• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.4
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• pp.997-1006
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• 2015

This study analyzed urban infrastructure risk to flooding based on the possibility map of flooding calculated by neural network model focusing on Seoul. This study found that Gangnam-gu, Songpa-gu, Seocho-gu and Seodaemun-gu contained relatively large high-risk areas to flooding. Over $4.17km^2$ of transportation facilities were located in high-risk area to flooding and Gangnam-gu included over $0.85km^2$ of infrastructures exposed to high inundation risk. This study is meaningful in that it first applied the neural network modeling to flooding risk assesment and results of risk assessment can be incorporated into various planning process.

• 한국ITS학회:학술대회논문집
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• 2008.11a
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• pp.244-253
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• 2008

• Journal of Korean Society of Transportation
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• v.31 no.4
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• pp.3-17
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• 2013

The purpose of this study is to develop a traffic assignment model where the variable of signal intersection delay is taken into account in assigning traffic in large-scale network settings. Indeed, despite the fact that the majority of the increase in travel time or cost involving congested urban network or interrupted flow are accounted for by stop delays or congested delays at signal intersections, the existing traffic assignment models did not reflect this. The traffic assignment model considering intersection delays presented in this study was built based on the existing traffic assignment models, which were added to by the analysis technique for the computation of intersection delay provided in Korea Highway Capacity Manual. We can conclude that a multiple variety of simulation tests prove that this model can be applied to real network settings. Accordingly, this model shows the possibility of utilizing a model considering intersection delay for traffic policy decisions through analysis of effects of changes in traffic facilities on large urban areas.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.93-103
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• 2021

The special means of transportation introduced to improve the mobility of the transportation vulnerable met the number of legal standards in 2016, but lack of development in terms of quality, such as the existence of long waiting times. In order to streamline the operation of special means of transportation, long-term standby traffic, which is the top 25% of the wait time, was extracted from the Daegu Metropolitan Government's special transportation history data, and spatial autocorrelation analysis and social network analysis were conducted. As a result of the analysis, the correlation between the average waiting time of special transportation users and the space was high. As a result of the analysis of internal degree centrality, the peak time zone is mainly visited by general hospitals, while the off-peak time zone shows high long-term waiting demand for visits by lawmakers. The analysis of external degree centrality showed that residential-based traffic demand was high in both peak and off-peak hours. The results of this study are considered to contribute to the improvement of the quality of the operation of special transportation means, and the academic implications and limitations of the study are also presented.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.5
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• pp.780-790
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• 2022

Quantitative risk levels must be presented by analyzing the causes and consequences of accidents and predicting the occurrence patterns of the accidents. For the analysis of marine accidents related to vessel traffic, research on the traffic such as collision risk analysis and navigational path finding has been mainly conducted. The analysis of the occurrence pattern of marine accidents has been presented according to the traditional statistical analysis. This study intends to present a marine accident prediction model using the statistics on marine accidents related to vessel traffic. Statistical data from 1998 to 2021, which can be accumulated by month and hourly data among the Korean domestic marine accidents, were converted into structured time series data. The predictive model was built using a long short-term memory network, which is a representative artificial intelligence model. As a result of verifying the performance of the proposed model through the validation data, the RMSEs were noted to be 52.5471 and 126.5893 in the initial neural network model, and as a result of the updated model with observed datasets, the RMSEs were improved to 31.3680 and 36.3967, respectively. Based on the proposed model, the occurrence pattern of marine accidents could be predicted by learning the features of various marine accidents. In further research, a quantitative presentation of the risk of marine accidents and the development of region-based hazard maps are required.

• Journal of the Economic Geographical Society of Korea
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• v.9 no.3
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• pp.379-395
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• 2006

