With the removal of underwater obstacles in the Busan New Port, the water depth of the pier has been secured up to 17m, and the port authority is constantly responding to the trend of container vessels becoming larger. Also, in 2020, 24,000TEU class container ships are entering to the port, and it is planned to secure the depth in the port to 23 m later in line with this trend. Mega container ships must check in advance the factors to be considered depending on the situation at the time, and for this, it is judged that information sharing among stakeholders is necessary. In this paper, to understand the effect of the corresponding route because of the mega container ships, a transit safety evaluation was conducted based on statistical data on ship entry and departure and maneuver characteristics of corresponding ships. The result showed that the transit of the mega container ships has increased up to 8.4% comparing to the risk of 4,000TEU class container ships. Additionally, safety measures such as minimum safety depth and tug operation plans were presented by gathering opinions on operational characteristics from the perspective of pilotage for safe transit in the Busan New Port area. Through this, it is considered that it will be possible to contribute to the prevention of accidents when entering and leaving the Busan New Port.
Journal of the Korea Society of Computer and Information
/
v.15
no.9
/
pp.57-65
/
2010
This paper describes the method of efficient container number recognition in colored container image with number plate at ATC(Automatic Transfer Crane) in container terminal yard. At the Sinseondae terminal gate in Busan, the container number recognition system is installed by "intelligent port-logistics system technology development", that is government research and development project. It is the method that it sets up the tunnel structure inside camera on the gate and it recognizes the container number in order to recognize the export container cargo automatically. However, as the automation equipment is introduced to the container terminal and the unmanned of a task is gradually accomplished, the container number recognition system for the confirmation of the object of work is required at ATC in container terminal yard. Therefore, the container number recognition system fitted for it is necessary for ATC in container terminal yard in which there are many intrusive of the character recognition through image including a sunlight, rain, snow, shadow, and etc. unlike the gate. In this paper, hardware components of the camera, illumination, and sensor lamp were altered and software elements of an algorithm were changed. that is, the difference of the brightness of the surrounding environment, and etc. were regulated for recognize a container number. Through this, a shadow problem, and etc. that it is thickly below hung with the sunlight or the cargo equipment were solved and the recognition time was shortened and the recognition rate was raised.
The objective of this study was to analyze the risk of collision accidents to the VTSOs (Vessel Traffic Service Operators) as small ferries and fishing boats are expanded for monitoring targets. The VTSOs was surveyed, the scale of the small vessels defined and the course of general cargo vessels and small vessels along the Busan VTS area investigated for three days. From calculating the risk with CoRI, patterns of increased or decreased risk due to course deviation were similar, and there was no significant difference between the maximum values and the minimum values. In addition, most VTSOs responded that the minimum time required was approximately three minutes to safely instruct in encounter situation, however, the collision risk with a small vessel is very rapidly changing within the three minutes, which is likely to increase the workload and decrease the concentration of the VTSOs. The objective of this study was to investigate the effect on VTSOs with respect to the expansion of small vessels as collision risk, it is expected that it will contribute to the establishment of a suitable scale for the target vessels for VTS through the analysis of each index of the CoRI model and various case studies.
Aids to Navigation(AtoN) Accident can reduce the credibility of AtoN by adding extra works to the administrators, and cause burdens and inconveniences to the users during their navigation leading to social costs (or economic losses). Prior research on social cost estimation model to determine the Encounter cost, Administrative cost and Risk cost on the Aton model was done to estimate the economic losses due to AtoN Accident. However, this research could not provide adequate study results on risk cost estimates. Therefore, this study complements the estimation model presented in the previous study, and the estimated social cost of 249 AtoN Accidents that occurred during the last 10 years (2008-2017) in the Busan Regional Office of Oceans and Fisheries jurisdiction area. The risk cost was estimated using contingent valuation method since the AtoN is a non-market goods. As a result, the social cost of AtoN Accidents for the past 10 years was estimated to total 12.4 billion won including Encounter cost of 3.1 billion won (25.38%), Administrative cost of 1.7 billion won (13.62%), and Risk cost of 7.5 billion won (61.01%).
