• Journal of Navigation and Port Research
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• v.31 no.6
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• pp.455-462
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• 2007

This paper estimated the future traffic volume incoming and outgoing in Incheon port, and analyzed the value of information serviced by tidal current signal operation center in Incheon. The cargo traffic in 2020 will increase twice as much as in 2005 according to the national ports basis plan. The maritime traffic will increase greatly consequently. Also, MOMAF has operated tidal current signal operation center to prevent marine accidents caused by current influence on vessels navigating through Incheon. However the quantitative effect is not known because there is no analysis about its value. Therefore the value of information serviced by tidal current signal operation center in Incheon was calculated with contingent valuation method(CVM), and the information value was analyzed considering future traffic in this study. Thus, the annual information value was calculated at about $170{\sim}280$ million won, considered traffic volume using the information of tidal current directly in 2020 since 2006.

• Journal of Navigation and Port Research
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• v.30 no.8 s.114
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• pp.617-622
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• 2006

Gamcheon Harbor was developed to cope with increased freight demand of Busan port and supplement function of the north port. Because container wharf is opened to 1997 as well as general wharf, present maximum 50,000DWT class containerships have been incoming and outgoing. However, In Gamcheon port, small size ships such as fishing boats, miscellaneous boats account for 50 percent of the traffic and a public marine products wholesale market that is building on the north wharf will open in 2008. Therefore, it needs to grasp future year traffic volume before establishing operation plan for port management. Also, analysis on crossing situation risk is required because the breakwater entrance in Gamcheon Harbor is narrow and the crossed passing of ship is ever-present at breakwater front. Thus the traffic volume in the future was presumed and quantitative analysis was achieved on crossing situation though simulations with the traffic volume.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.147-157
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• 2022

Recently, many studies have been conducted to analyze traffic or object recognition that classifies vehicles through artificial intelligence-based prediction models using CCTV (Closed Circuit TeleVision)or drone images. In order to develop an object recognition deep learning model for accurate traffic estimation, systematic data construction is required, and related standardized guidelines are insufficient. In this study, previous studies were analyzed to derive guidelines for establishing artificial intelligence-based training data for traffic estimation using drone images, and business reports or training data for artificial intelligence and quality management guidelines were referenced. The guidelines for data construction are divided into data acquisition, preprocessing, and validation, and guidelines for notice and evaluation index for each item are presented. The guidelines for data construction aims to provide assistance in the development of a robust and generalized artificial intelligence model in analyzing the estimation of road traffic based on drone image artificial intelligence.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.1
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• pp.1-14
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• 2013

Annual average daily traffic (AADT) serves the important basic data in transportation sector. Despite of its importance, AADT is estimated through permanent traffic counts (PTC) at limited locations because of constraints in budget and so on. At most of locations, AADT is estimated using short-term traffic counts (STC). Though many studies have been carried out at home and abroad in an effort to enhance the accuracy of AADT estimate, the method to simplify average STC data has been adopted because of application difficulty. A typical model for estimating AADT is an adjustment factor application model which applies the monthly or weekly adjustment factors at PTC points (or group) with similar traffic pattern. But this model has the limit in determining the PTC points (or group) with similar traffic pattern with STC. Because STC represents usually 24-hour or 48-hour data, it's difficult to forecast a 365-day traffic variation. In order to improve the accuracy of traffic volume prediction, this study used the geostatistical approach called co-kriging and according to their reports. To compare results, using 3 methods : using adjustment factor in same section(method 1), using grouping method to apply adjustment factor(method 2), cokriging model using previous year's traffic data which is in a high spatial correlation with traffic volume data as a secondary variable. This study deals with estimating AADT considering time and space so AADT estimation is more reliable comparing other research.

• Journal of Environmental Science International
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• v.18 no.7
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• pp.747-754
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• 2009

In this study, which aims to estimate the volume of greenhouse gas emitted by road transportation vehicles in Changwon City, the emission rate was calculated on the basis of the actual traffic volume measured at major crossroads and compared with the results obtained from the methodology used to estimate the greenhouse gas emissions of road transportation provided in the IPCC 2006 GL guidelines (Tier 1, Tier 3). Analysis of the results of the comparison showed that the Tier 1 methodology, which was applied in the estimation of the rate of greenhouse gas emissions, carries a high probability of underestimation, while the Tier 3 methodology carries a relatively high probability of overestimation. Therefore, when considering the assignment of permissible rates of emission to local governments, the application of the methodology, i.e. whether one uses Tier 1 or Tier 3, may result in a large difference in the rate of allowable emissions. It is suggested that a method based on the actual volume of traffic would be the most reasonable one with regard to the development of a realistic plan.

• Journal of Navigation and Port Research
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• v.45 no.6
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• pp.306-313
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• 2021

National fishing harbour is as a refuge for fishing boats and a breakwater lighthouse is installed as a functional facility in consideration of harbour identification and the safety of passing vessels. In this study, the service level of breakwater lighthouse (234 units) was estimated based on the traffic volume of 105 national fishing harbour. For this purpose, the evaluation items were determined, the fishing harbour standard index was calculated (Fs=1), the proximity of fishing harbour was identified and the function (service level) of the breakwater lighthouse was estimated in the following order. However, national fishing harbour differed in size, traffic volume and fishing vessel capacity. Therefore, 105 national fishing harbour were divided into three groups through cluster analysis. The cluster analysis was based on the service level factors of the breakwater lighthouse, such as the number of weeding fishing vessels, tonnage of fishing vessels, the number of incoming and outgoing vessels per year, and fishing vessel capacity. As a result of the estimation, the service level of the breakwater lighthouse (light tower height, visual height, visual range, interval) was 10.50m, 16.50m, 7.00mile, 5.5sec for group 1, and 10.67m, 16.16m, 8.33mile, and 6.0sec for group 2, The three groups are 11.53m, 16.75m, 6.75mile and 5.0sec. The results of this study can be used as useful basic data for improving the service level of traffic vessels when a breakwater lighthouse is built in a fishing harbour in the future.

• The Journal of the Korea Contents Association
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• v.15 no.11
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• pp.582-590
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• 2015

Road type classification and K-factors are important role when design of number of lane. In this study not only classifies road type and estimating of K-factor but also economic evaluation tries for feasibility verification. Road type analysis results, time of day traffic volume variation, weekend-factor and vacation-factor are large in recreation roads. Weekday traffic volume and weekend traffic volume are similar patterns in provincial roads. AADT is high and time of day traffic volume variation is small in urban roads. In this study compares with economic analysis that designing of number of lane between KHCM's K-factor and this study K-factor. Economic analysis results, designed roads by this study's K-factor reduce cost about 4,708 hundred million won. So this study's K-factor is economical on provincial 4 lane roads.