the average daily vessel traffic was the highest, of 84.9 vessels, in September and smallest, of 64.5 vessels, in February. Fig. 4 shows the monthly variation index (The monthly average daily maritime traffic for each month ÷ the annual average daily maritime traffic), which was shown to be 1.11 and 0.84 for September and February, respectively so the maritime traffic in September was about 32.1 % higher than the maritime traffic in February.

### 3.3.Weekly maritime traffic

Table 3 shows the maritime traffic by week. The average weekly maritime traffic by week reveals that it was highest, of 80.7 vessels, in Tuesday and smallest, of 70.8 vessels, in Sunday. Fig. 5 shows the weekly variation index (The average weekly maritime traffic by day ÷ the annual average daily maritime traffic) revealing that the daily variation indexes of Tuesday and Sunday were 1.05 and 0.92, respectively and the maritime traffic in Tuesday is about 14.1 % higher than of Sunday.

### 3.4.Hourly maritime traffic

Table 4 shows the maritime traffic by hour. Fig. 6 shows the hourly variation index (The average daily maritime traffic by hour ÷ the annual average daily maritime traffic) revealing that the maritime traffic from 09 to 10 was 1.88 which is about 382.1 % higher than 0.39 for the maritime traffic from 03 to 04. Also, from 20 to 03, the number of inbound vessels was found to be relatively higher than of outgoing vessels and from 03 to 09, the number of outgoing vessels was relatively larger than of incoming vessels.

## 4.Reliability of Observation Days

### 4.1.Relationship between the Observation Days and Coefficient of Variation

When the coefficient of variation(CV) of the sample mean by the observation days is applied, it could be expressed as the equation (1)(Kinzo and Kiyoshi, 1974).

Since the standard deviation(*σ*) of the maritime traffic in Mokpogu were 15.9 vessels, and the sample mean (*x*) of the maritime traffic in Mokpogu was similar with the population mean (*μ*) of 76.8 vessels, it could be calculated as *σ*/$\mathrm{\sigma}/\overline{x}$
=0.207 and expressed as the analytical equation (2).

On the other hand, from the population, it is possible to calculate the sample mean $\left(\overline{x}\right)$ and standard deviation $\left({\mathrm{\sigma}}_{\overline{x}}\right)$ for sample groups in the number of i={number of days in one year-(n-1)}, which was calculated by moving the number of populations by each observation days. The regression equation for CV for each of 50 sample groups could be computed as in the Fig. 7 and then, expressed as the equation (3).

### 4.2.Estimation of Annual Average Daily maritime traffic

The annual average daily maritime traffic, in general, is estimated with continuous observation for days, as the equation (4).

Where *k* : Reliability Coefficient

µ : Estimated Annual Average Daily maritime traffic

x : Average maritime traffic, estimated with the observation made in the number of days.

For instance, during the 7-day maritime traffic investigation at Mokpogu, when the annual average maritime traffic was calculated at the 95 % confidence level (*k*=1.96), as the equation (5).

Table 5 shows the maximum standard error in the estimation of annual average daily maritime traffic by observation days. For instance, when it is estimated at the 95 % confidence level, the 3-day maritime traffic investigation and 7-day maritime traffic investigation could have 28.2 % and 21.6 % of the maximum standard error, respectively.

### 4.3.Comparison with Previous Studies

Table 6 shows the comparison of the maximum standard errors between the previous study(Kinzo and Kiyoshi, 1974) on the maritime traffic at the Akashi Strait for 1 year and this study, and it reveals that a result of this study is similar with of the aforementioned study. When the observation days was 14, the maximum standard errors were found to be 17.6 and 17.3 for the Akashi Strait and Mokpogu, respectively. Also, when the observation days was 30, the maximum standard errors were found to be 13.1 and 13.6 for the Akashi Strait and Mokpogu, respectively. It could be reasonable to assume that the maximum standard error tends to be similar when the observation days is 14 or above.

## 5.Conclusion

In this study, a difference in the monthly, weekly and hourly maritime traffic was compared after applying it with the variation index, and the maximum standard error, varied by observation days, is as the following.

The comparison of monthly variation indexes showed that the monthly variation indexes for September and February were 1.11 and 0.84, respectively, in turn revealing that the maritime traffic in September was about 32.1 % larger than February. Also, the daily variation indexes for Tuesday and Sunday were 1.05 and 0.92, respectively, in turn revealing that the maritime traffic in Tuesday was about 14.1 % larger than Sunday. Therefore, if the maritime traffic investigation is made in the month and week with low maritime traffic, each variation index should be applied to reflect the actual maritime traffic. Also, it is suggested that the maritime traffic investigation should be made on Tuesday, Wednesday and Thursday in either September or October when the maritime traffic is relatively large. When the maritime traffic investigation is executed for at least 1 week in consideration of the daily variation index, it is possible to reduce the maximum standard error rate to be within 21 %.

Since this study suggest an error rate of the maritime traffic investigation by variation index and observation days, it is possible to detect the maritime traffic flow by month, week, and hour. Thus, it could be utilized as data for the maritime traffic congestion and flow evaluation, which is to evaluate whether a fairway could accommodate the volume of maritime traffic, as well as setting of speed limits and improvement of fairways.