The purpose of this study is to use the EGARCH model and Granger causality test to analyze how the change in the BDI affects the Korean stock price volatility. The main analysis results are summarized as follows. First, according to the results of the mean equation, the change in the BDI is significant in large-cap stocks, as well as in the manufacturing, service, and chemistry indexes, but not in others. This implies that the Korean stock market does not respond appropriately to the maritime market situation; further, the increase in demand for raw materials has not led to a real economic recovery. Second, in the result of the variance equation, the coefficient on the change in the BDI is negative(-), and the change in the BDI is significant for all size indexes. Particularly, the change in the BDI has a greater impact on the volatility of small-cap stocks than that of large-cap stocks. The results of the analysis of the sector indexes were statistically significant for the service, financial, construction, and electric and electronics industries, but not for the manufacturing and chemical industries. In particular, the changes in the BDI have the greatest impact on the construction industry. Third, according to the Granger causality test results, the change in the BDI leads the financial industry and construction industry. There is, however, no relationship between the BDI and the other indexes. This shows that change in the shipping freight index can be used to predict the volatility in the Korean stock market. This can help investors and policymakers make better decisions.
Bridges across waterways act as interference factors, that reduce the navigable water area from the perspective of navigation safety. To analyze the safety navigational width of ships navigating bridges across waterways, the optimal traffic distribution based on AIS data was investigated, and ships were classified according to size through k-means clustering. As a result of the goodness-of-fit analysis of the clustered data, the lognormal distribution was found to be close to the optimal distribution for Incheon Bridge and Busan Harbor Bridge. Also, the normal distributions for Mokpo Bridge and Machang Bridge were analyzed. Based on the lognormal and normal distribution, the analysis results assumed that the safe passage range of the vessel was 95% of the confidence interval, As a result, regarding the Incheon Bridge, the difference between the normal distribution and the lognormal distribution was the largest, at 64m to 98m. The minimum difference was 10m, which was revealed for Machang Bridge. Accordingly, regarding Incheon Bridge, it was analyzed that it is more appropriate to present a safety width of traffic by assuming a lognormal distribution, rather than suggesting a safety navigation width by assuming a normal distribution. Regarding other bridges, it was analyzed that similar results could be obtained using any of the two distributions, because of the similarity in width between the normal and lognormal distributions. Based on the above results, it is judged that if a safe navigational range is presented, it will contribute to the safe operation of ships as well as the prevention of accidents.
The most change in this century is supposed to be declination of ideology, and block of world economy. Addition to down full of cold war atmosphere around Northeast Asia, not only economic and social mood in this region is dramatically changed, but also it gave birth to the block of Northeast economy that accerlated new hub of world economy. According to dramatic change of economic surroundings the dynamic potential of growth in this region will be guided to enlarge inter-regional trade and increase volume of trade, thus suggests to grow steadily transportation. cargos in this region will have to arrange the system of delivery and inner transportation, accessary facilities, inter-regional harbors if North America and EC has connected easily. As have accerlated GATT and UR represented multilateralism and regionalism, it has regulated to increase trades of region due to relief of the trade barrier through specific areas has agreed with separately. The flow of regionalism of world economy has appeared to realize EC and NAFTA centered U.S.A, and also has presented to free trade region or one-size market agreement in Asia as APEC, EAEG in Malaysia, and etc. In defense to this block and internationalism of world economy, Pusan has to come forward to the hub of Northeast others has proposed a project to dominate the Northeast, Economy Association Agreement as Far East comprehensive development project in USSR, Hunchun development project in NK, and East Sea development project in PRC, Niigate regional development in Japan, Duman River development project in NK, and East Sea development project in Korea. As this exercise has proceed, Pusan also have arranged development strategy definitely and prepared provisions systematically. Engaging to participate center of delivery system is meant to be completed complex functions, namely the transfer storage processing & assembly function of international commodity. Pusan has ability to be terminal point of TSR. it had been connected to EC as the biggest economy block and TKR as complex transportation root to Far East, it would be the center of inground and seabase delivery terminal to Rotterdom as the biggest container pier and major piers to North-East and South East Asia. In order to provide a Role of 21 century's internationalization, Pusan has appealed to participate in management information research and development connected to Pohang-Ulsan-Changwon-Masan, and has utilized efficiently the resources such as man, material, money and information.