The purpose of this study is to analyze the spatial characteristics of travel patterns and travel behaviors of transit users in the Metropolitan Seoul area. We apply the data mining techniques to explore the travel patterns of transit users from the T-money card database which has been produced over 10,000,000 transaction records per day. The database contains the information of locations and times of origin, transfer, and destination points for each transaction as well as the informations of transit modes taken via the transaction. We develop an data mining algorithm to explore traversal patterns from the enormous information. The algorithm determines the travel sequences of each passenger, and produce the volumes of support on each points (stops) of transportation networks in the Metropolitan Seoul area. In order to visualize the spatial patterns of travel demands for transit systems we apply GIS techniques, and attempt to investigate the spatial characteristics of travel patterns and travel demand. Subway stops located in the Gangnam area appear the highest peak for the travel origin and destination, while the CBD in the Gangbuk stands at the second position. Two or three sub-peaks appear at the densely populated residential areas developed as the high-rise apartment complex. Subway stations located along the Subway Line 2, especially from Guro to Samsung receive heavy travel demand (total support), while bus stops located at the CBD in the Gangbuk stands the highest travel demand by bus.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.87-96
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• 2022

The traffic vulnerable, including elderly pedestrians, have a relatively low walking speed and slow cognitive response time due to reduced physical ability. Although a smart crossing system has been developed and operated to improve problem, it is difficult to operate a signal that reflects the appropriate walking speed for each pedestrian. In this study, a neural network model and a multiple regression model-based traversing speed estimation model were developed using image information collected in an area with a high percentage of traffic vulnerability. to support the provision of optimal walking signals according to real-time traffic weakness. actual traffic data collected from the urban traffic network of Paju-si, Gyeonggi-do were used. The performance of the model was evaluated through seven selected indicators, including correlation coefficient and mean absolute error. The multiple linear regression model had a correlation coefficient of 0.652 and 0.182; the neural network model had a correlation coefficient of 0.823 and 0.105. The neural network model showed higher predictive power.

• Journal of Korea Port Economic Association
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• v.32 no.4
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• pp.29-39
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• 2016

As geopolitical, archaeological and strategic interests on cooperation with countries in the Far Eastern Area is gradually increased, countries are competing to attract or install a logistics or manufacturing hub in their countries. In this study, we investigated the relative importance of factors on the main three and nine detailed criteria from the domestic and overseas experts on Far Eastern Area. Using AHP(Analytic Hierarchy Process) analysis, priority importance of factors was derived. As a result, we find that the most important factor was economic factor. In detail, industrial complex creation was the highest factor and the institutional guarantees for the investment on policy and transportation network was second highest factor. Based on analysis result, specific competitiveness level in the 10 region of Far East was follows. Hunchun, Vladivostok, Yanji, Tumen, Rajin, Hassan, Ussuriysk, Cheongjin, Mihaylov Skiing, Nije Jeuchinski were showed in order. Hunchun showed the highest competitive level in location, topography, compliance to the around cities, transportation network, industrial complex, excellence in logistics facilities, long-term investment plans, institutional guarantees for investment, customs efficiency and political stability. However, in other factors such as population and number of households, public facilities, potential demand and resource utilization, Vladivostok showed the highest level.

• Journal of Korean Society of Transportation
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• v.31 no.1
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• pp.15-24
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• 2013

To accommodate urban concentration of population, multi-purpose super-tall buildings have been introduced, but they induce many travel demands causing regional traffic problems. While several travel demand management policies such as transit promotion or parking limits are presented to alleviate such problems, transportation infrastructure are still insufficient to meet high demands. In this study, super-tall buildings are categorized by scale and purpose, then mode-specific derived demand is estimated using modal share of each category. Optimal transportation infrastructure level is determined by condition-based average changing amount yielded by street network delay (in case of road) or the number of transit routes (in case of transit).

• Journal of the Korean Geographical Society
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• v.33 no.1
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• pp.93-107
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• 1998

The purpose of this paper is to examine the change in the regional characteristics of Y${\={o}}$ju as its economic base has been shifted. From Chosun Dynasty to the mid 1960s, Y${\={o}}$ju had been known as a core of rice production, utilizing favorable natural conditions and well developed river transportation system, with commercial and administrative functions. From the mid 1960s to the mid 1980s, Y${\={o}}$ju had been excluded from the process of the national industrilization, which made Y${\={o}}$ju remain lagged. The transportation system was blocked and the industrial investment in this area was prevented by a variety of restrictive laws. Since the mid 1980s, Y${\={o}}$ju entered into a prosperous are as the land transportation system began to be dramatically improved and some of the restrictions were alleviated. Tecently, diversification and commercialization in the agricultural sector have progressed in land use. In the manufacturing sector, Y${\={o}}$ju becomes a core of the pottery industry in tems of the total amount of its production.