In this study, daily data from January 2002 to June 2022 were used to investigate the relationship between risk-return relationship and market fear, uncertainty, stock market, and maritime freight index for the crude oil market. For this study, the time varying EGARCH-M model was applied to the risk-return relationship, and the wavelet consistency model was used to analyze the relationship between market fear, uncertainty, stock market, and maritime freight index. The analysis results of this study are as follows. First, according to the results of the time-varying risk-return relationship, the crude oil market was found to be related to high returns and high risks. Second, the results of correlation and Granger causality test, it was found that there was a weak correlation between the risk-return relationship and VIX, EPU, S&P500, and BDI. In addition, it was found that there was no two-way causal relationship in the risk-return relationship with EPU and S&P500, but VIX and BDI were found to affect the risk-return relationship. Third, looking at the results of wavelet coherence, it was found that the degree of the risk-return relationship and the relationship between VIX, EPU, S&P500, and BDI was time-varying. In particular, it was found that the relationship between each other was high before and after the crisis period (financial crisis, COVID-19). And it was found to be highly associated with organs. In addition, the risk-return relationship was found to have a positive relationship with VIX and EPU, and a negative relationship with S&P500 and BDI. Therefore, market participants should be well aware of economic environmental changes when making decisions.
This study analyzed the risk spillover of BDI on shipping company stock prices through the Copula-CoVaR method based on daily data from January 4, 2010, to October 31, 2022. The main empirical analysis results and policy implications are as follows. First, copula results showed that there was a weak dependence between BDI and shipping company stock prices, and PAN, KOR, and YEN were selected as the most fitting model for dynamic Student-t copula, HMM was selected as the rotated Gumbel copula, and KSS was selected as the best model. Second, in the results of CoVaR, it was confirmed that the upside (downside) CoVaR was significantly different from the upside (downside) VaR in all shipping companies. This means that BDI has a significant risk spillover on shipping companies. In addition, as for the risk spillover, the downside risk is generally lower than the upside risk, so the downside and upside risk spillover were found to be asymmetrical. Therefore, policymakers should strengthen external risk supervision and establish differentiated policies suitable for domestic conditions to prevent systematic risks from BDI shocks. And investors should reflect external risks from BDI fluctuations in their investment decisions and construct optimal investment portfolios to avoid risks. On the other hand, investors propose that the investment portfolio should be adjusted in consideration of the asymmetric characteristics of up and down risks when making investment decisions.
This work aims to : establish a model of the container physical distribution system of Pusan port comprising 4 sub-systems of a navigational system, on-dock cargo handling/transfer/storage system, off-dock CY system and an in-land transport system : examine the system regarding the cargo handling capability of the port and analyse the cost of the physical distribution system. The overall findings are as follows : Firstly in the navigational system, average tonnage of the ships visiting the Busan container terminal was 33,055 GRT in 1990. The distribution of the arrival intervals of the ships' arriving at BCTOC was exponential distribution of $Y=e^{-x/5.52}$ with 95% confidence, whereas that of the ships service time was Erlangian distribution(K=4) with 95% confidence, Ships' arrival and service pattern at the terminal, therefore, was Poisson Input Erlangian Service, and ships' average waiting times was 28.55 hours In this case 8berths were required for the arriving ships to wait less than one hour. Secondly an annual container through put that can be handled by the 9cranes at the terminal was found to be 683,000 TEU in case ships waiting time is one hour and 806,000 TEU in case ships waiting is 2 hours in-port transfer capability was 913,000 TEU when berth occupancy rate(9) was 0.5. This means that there was heavy congestion in the port when considering the fact that a total amount of 1,300,000 TEU was handled in the terminal in 1990. Thirdly when the cost of port congestion was not considered optimum cargo volume to be handled by a ship at a time was 235.7 VAN. When the ships' waiting time was set at 1 hour, optimum annual cargo handling capacity at the terminal was calculated to be 386,070 VAN(609,990 TEU), whereas when the ships' waiting time was set at 2 hours, it was calculated to be 467,738 VAN(739,027 TEU). Fourthly, when the cost of port congestion was considered optimum cargo volume to be handled by a ship at a time was 314.5 VAN. When the ships' waiting time was set at I hour optimum annual cargo handling capacity at the terminal was calculated to be 388.416(613.697 TEU), whereas when the ships' waiting time was set 2 hours, it was calculated to be 462,381 VAN(730,562 TEU).
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